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版本更新 · v2arXiv:2603.03280

How to Peel with a Knife: Aligning Fine-Grained Manipulation with Human Preference

Toru Lin, Shuying Deng, Zhao-Heng Yin, Pieter Abbeel, Jitendra Malik

原文摘要

Many essential manipulation tasks - such as food preparation, surgery, and craftsmanship - remain difficult for autonomous robots. These tasks are characterized not only by contact-rich, force-sensitive dynamics, but also by their "implicit" success criteria: unlike pick-and-place, task quality in these domains is continuous and subjective (e.g. how well a potato is peeled), making quantitative evaluation and reward design difficult. We present a learning framework for such tasks, using peeling with a knife as a representative example. Our approach follows a two-stage pipeline: first, we learn a robust initial policy via force-aware data collection and imitation learning, enabling generalization across object variations; second, we refine the policy through preference-based finetuning using a learned reward model that combines quantitative task metrics with qualitative human feedback, aligning policy behavior with human-evaluated task quality. Using only 50~200 peeling trajectories, our system achieves 100% same-produce success on cucumbers, apples, and potatoes, with performance improving by up to 40% through preference-based finetuning. Remarkably, policies trained on one produce category also transfer zero-shot to selected held-out produce categories, achieving 50% success on zucchini, 90% on pear, and 80% on daikon radish.

cs.RO · cs.AI · cs.CV · cs.LG · eess.SY

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arXiv ↗PDF ↗
版本更新 · v6arXiv:2605.19398

Rebalancing Reference Frame Dominance to Improve Motion in Image-to-Video Models

Wooseok Jeon, Seungho Park, Seunghyun Shin, Sangeyl Lee, Hyeonho Jeong, Hae-Gon Jeon

原文摘要

Image-to-video models often generate videos that remain overly static, compared to text-to-video models. While prior approaches mitigate this issue by weakening or modifying the image-conditioning signal, they often require additional training or sacrifice fidelity to the reference image. In this work, we identify reference-frame dominance as a key mechanism behind motion suppression. We observe that non-reference frames in I2V models allocate excessive self-attention to reference-frame key tokens, causing reference information to be over-propagated across time and suppressing inter-frame dynamics. Based on this finding, we propose DyMoS (Dynamic Motion Slider), a training-free and model-agnostic method that rebalances the attention pathway from generated frames to the reference frame during initial denoising steps. DyMoS leaves both the input image and model weights unchanged and introduces a single scalar parameter for continuous control over motion strength. Experiments across multiple state-of-the-art I2V backbones demonstrate that DyMoS consistently improves motion dynamics while maintaining visual quality and fidelity to the reference image.

cs.CV · cs.AI

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2602.02220

LangMap: A Human-Verified Benchmark for Hierarchical Open-Vocabulary Goal Navigation

Bo Miao, Weijia Liu, Jun Luo, Lachlan Shinnick, Jian Liu, Thomas Hamilton-Smith, Yuhe Yang, Zijie Wu 等 11 位作者

原文摘要

Language-conditioned goal navigation requires embodied agents to locate user-specified targets without step-by-step guidance. However, existing benchmarks largely focus on category-level goals or rely on instance descriptions generated by vision-language models, which often contain ambiguities and semantic errors, limiting systematic and reliable evaluation. We introduce HieraNav, an open-vocabulary navigation task with goals at four hierarchical semantic levels: scene, room, region, and instance. We present Language as a Map (LangMap), the first goal navigation benchmark to enrich real-world indoor 3D scans with human-verified semantic annotations supporting tasks across all four goal levels. Built on HM3D using a contrastive annotation protocol that compares same-scene regions and instances, LangMap provides region labels and discriminative region and instance descriptions covering 414 object categories and contains over 18K tasks. Each target has concise and detailed descriptions, enabling evaluation across instruction styles. Automated and human evaluations validate our annotation quality: our descriptions improve text-to-view matching accuracy over GOAT-Bench's by 23 points on all shared annotated instances, and an independent human audit yields 92.5% unique-and-correct matches. We also propose PlaNaVid, an RGB-only baseline that combines Bounded Diverse Memory with high-level planning to prime a reactive policy for multi-goal navigation, achieving top-tier success rates without depth, 3D scene representations, or object masks. Further analyses reveal that exploration and hierarchical disambiguation failures become more prominent at finer goal levels, while long-tail categories, small objects, distant targets, timely stopping, and multi-goal completion remain challenging. Benchmark and code: https://bo-miao.github.io/LangMap

cs.CV · cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02603

BGD-SF PolySegNet: boundary-guided dynamic selective fusion network for robust polyp segmentation in colonoscopy images

Nishi Kanta Paul, Shamia Maherin, Morsheda Akter, Moinul Hossain, Md Kishor Morol

原文摘要

Effective polyp segmentation in colonoscopy images is crucial for early detection of colorectal cancer, a leading cause of cancer-related death worldwide. While current deep learning models perform well on traditional Dice benchmarks, border segmentation remains difficult. Most methods use boundary information only as an additional loss signal during training, which prevents the network from explicitly learning border-guided feature selection or mask refinement. This paper introduces BGD-SF PolySegNet, a boundary-guided encoder-decoder architecture that controls three phases of the segmentation pipeline: dynamic multi-scale context aggregation, selective skip-feature fusion, and final mask refinement. The architecture comprises a pretrained EfficientNet-B4 backbone and three proposed modules: Boundary-Guided Dynamic Cascaded Multi-Scale Context Aggregation (BGD-CMSCA), Boundary-Guided Selective Attention Fusion (BG-SAGF), and a Multi-Level Boundary Guidance Head (MBGH). The model is trained on 3,535 annotated images from Kvasir-SEG, BKAI-IGH NeoPolyp-Small, and PolypGen, and evaluated on four held-out benchmark datasets. On the internal test set, BGD-SF achieves a Dice of 0.887, IoU of 0.839, mask-derived Boundary F1 of 0.531, HD95 of 30.32, and ASSD of 18.10, improving Boundary F1, HD95, and ASSD by 15.7%, 27.5%, and 34.6% over the strongest CNN baselines. External validation on CVC-ClinicDB, CVC-ColonDB, ETIS-Larib, and CVC-300 yielded Dice scores of 0.891, 0.776, 0.863, and 0.885, respectively. The model runs at 35 FPS with 22.51M parameters. Wilcoxon signed-rank tests confirm statistically significant mask-derived Boundary F1 improvements over six of seven ablation variants (p < 0.001; small-to-moderate effect sizes, r = 0.215-0.434), demonstrating that integrating boundary guidance as an active architectural component measurably advances polyp boundary delineation.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02599

Camera-Free Object Discovery and Interaction with GazeTags

Vimal Mollyn, Chris Harrison

原文摘要

We present GazeTags, a camera- and computer-vision-free approach for worn head-gaze object awareness. Our small electronic tags cost roughly \$2 each and can operate for more than a year on a coin cell battery. They work in concert with a user-worn device (smartglasses, hearables, etc.) that periodically emits a focused infrared signal aligned with the user's head direction. Only GazeTags within that narrow head-oriented beam wake and respond, providing their identity and optional payloads such as object state or live sensor readings. By avoiding cameras and computer vision pipelines, our approach is very energy efficient, able to operate continuously even in battery-constrained wearables, and sidesteps non-trivial privacy issues. We describe our hardware design, characterize our system performance and operating tradeoffs, and demonstrate how GazeTags can enable practical gaze-aware environments.

cs.HC · cs.CV · cs.GR · cs.MM

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02593

AVID: A 5T fMRI Dataset for Benchmarking Auditory-induced Visual Mental Imagery Decoding

Shiqi Shen, Shurui Li, Xilin Zhang, Yuanning Li

原文摘要

Visual mental imagery (VMI) decoding uses brain activity to reconstruct internally generated visual scenes, providing a potential pathway for externalizing internal experiences and developing neuroprosthetic communication interfaces. However, progress is limited by the lack of dedicated, training-scale VMI datasets. Current VMI paradigms often rely on visual prompts or short-term memory images, introducing visual and mnemonic confounds that make it difficult to disentangle neural activity associated with endogenous image construction from signals driven by prior or concurrent visual input. Furthermore, existing VMI datasets offer limited descriptive control over individual visual attributes, making it difficult to systematically assess fine grained visual details. To address these gaps, we introduce AVID, the first dedicated training-scale 5.0 Tesla functional magnetic resonance imaging (fMRI) resource for auditory-induced VMI decoding. AVID comprises approximately 67 hours of dense recordings from nine participants. The dataset is structured into two complementary splits: a scene-level complex split using naturalistic Mandarin descriptions and a controlled simple split with explicitly specified visual attributes. We further formalize a standardized evaluation protocol that decouples source-image consistency from semantic alignment, allowing for a more nuanced assessment of imagery reconstruction. Baseline qualitative and quantitative results show that current reconstruction pipelines remain limited on AVID, although the most successful reconstructions preserve recognizable scene level semantics from spoken descriptions. By combining dedicated training-scale neural recordings and standardized evaluation resources, AVID provides both a data foundation for imagery-specific model development and a benchmark for evaluating VMI decoding with substantially reduced sensory contamination.

cs.CV · cs.AI · q-bio.NC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02581

SMILE: A Structured Multi-Agent Clinical Reasoning for Liver Ultrasound Understanding

Haris Ghafoor, Minjee Seo, Seonaeng cho, Keungmo Yang, Jaejun Lee, Kyungho Yoon

原文摘要

Automated liver ultrasound report generation improves the diagnosis of liver diseases by producing clinically structured descriptions of anatomical and disease-related findings. However, current generative vision-language models (VLMs) often rely on a single reasoning pathway that maps ultrasound appearance directly to textual descriptions. In addition to the inherent vision-language modality gap, this design is limited for liver ultrasound because disease interpretation is constrained by subtle visual evidence, heterogeneous disease patterns, and limited paired supervision. To address these limitations, we propose SMILE, a Structured Multi-agent clInicaL rEasoning framework that uses multiple complementary specialist agents to interpret ultrasound images and consolidates their outputs through a judge-guided consensus mechanism. Our approach employs three specialized agents: an image interpretation agent for morphological descriptions, a clinical-stage reasoning agent, and a visual question answering (VQA) agent for targeted queries. These agent responses are then aggregated and scrutinized by a judge VLM using a consensus filtering prompt to produce a unified, clinically coherent output. Across a public liver ultrasound dataset and an internal clinical dataset, SMILE consistently outperforms state-of-the-art VLMs, achieving up to 19.0\% improvement in ROUGE-1 and 18.8\% improvement in clinical F1-score, while reaching VQA F1-scores of 0.82 and 0.65 on public and internal datasets, respectively. These results demonstrate the potential of SMILE for data-scarce and limited-supervision clinical settings. Our models, datasets, and code are available \href{https://github.com/CMME-Lab/SMILE}{here}.

cs.CV · cs.CL · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02578

Probing Local Conditional Fit for Diffusion Model Membership Inference

ByungHee Cha, Naoki Murata, Yuhta Takida, Jong Chul Ye, Yuki Mitsufuji

原文摘要

Membership inference determines whether a candidate example was used to train a text-to-image diffusion model and is an important tool for auditing training-data use. We identify a directional membership signal in \emph{conditional prediction shifts}, the changes in predicted noise under alternative text conditions at a fixed noisy input: whether movement toward the alternative prediction initially lowers or raises the denoising loss from its value under the original caption. Members exhibit less room for such local improvement. To quantify this response, we propose \emph{LoFIT} (Local Conditional Fit), which measures the initial response of the denoising loss along normalized prediction shifts and aggregates it across multiple text conditions and diffusion states. LoFIT requires no target-model gradients and can be computed using only forward queries to the denoiser. Evaluations on four datasets show improved membership discrimination across fine-tuned and pretrained models. LoFIT distinguishes members early in fine-tuning, while the naive denoising-loss baseline remains near chance. Controlled comparisons show that the membership signal is concentrated in the first-order response, with little discrimination from prediction-shift magnitude alone.

cs.AI · cs.CV · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02568

SeMo3D: Unifying 3D Generation, Editing, and Completion via Selective Modeling

Xiaokun Sun, Jian Yang, Zhenyu Zhang

原文摘要

3D generation, editing, and completion are typically treated as regenerating the complete 3D asset under task-specific constraints, even when only a local region needs modification. This leads to task isolation, inconsistency in content that should remain unchanged, and dependence on task-specific paired data. We address these issues with a key observation: the three tasks all synthesize a designated region while keeping the rest intact---the full region for generation, the target region for editing, and the missing region for completion. We term this paradigm Selective Modeling and build SeMo3D upon it; it naturally unifies the three tasks, preserves unchanged content by construction, and enables training without paired editing or completion data, since any 3D asset can be partitioned into a selected region and its context. For training, we propose region-selective denoising, which randomly selects regions of varying sizes on standard 3D assets and applies noise and loss only to them, jointly learning all three tasks. For architecture, we propose Omni3DiT, a multi-condition 3D diffusion transformer that distinguishes regions to create from those to retain and fuses image, text, and point guidance for fine-grained control. Experiments across three benchmarks show SeMo3D surpasses state-of-the-art methods on all tasks, delivering multimodal controllability in generation and strong consistency and accuracy in editing and completion. SeMo3D further serves as a unified modeling tool for agents, enabling efficient 3D creation through natural-language interaction. Project page: https://xiaokunsun.github.io/SeMo3D.github.io/.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02551

Flat Features, Detailed Images? Simple Detail Control for Diffusion Transformers

Qingtao Yu, Zhen Wang, Jiyang Zheng, Minghao Sun, Hongdong Li, Dylan Campbell

原文摘要

Prompt engineering is commonly used to encourage text-to-image models to generate more detailed images. However, it does not provide reliable, continuous, or disentangled control. We analyze feature tokens produced with and without detail-enhancing prompts across six diffusion transformers and observe a common pattern: these prompts tend to increase the entropy of each token's energy distribution while largely preserving its ordering. That is, flattening the distribution increases the level of detail. Motivated by this counter-intuitive finding, we introduce a simple training-free approach that continuously adjusts the level of detail according to a scalar. Specifically, it shifts the squared channel magnitudes of each token toward a higher entropy distribution with a time-aware scheduler to concentrate the intervention at intermediate sampling steps. Across extensive experiments on different types of diffusion transformers, we show that our approach provides continuous control over detail metrics while largely preserving image quality and semantic structure. It requires only a few lines of code and adds negligible inference time overhead.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.11302

iCATS: Fast Video Generation via Interaction-Aware Sparse Attention and Timestep-Adaptive Sparsity

Chengfeng Han, Ang Wang, Baole Ai, Xianlu Bian, Jie Yao, Zilong Huang, Dandan Ding

原文摘要

Training-free sparse attention offers a practical acceleration solution to Diffusion Transformers (DiTs) via reducing computations without fine-tuning. It typically involves estimating the importance of query-key regions and deriving sparse masks to compute only the important candidates, which inevitably introduces approximation errors that may degrade generation quality. To better balance the efficiency-quality trade-off, we propose iCATS, integrating improved importance estimation and sparse mask construction with an efficient hardware execution strategy. Specifically, for importance estimation, unlike previous works that perform independent clustering over query and key tokens based on feature similarity to estimate attention scores, iCATS demonstrates that clustering based on query-key dot-product interactions is more accurate and further reformulates this objective as a simple quadratic form for low-cost computation. For sparse mask construction, instead of using a fixed top-p rule, we observe that tolerance to sparse approximation errors varies across denoising timesteps and therefore introduce an SNR-guided sparsity schedule to adjust sparsity dynamically, leading to higher accuracy. Finally, for hardware execution, we devise a tail-merging strategy to reduce padding overhead caused by irregular cluster sizes, improving GPU kernel utilization. Extensive experiments show that iCATS achieves $2.03\times$ acceleration with 31.017 dB PSNR on HunyuanVideo-T2V-13B and $1.55\times$ acceleration with 29.301 dB PSNR on Wan2.1-T2V-14B, delivering a state-of-the-art efficiency-quality trade-off.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02526

INTERACT: Learning Interaction-Aware World Dynamics from Driving Videos for Autonomous Driving

Zhan Chen, Songyan Zhang, Wenhui Huang, Zirui Li, Zhidong Wang, Qihang Huang, Shiyu Fang, Chen Lv

原文摘要

Safe autonomous driving requires more than recognizing the current scene: the ego vehicle must anticipate how surrounding traffic participants will evolve under its own motion. However, existing end-to-end driving models typically encode scenes into dense visual or BEV features, leaving object-level interactions implicit. We propose (INTERACT INTERaction-aware Action-Conditioned Trajectory learning), a three-stage framework that learns a slot-based object-centric world model from unlabeled driving videos and transfers it to trajectory planning. INTERACT first learns recurrent slots that group dense visual features into temporally structured scene-entity representations without object annotations. It then infers latent ego actions from slot context and video transitions, and uses them to condition future slot prediction, capturing how ego motion and surrounding entities co-evolve. Finally, the learned slots, latent actions, and future slot predictions are distilled into a trajectory planner to guide trajectory generation and selection. Extensive experiments on NAVSIM and nuScenes validate the effectiveness of INTERACT. In particular, it achieves state-of-the-art performance on NAVSIM, with improvements in safety-critical collision metrics.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02519

MedHEB: Benchmarking Medical Embeddings Across Heterogeneous Clinical Evidence

Yingshu Li, Shaoyang Zhou, Zhanyu Wang, Yunyi Liu, Xinyu Liang, Xi Zhang, Lingqiao Liu, Lei Wang 等 9 位作者

原文摘要

Clinical evidence is inherently relational: medical images are interpreted with prior studies, cross-modal examinations, reports, and localized findings, and retrieving relevant evidence is therefore central to clinical interpretation. Recent foundation models increasingly encode these heterogeneous sources into shared embedding spaces, yet existing embedding evaluation often focuses on isolated matches, leaving unclear whether current models retrieve relevant evidence across changes in modality, appearance, and granularity. We introduce MedHEB, a unified benchmark with 107 retrieval tasks from 54 datasets, including both in-distribution and held-out-source evaluation, across three axes: evidence form (2D images, 3D volumes, text), pairing type (image--image, image--text, text--text, and modality-bridging visual matching), and relevance granularity (global, question-conditioned, region-level). Under a single ranking protocol, MedHEB varies clinically relevant evidence definitions to test whether embedding proximity remains meaningful across heterogeneous evidence, including modality-bridging visual retrieval and region-level text-to-region retrieval. Across representative models, performance is uneven across retrieval families: models strong on conventional image--text or text--text retrieval are less reliable on modality-bridging and region-level tasks. Current embedding spaces capture some clinical relationships well but do not yet provide balanced retrieval across heterogeneous evidence. We further provide MedEmb, an MLLM-based reference embedding model trained on the MedHEB ID split, as a reproducible baseline for unified 2D--3D--text medical retrieval. We release MedHEB with preprocessing scripts, evaluation code, access manifests, and MedEmb to support research on clinically meaningful medical embeddings.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02512

Latent Spectral Prompting for Multimodal Hyperspectral Object Tracking

Wangshu Cai, Jun Zhou, Lei Wang, Mohammad Aminul Islam, Xu Han, Kuldip Paliwal

原文摘要

Hyperspectral videos provide rich spectral signatures for robust tracking under challenging conditions such as camouflage and low illumination. However, limited hyperspectral annotations and the large RGB-hyperspectral domain gap hinder the direct use of modern temporally pretrained RGB trackers. We propose SProTrack, a multimodal framework that transfers temporal knowledge from pretrained RGB trackers to the hyperspectral domain through latent spectral prompting. The key idea is to decouple temporal modeling from spectral adaptation, allowing temporal dynamics to be inherited from RGB foundation models while hyperspectral information is learned from unlabeled data in a self-supervised manner. Unlike band-selection methods that discard most spectral information or prompt-based approaches that rely on local-search crops, SProTrack learns a global, scene-level spectral representation from the full hyperspectral frame using an autoencoder. This latent representation is used to guide spectral prompt extraction while a pretrained RGB tracker preserves strong temporal modeling on RGB-compatible inputs. By encoding spectral-spatial structure into compact latent prompts, SProTrack bridges the RGB-hyperspectral modality gap without extensive retraining or dense supervision. Experiments on HOT2020 and HOT2023 demonstrate that SProTrack achieves the best overall performance, with strong results across all spectral domains.

cs.CV · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02506

CellPlex: Benchmarking Single-Cell Spatial Proteomics Prediction from H&E

Zijun Gao, Yijiang Chen, Xiangde Luo, Pheng-Ann Heng

原文摘要

Spatial proteomics measures tens of proteins in every cell of a tissue section, but its cost confines it to small research cohorts, whereas hematoxylin and eosin (H\&E) slides exist for nearly every patient. Inferring single-cell spatial proteomics from H\&E would bring its analyses to routine pathology, and a growing number of virtual staining models pursue this goal. Each is evaluated on its own cohort, panel and pixel-level metric, and released models transfer poorly to new cohorts, leaving open which approach to build on and which proteins H\&E can reveal. We introduce CellPlex, a benchmark that poses the task at the level of the cell: seven cohorts with same-section H\&E and multiplexed immunofluorescence, from colorectal and breast cancer to lymph-node metastases, bone marrow and glioma, with 717 samples, 44.8 million cells and 149 proteins, processed by one pipeline into per-cell targets. Pathology foundation models (PFMs) are adapted with one cell-level protocol, specialist models are fine-tuned under a matched budget, and all are scored on the same cells. Adapted PFMs are the most accurate models on every cohort, ahead of the specialists even after fine-tuning, but keep less than half of their accuracy on cohorts they were not trained on. Which proteins can be predicted depends on the tissue, not on how many cells were measured for them. By fixing the cells, targets and splits on which predictions are judged, CellPlex sets a common standard for inferring costly molecular measurements from routine H\&E and shows how far H\&E can stand in for spatial proteomics. We will release the data, splits, evaluation code and baselines as a testbed for developing and comparing future pathology foundation models and virtual staining methods.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02505

QFacePlacement: From Arbitrary Tensorization to Semantic Spatial Correspondence in Modern Face Verification

Krishna Bhatia, Kavita Choudhary, Pruthwik Mishra, Praveen Kumar Chandaliya

原文摘要

Quantum-inspired computer vision often reshapes high-dimensional features into tensors and applies tensor-network models. In face verification, this can be misleading: modern embeddings are optimized for cosine similarity, and tensorization alone does not establish the low-rank structure needed for tensor trains or matrix-product operators (MPOs). We study model placement through two controlled interfaces. First, cross-model embedding alignment requires a 512 x 512 operator to map AdaFace embeddings into ArcFace space. Dense Ridge and Procrustes mappings remain strong, while deterministic TT-SVD and MPO compression underperform even with basis changes and low-rank residuals. At a 64K-parameter storage budget, mean directional AUC is 0.9297 for dense Ridge, 0.8952 for rank-64 SVD, 0.8685 for the best low-rank-MPO hybrid, and approximately 0.649 for raw MPO. Second, before global pooling, we construct a 7 x 7 x 7 x 7 probe-gallery correspondence tensor from local AdaFace descriptors. A geometry-aware symmetric tensor train with bond dimension 2 uses 122 trainable parameters, compared with 2,403 for a dense linear matcher, while attaining an LFW AUC of 0.9802. Yet compression does not translate into better low-false-acceptance performance: on CFP-FP, TAR at FAR = 0.001 is 0.0102 versus 0.1734 for global cosine matching, and on CPLFW it is 0.7805 versus 0.8737. These results suggest that quantum-inspired factorization is most credible when the chosen interface provides task-aligned axes and compressible structure. They also show why aggregate AUC must be assessed alongside operational low-FAR metrics.

cs.CV · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02499

Fast-Slow Evolutionary Occupancy Prediction via Controlled Dynamics

Runhe Yang, Zhiyuan Zhou, Yuxiang Yan, Xuetong Yang, Yikai Pan, Jiacheng Tang, Zhuolin He, Jianhua Han 等 10 位作者

原文摘要

3D semantic occupancy prediction provides dense geometric and semantic scene representations for autonomous driving, where both high accuracy and low response latency are crucial for safe downstream forecasting and planning. Existing methods usually exhibit different accuracy-latency characteristics. Performance-oriented models provide stronger geometric and semantic predictions, while deployment-friendly models provide faster responses. This motivates EvoOcc, a fast-slow evolutionary occupancy prediction framework that jointly exploits their complementary strengths to improve the accuracy-latency balance without changing the upstream architectures. EvoOcc models evolutionary occupancy prediction as a continuous time controlled dynamical system, enabling flexible adaptation to variable slow system delays and irregular fast system arrivals. The framework further decomposes the evolution process into dual state evolution where ego state evolution compensates for the deterministic coordinate shifts and scene state evolution models the genuine geometric and semantic scene changes. Extensive experiments on three different fast-slow system pairs and irregular prediction arrivals show that EvoOcc consistently improves over fast system baselines while maintaining much lower latency than the slow system, indicating a favorable accuracy-latency balance and stable behavior under practical inference situations.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2610.33702

Seeing the Options: What Should a Visual Candidate See?

Fengxin Li

原文摘要

Agents often choose among candidates that are images: elements of a screenshot, product photos, charts. A single-pass decision model either encodes the request and all candidates as one sequence, making its answer depend on candidate order, or encodes each candidate alone, which keeps order invariance and reusable encodings but hides the request from the candidate. We ask where the joint encoder's advantage comes from: from candidates seeing the request, or from candidates seeing each other while they are encoded. With one backbone and the same fine-tuning steps on screenshot crops of Multimodal-Mind2Web, the answer is mostly the request. Continuing each candidate from a forked cache of the encoded request recovers 11 to 13 of the 12 to 17 points that independent encoding loses to joint encoding, at the price of cross-request reuse, and we prove that order invariance stays exact whenever candidates never see each other. A joint encoder retrained with its candidates isolated from one another during encoding loses 1.1 and 0.4 ranking points in one paired run (0.7 and 0.3 against the mean of five joint runs), and permutation-equivariant interactions among candidates, over pooled vectors or inside the encoder, gain nothing beyond continued training. At a matched abstention operating point the state-conditioned model is 0.6 and 1.0 points below the joint encoder (mean of four runs) with a flip rate of zero, against 8-10% for the joint encoder; on AndroidControl crops (three and five runs) it is 2.5 to 4 ranking points below. Finally, the abstention threshold drifts: the choice term of the loss ignores the score level, and the coverage term pins it only weakly once positives saturate; a quantile threshold aligns operating points.

cs.AI · cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02488

End-to-End Stability of Online Active Learning in Large-Scale Neural Systems

Shuiwen Shen, Jinhua Zhang, Chenlei Hou, Peng Shi, Yong Han, Bo Gao, Xiaoxiao Na, Zhihui Lin 等 9 位作者

原文摘要

Online Active Learning (OAL) aims to extend deep learning from static inference after offline training to continuous adaptation during real-time interaction with a changing physical environment. This transition is essential for safety-critical applications, but it raises a central theoretical question: whether the online parameter update process remains stable when scaled to multilayer neural architectures. This paper develops a stability-focused OAL framework. The framework uses prediction error as a feedback signal to update a structured deep model, thereby linking online learning with Lyapunov-based energy minimization. We show that prediction error convergence alone is insufficient for global parameter convergence because multilayer networks may admit high-dimensional manifolds of equivalent output solutions. The analysis therefore separates output error regulation, internal zero dynamics, interlayer coupling, and parameter convergence. Persistency of excitation and active damping are then used to remove the residual null-space modes that remain after output error convergence. A cascaded Lyapunov analysis establishes asymptotic stability of the overall OAL learning process, while monotonic activation functions and diffeomorphic layer mappings provide the uniqueness condition for the target parameter trajectory. The resulting proof provides a theoretical basis for applying online adaptive learning to large-scale neural systems in nonstationary and safety-critical settings.

cs.LG · cs.CV

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版本更新 · v2arXiv:2606.08239

When No Answer Is Correct: Diagnosing Absent Answer Detection for MLLMs in Video Understanding

Yiheng Wang, Yueqian Lin, Lichen Zhu, Yudong Liu, Hai "Helen" Li, Yiran Chen

原文摘要

Multimodal large language models (MLLMs) have made substantial advancements in video understanding, yet the reliability of their responses remains underexplored. This work presents a diagnostic study of absent answer detection for MLLMs in video understanding, where the correct answer is deliberately excluded from the candidate set and a reliable model is expected to recognize that no valid option exists. We evaluate the absent answer detection behavior under three settings: multiple-choice questions augmented with a ``None of the Above'' option, open-ended generation with a detection instruction, and standard evaluation without any guidance. Across a diverse set of models and benchmarks, we find that MLLMs overwhelmingly select plausible distractors rather than detecting the absent answer. This failure is more pronounced in temporal reasoning tasks and worsens with denser frame sampling. We further explore chain-of-thought prompting as a mitigation strategy and find that while it substantially improves detection rates, performance remains unsatisfactory, suggesting that prompting-based strategies alone are insufficient to fully address this limitation. These findings expose a systematic failure in absent answer detection and highlight the need for explicit detection mechanisms in multimodal systems.

cs.AI · cs.CL · cs.CV

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2610.10288

TouchScale: 500 Hours of Human Vision and Touch for Visual-Tactile Learning

Dayou Li, Hao Wang, Qianqian Yang, Zihao Zhu, Haoquan Fang, Ziyao Zeng, Yan Han, Zihan Wang 等 26 位作者

原文摘要

Large-scale egocentric human interaction data is becoming an important source of physical supervision for embodied learning, yet video alone leaves the contact and pressure that characterize physical interaction unrecorded. Recent visual-tactile datasets provide this missing supervision, but their synchronized tactile data remain far smaller in volume than human video. Moreover, the largest resources often merge recordings from different sensors or annotation procedures, which makes the effect of data scale difficult to isolate. We therefore introduce TouchScale, a 500-hour dataset of contact-rich human interaction recorded with a single unified wearable setup. Its approximately 2K predefined task descriptions span everyday activities and structured manipulation, and each recording temporally aligns egocentric RGB-D video with wrist RGB video and dense full-hand bimanual tactile measurements. Compared with prior tactile data, training on the full TouchScale raises zero-shot contact IoU on data from an unseen tactile sensor from 0.134 to 0.383. Pretraining a visual encoder on TouchScale also yields the highest action recognition accuracy on three benchmarks among the compared visual-tactile datasets. Used for visual-tactile mid-training of a robot policy, TouchScale improves the average real-world success rate across four contact-rich manipulation tasks from 22.5% to 57.5%. With the sensor and collection protocol held fixed, both zero-shot tactile prediction and robot success show an overall upward trend as more TouchScale data is used. These results suggest that human visual-tactile data collected at scale with consistent sensing benefits both perception and robot manipulation. We will publicly release TouchScale, including all synchronized visual-tactile recordings and reconstructed object models, to support future research on scalable visual-tactile learning.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2608.07015

Understand Before Detect: Vision--Language Learning for Omni-Domain Infrared Small Target Detection

Haoyang Yuan, Boyang Li, Yingqian Wang, Yimian Dai, Nuo Chen, Xinfei Huang, Shuqi Yi, Zaiping Lin 等 10 位作者

原文摘要

Omni-domain infrared small target (IRST) detection is crucial for infrared surveillance, yet remains challenging due to heterogeneous imaging domains and inconsistent target characteristics. Previous deep learning-based methods have been developed for visual-only paradigms and achieved promising performance on domain-specific tasks. However, existing methods follow the task-specific supervised learning paradigm. This paradigm simplifies the full-scene infrared observations to sparse target supervision, discarding the semantics that remain invariant across heterogeneous domains. Consequently, detection performance suffers substantially under domain shifts. To handle this issue, we introduce \textbf{``understand before detect''}, a paradigm that formulates omni-domain IRST detection as an understanding-driven process, where holistic infrared target understanding precedes precise detection. Building on this paradigm, we propose \textbf{JinSight}, which first develops holistic IRST understanding through language supervision and then transfers the learned cross-domain representations to precise small-target detection. By grounding infrared representations in language semantics, JinSight enables a single model to generalize across heterogeneous infrared domains. We then introduce Latent Semantic Interaction (LSI), which exchanges language-aligned global semantics with fine-grained spatial features in a compact low-rank space. To address the lack of multimodal omni-domain IRST benchmarks, we build \textbf{OmniIRST-VL}, the first large-scale, highly diverse vision--language dataset for omni-domain IRST detection. It comprises over 39k annotations across six complementary instruction tasks covering both scene-level understanding and target-centric reasoning.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2607.15321

Rethinking the Readout: Unlocking Video Backbones for AI-Generated Video Detection

Manni Cui, Ziheng Qin, ZiAn Wang, Ruiqi Liu, Dianyuan Zou, Jianglan Wei, Han Zhou, Yu Liu 等 11 位作者

原文摘要

AI-generated videos (AIGVs) typically contain subtle temporal artifacts that arise from inter-frame inconsistencies rather than within individual frames. A detector that captures such artifacts should therefore benefit from video pretrained backbones over image only ones. In practice, however, video backbones with standard global readouts often fail to outperform strong image pretrained probes on AIGV benchmarks. We attribute this gap to excessive spatiotemporal aggregation in the readout. Video pretrained backbones tend to compress each frame into a single global descriptor. This compression suppresses local patch level temporal dynamics and discards inter patch relations, which are precisely the cues that AIGV detection most reliably depends on. Based on this, we propose Velocity Gated Patch Velocity Profiling (V-PVP), a lightweight readout that replaces only the aggregation layer with two parallel streams over the patch velocity field, adding only about $0.5$M trainable parameters. V-PVP serves as a general plug-and-play module that consistently improves performance across diverse video backbones under both end-to-end fine-tuning and linear probing settings. Our method reaches 95.28 AUC on AIGVDBench while keeping the backbone fully frozen. The results show that simply replacing the aggregation layer reactivates the temporal potential of frozen video backbones, restoring their advantage on AIGV detection. Code is available at https://github.com/manic-cui/Rethinking-the-readout.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2507.01652

Autoregressive Image Generation with Linear Complexity: A Spatial-Aware Decay Perspective

Yuxin Mao, Zhen Qin, Jinxing Zhou, Hui Deng, Xuyang Shen, Bin Fan, Jing Zhang, Yiran Zhong 等 9 位作者

原文摘要

Autoregressive (AR) models have recently emerged as a promising paradigm for image generation, as they can model visual tokens through the same next-token prediction principle that underlies large language models. However, most competitive AR image generators still rely on Transformer attention, whose quadratic token interaction cost and growing key-value cache become increasingly expensive for high-resolution visual sequences. Linear attention provides an attractive route to efficient AR generation, but our study shows that directly importing linear or decay-based attention from language modeling leads to clear quality degradation. We identify a key cause: after a 2D token map is flattened by raster scan, 1D sequence locality no longer faithfully reflects spatial locality, and conventional decay mechanisms may introduce a false-locality bias at row transitions. To address this problem, we propose Linear Attention with Spatial-Aware Decay (LASAD), a simple recurrent attention mechanism that modulates decay according to the original 2D token geometry while preserving linear complexity. Built upon this layer, LASADGen replaces standard attention in an AR image generator with spatial-aware linear attention. Experiments on ImageNet demonstrate that LASADGen achieves a favorable quality-efficiency tradeoff, improving generation quality over linear-attention baselines while reducing inference cost and memory usage compared with Transformer-based AR models.

cs.CV · cs.AI · cs.MM

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02437

Evaluating Synthetic Skin Lesion Images for Classifier Training: A Safety-Aware Algorithm

Amindu Kumarasinghe, Vinh Bui, Reza Ghanbarzadeh

原文摘要

Skin cancer classifiers need large sets of annotated images, which are scarce, especially for rare lesion types. AI-generated images can fill the gap, but a generator produces images of widely varying quality, so selecting which images to retain is important. That choice usually rests on a single quality score, most often the Fréchet Inception Distance (FID), which has not been tested against what a classifier needs and cannot tell whether the generator memorizes its training photographs. We test that practice with a tournament of 16 quality metrics. Each metric alone selects a training subset from a pool of 275,472 images generated by a diffusion model (EDM2), and a classifier trained on each subset scores the metric per class. The tournament shows that no single metric serves every class: FID computed on Inception features gives the best classifier for one lesion type and no measurable advantage over random sampling for another, and the generation setting it prefers is also the one closest to the training images. We turn these per-class measurements into a safety-aware evaluation algorithm that weights each metric by its measured value in each class and removes images too close to a real training image. On the held-out ISIC-2018 test set, the algorithm improves Macro-$F_2$, a recall-weighted measure of classification performance, over the FID baseline for a Swin-B classifier by +0.0136 to +0.0164 across four safety thresholds (Bonferroni-corrected $p < 0.02$) and over random sampling by about +0.03, though not for ResNet-50. Tightening the threshold improves a memorization-safety score with no measurable change in utility, but every curated set remains less safe than random sampling. The results show a modest but statistically significant gain on the primary classifier, and offer a practical, safety-aware procedure for evaluating and using synthetic data.

cs.CV · cs.LG

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arXiv ↗PDF ↗
版本更新 · v5arXiv:2509.24773

ViSSFlow: Unifying Video-Conditioned Sound and Speech Generation via Joint Learning

Xin Cheng, Yuyue Wang, Xihua Wang, Yihan Wu, Kaisi Guan, Yijing Chen, Peng Zhang, Xiaojiang Liu 等 10 位作者

原文摘要

Video-conditioned audio generation, including Video-to-Sound (V2S) and Visual Text-to-Speech (VisualTTS), has traditionally been studied as two separate tasks despite their shared objective of generating audio conditioned on visual content. Existing methods rely on task-specific architectures and training paradigms, while the possibility of a unified generative framework remains largely unexplored due to heterogeneous conditioning signals and limited joint training data. In this paper, we propose ViSSFlow, a unified flow-matching framework for both V2S and VisualTTS. To effectively integrate diverse conditioning modalities within a Diffusion Transformer (DiT), we introduce a disentangled condition aggregation mechanism that exploits the complementary roles of attention layers, using cross-attention to model semantic conditions and self-attention to capture temporally dense visual cues. Furthermore, contrary to the common assumption that joint optimization of V2S and VisualTTS causes task interference, we show that a single model can be trained end-to-end without sacrificing task-specific performance. To address the scarcity of paired training data covering both sound and speech generation, we further propose a simple yet effective feature-level data synthesis strategy that enables robust joint learning. Extensive experiments on V2S, VisualTTS, and joint generation benchmarks demonstrate that ViSSFlow consistently outperforms state-of-the-art task-specific baselines while providing a unified solution for video-conditioned audio generation. Code and demos are provided in Supplementary Material.

eess.AS · cs.AI · cs.CL · cs.CV · cs.SD

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02419

TERA: Task-Evidence Restoration for Underwater Object Detection under Bandwidth-Constrained Transmission

Heejin Kim, Joon-Seok Song, Eunil Park

原文摘要

Underwater object detectors are typically evaluated on pre-transmission images, overlooking additional degradation from bandwidth-constrained downscaling and compression. We introduce AquaTX, a controlled benchmark built from real underwater detection datasets with seen and held-out codecs. We also propose TERA, a metadata-blind receiver-side framework combining task-guided pixel restoration and dual-state feature correction while keeping the detector frozen. Under seen compound degradation, TERA improves mean average precision (mAP50:95, averaged over IoU thresholds from 0.50 to 0.95) from 37.7 to 50.0 on DUO-TX and from 31.5 to 41.3 on RUOD-TX relative to the frozen detector without restoration. TERA also preserves clean-input performance and remains competitive with detector fine-tuning under the held-out AVIF codec. These findings support transmission-aware evaluation and task-guided restoration for underwater object detection. Code is available at https://github.com/hijinkim/TERA.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02405

MirrorComm: A Communication-Efficient Cooperative Perception Framework via Mirrored World Models

Shenghan Luo, Yin Xu, Cixiao Zhang, Yang Wang, Yujie Cao, Zhuoran Xiao, Wenjun Zhang

原文摘要

Cooperative perception (CP) requires substantial communication bandwidth to exchange sensing information among connected agents. A variety of compression techniques have been proposed to reduce transmission costs while retaining task-relevant information. However, most existing methods follow a frame-wise compression paradigm, overlooking temporal predictability and repeatedly transmitting information that could be inferred from historical context. To address these limitations, we propose MirrorComm, the first world model-based communication-efficient CP framework. Specifically, mirrored world models at the transmitter and receiver predict the transmitter's current observation from a shared history of its reconstructed observations. The transmitter compares this prediction with its current observation and selectively transmits observation updates to correct prediction errors, reducing repeated transmission of predictable information. Experiments on the OPV2V dataset across two downstream tasks including object detection and semantic segmentation show that MirrorComm reduces communication bandwidth requirements by up to approximately 66-fold while maintaining comparable performance to a state-of-the-art (SOTA) method. Ablation studies further demonstrate the effectiveness of the individual components.

eess.SP · cs.CV

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2605.18599

Resolving Representation Ambiguity in Feedforward Novel View Synthesis Transformer via Semantic-Spatial Decoupling

Yihang Wu, Yihang Sun, Shaofeng Zhang, Zuxuan Wu, Junchi Yan, Xiaosong Jia, Yu-gang Jiang

原文摘要

Transformer-based models have advanced feedforward novel view synthesis (NVS). Current architectures such as GS-LRM and LVSM mix semantic information (e.g., RGB) and spatial information (e.g., Plücker rays) into a shared feature space. Since Plücker rays naturally carry lattice-like spatial structure, these designs can make the spatial bias interfere with appearance representation and degrade rendering fidelity. To this end, we propose to decouple the representation of feedforward NVS transformers into separate semantic and spatial tokens. The decoupled design keeps semantic and spatial information explicit in their branches while preserving cross-branch interaction through shared attention routing. Built on this design, we introduce optional categorized supervision and bidirectional modulation: the former provides branch-specific training signals, while the latter improves interaction between the two branches. Notably, the base decoupled design introduces virtually zero additional inference latency due to its architectural design. The proposed designs achieve consistent improvements, demonstrating effectiveness across decoder-only and encoder-decoder feedforward NVS models.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02393

IMUdrape: Generating Garment-Mounted Virtual IMU Data from Text through Physics-Based Cloth Simulation

Zikang Leng, Thomas Plötz

原文摘要

Inertial sensors are moving into clothing, from sensorized sportswear to garments instrumented with hundreds of IMUs. Sewn into clothing, a sensor no longer moves rigidly with the body: loose fabric slides, twists and swings, so a garment-mounted sensor records the motion of the fabric as well as that of the body, and this extra motion differs from one garment to the next. Models for such sensors need training data recorded with the deployed garment, yet labeled data are scarce. Virtual IMU data generated from videos or text have eased this scarcity for body-attached sensors, but existing generators attach their virtual sensors rigidly to the body. We present IMUdrape, which generates garment-mounted virtual IMU data from text through physics-based cloth simulation. IMUdrape synthesizes motion from activity descriptions, reconstructs the target garment as a sewing pattern from photographs, places virtual IMUs at the deployment's real sensor positions, and simulates the garment with an intersection-free contact solver, so that every simulation yields paired garment and body signals. On four real datasets with clothing-mounted sensors, IMUdrape improves on existing virtual-data approaches for both activity recognition and full-body pose estimation. With one real subject, its data improve recognition macro-F1 by 7% to 46% over real data alone and by 2% to 11% over body-mounted virtual data from the same motion. When the garment is simulated on recorded motion, the pose error without any real training data falls by 30% to 33% relative to generated motion, and garment data remain 12% more accurate than body data. IMUdrape thus reduces the amount of real data that garment-mounted sensing requires.

cs.CV · cs.HC

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arXiv ↗PDF ↗