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版本更新 · v2arXiv:2605.11722

EPIC: Efficient Predicate-Guided Inference-Time Control for Compositional Text-to-Image Generation

Sunung Mun, Sunghyun Cho, Jungseul Ok

原文摘要

Recent text-to-image (T2I) generators can synthesize realistic images, but still struggle with compositional prompts involving multiple objects, counts, attributes, and relations. We introduce EPIC (Efficient Predicate-Guided Inference-Time Control), a training-free inference-time refinement framework for compositional T2I generation. EPIC casts refinement as predicate-guided search: it parses the original prompt once into a visual program of object variables and typed predicates, then verifies each candidate image against this program using visual evidence extracted from that image. A candidate is judged to satisfy the prompt when all predicates pass; otherwise, unmet predicates guide the next step, routing localized failures to targeted editing and layout-level or persistent failures to resampling. On GenEval2, EPIC improves the prompt-level Soft-TIFA GM score from 34.16% for single-pass generation to 71.46%. With the same generator, editor, and image-model execution budget, EPIC outperforms the strongest baseline by 19.23 points. It also uses 31% fewer image-model executions, 72% fewer multimodal large language model (MLLM) calls, and 81% fewer MLLM tokens per prompt.

cs.CV · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06221

FedXCrop: Privacy-Preserving Crop Disease Detection via Federated Learning with Post-Aggregation Explainability under Non-IID Farm Distributions

Kriti Saini, Manikandan P

原文摘要

Deep learning can classify crop diseases accurately from benchmark leaf images, but centralized training requires farms to share raw data, and predictions without explanations offer limited support for treatment decisions. We present FedXCrop, a federated crop disease classification pipeline that quantitatively evaluates explanations of the aggregated global model. A MobileNetV2 model trained centrally on PlantVillage achieved 99.38% test accuracy (standard deviation 0.05 over three seeds) on a fixed test set of 5,428 images across 38 classes. Federated models initialized with ImageNet weights were trained for 30 rounds across five clients. FedAvg achieved 98.81% accuracy with identically distributed clients and 98.81%, 98.59%, and 97.78% under Dirichlet label skew (alpha = 1, 0.5, and 0.1, respectively). FedProx was not significantly different from FedAvg at any heterogeneity level (McNemar p > 0.3). Grad-CAM and SmoothGrad explanations were evaluated on 380 test images. Both attributed more energy to the leaf than chance (0.70-0.78 versus 0.60), while Grad-CAM maps from federated and centralized models showed Spearman correlations of 0.74-0.76. Agreement decreased as heterogeneity increased, lesion localization was only modestly above chance, and a model-randomization test indicated that SmoothGrad maps depended substantially on image structure rather than learned parameters. In this setting, label skew reduced federated accuracy by 0.6-1.6 percentage points relative to centralized training. FedAvg largely preserved Grad-CAM behavior, while FedProx provided no measurable benefit.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06205

Cross-Modal Semantic Successive Refinement for Generative Image Communication over Underwater Acoustic Channels

Xu Pei, Bin Wang, Guangxu Zhu, Yi Zhang, Haiyan Fan

原文摘要

Underwater acoustic communication faces challenges such as limited available bandwidth and complex channel environments, which impose demanding requirements on efficient image transmission and reliable reconstruction. To address these challenges, this paper proposes a generative image communication framework based on cross-modal semantic successive refinement. At the transmitter, text descriptions, compact bottleneck features, and visual side information compressed by residual vector quantization are transmitted. At the receiver, text and bottleneck features are first fused to predict a base estimate of the intermediate visual representation. The received RVQ indices are then used to recover supplementary visual information and perform residual correction, followed by image reconstruction using a one-step Pixel Mean Flow model. During training, a CLIP-based text-image semantic consistency constraint is introduced to preserve the semantic correspondence between generated results and the original text descriptions. During transmission, protection resources are allocated according to the importance of different information branches and RVQ quantization stages. Simulations using COCO 2017 images and measured underwater acoustic channel records from Watermark show that text and bottleneck features provide complementary semantic and structural conditions, RVQ visual side information further improves reconstruction quality, and layered unequal error protection helps improve recovery at low SNRs. Code-rate allocation experiments reveal a tradeoff between the reliability of fundamental conditions and visual-detail reconstruction under a limited visual-feature coding budget, providing guidance for allocating protection resources in underwater acoustic image transmission.

cs.IT · cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06203

BiCG-Net: Target-Specific Image Quality Assessment via Observation Modeling and In-Image Differencing

Yibu Yang, Tingqiong Cui, Liming Lei, Hao Xiong, Jun Xu, Siyuan Liu, Guanghui Huang

原文摘要

We estimate the quality of a specific target region (e.g., a train body) in an image, rather than the whole image. This differs fundamentally from whole-image No-Reference Image Quality Assessment (NR-IQA): when the target occupies a small fraction of the frame, the whole-image score is dominated by background, and no public benchmark provides target-level human Mean Opinion Score (MOS). We estimate target quality via nested sampling - a sequence of patches expanding outward from the target box - and infer quality from the relative structure of this sequence. This motivates BiCG-Net, a student network that distills an 11-teacher ensemble through a linear observation model and an in-image differencing mechanism, which provably cancels the target term and teacher-specific bias. We train and test the student independently on each dataset, and evaluate zero-shot transfer across benchmarks. On CSIQ and LIVEC, the student reaches PLCCs of 0.9009 and 0.9133 against human MOS, exceeding the best single teacher and the teacher consensus. On a self-built MOS-free railway dataset, the student reaches a PLCC of 0.9855 against a weak label. The student exceeds its teachers because it learns a new mapping from the relative structure of teacher readings to the ground-truth label, rather than mimicking them. Residual analysis on CSIQ and LIVEC shows the prediction residual is significantly correlated with background contamination, teacher dispersion, and label extremity, demonstrating the student extracts information the consensus does not contain. Under CSIQ-to-LIVEC transfer, the frozen student reaches a PLCC of 0.8416, exceeding the consensus by 0.1947. We position BiCG-Net as an offline target-level quality auditing and label-generation engine; its 39,482-parameter student is compact by design, acting as a unifying layer mapping heterogeneous teacher readings onto a common quality scale.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06195

Annotator-Free Object Detection via Foundation Model Collaboration

Yasiru Ranasinghe, Vishal M. Patel

原文摘要

Object detection research remains constrained by high annotation costs and the closed-set assumptions of supervised models, limiting scalability to novel categories. We present a target-dataset annotation-free object detection framework that eliminates the need for human labeling on the target dataset by leveraging the complementary strengths of pretrained foundation models, including segmentation and vision-language systems, combined with a self-refining training loop. To further reduce reliance on expensive LVLM queries, we introduce a landmark-based clustering mechanism that amortizes labeling across image clusters, improving label consistency while lowering computational cost. Our method autonomously generates segmentation proposals, assigns semantic labels through multimodal reasoning, and iteratively improves both localization and classification using a lightweight open-set detector. This modular pipeline enables adaptation to completely unseen object classes and domains without any human-provided target-dataset annotations, while maintaining compatibility with existing detection architectures. Evaluations on COCO, LVIS, ODinW-35, and Roboflow-100 demonstrate substantial performance gains over state-of-the-art open-vocabulary and long-tail detection methods, achieving 60.8 AP on COCO and 47.4 AP on LVIS under zero-shot settings. Beyond strong benchmark results, our approach provides a scalable foundation for real-world detection systems in unknown environments, where annotation is infeasible.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06180

Rohbau3D-Semantics: Dense Semantic and Instance Labels for Point Clouds of Indoor Shell Construction Scenes

Lukas Rauch, Thomas Braml

原文摘要

We present the Rohbau3D-Semantics dataset, which adds dense semantic and instance-level labels to laser scans of the previously released Rohbau3D domain data from shell construction sites. The dataset comprises 504 point cloud scenes from 14 survey sites, each captured with a terrestrial laser scanner and stored in a scanner-centered local coordinate system. Unlike point clouds sampled from reconstructed meshes or synthetic building models, the data preserve the characteristic properties of real laser scanning, including non-uniform sampling, measurement artifacts, reflections, and fine geometric detail. Combined with manual labels, expert oversight during pointwise annotation, and full-scene review, this yields a high-quality benchmark for semantic scene understanding in complex built environments. The dataset includes 17 class labels of building components and one background class, along with instance annotations for object-level analysis. In addition to the 3D point clouds, the release also provides scripts for deriving geometrically aligned panoramic and cube-map representations of color, LiDAR reflection intensity, depth, surface normals, semantic labels, and instance labels from the 3D arrays. We describe the acquisition pipeline, annotation protocol, quality-control process, and panorama-generation procedure, and provide benchmark splits and baseline experiments for semantic segmentation. By combining precise terrestrial laser scans, dense annotations, and a wide variety of shell-construction scenes, Rohbau3D-Semantics bridges the gap among synthetic indoor datasets, mesh-derived benchmarks, and smaller construction-specific resources. The dataset is intended to support research on 3D scene understanding, construction-site analytics, and multimodal scene analysis in the built environment. Access and download the full dataset via https://github.com/RauchLukas/rohbau3d

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.30698

MM-VeriAgent: Learning to Use Extensive Tools to Verify Multimodal Misinformation with Reinforcement Learning

Peipei Li, Shuhan Xia, Shengyang Liu, Zekun Li, Ran He

原文摘要

Real-world multimodal misinformation often involves mixed forgery sources, requiring sample-specific detection strategies. Existing tool-augmented methods rely on predefined workflows or inference-time planning, limiting adaptability or increasing inference cost. To address this issue, we introduce \textbf{MM-VeriAgent}, which learns to verify mixed-source multimodal misinformation with tools. We first build \textbf{MM-VeriTools}, a specialized toolkit for misinformation detection agents. By benchmarking various candidate models and methods on the sub-tasks required by mixed-source detection, we select the strongest for textual, visual, and cross-modal forgery analysis and encapsulate them as callable tools with a unified interface. On top of this toolkit, we train the LVLM agent with reinforcement learning to teach it how to use these tools to better solve mixed-source detection. Since many of the tools are specialized models whose online execution at every rollout severely limits RL efficiency, we further introduce \textbf{Tool-Execution Cache}, which pre-executes candidate tool calls and reuses their cached outputs during training. This preserves multi-step rollouts while reducing online tool execution, largely improving the training efficiency.Experiments on MMFakeBench demonstrate substantial accuracy gains over the base model without explicit tool search at inference time. Ablation and efficiency analyses further validate the learned tool-use policy and show that Tool-Execution Cache reduces online tool executions during training.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.03455

Bg-Mix: On Using Image Backgrounds for Data Augmentation in Electron Microscopy Images

Ankur Srivastava, Joana Sarah Grah, Katharina Ladewig, Nils Körber

原文摘要

Electron microscopy (EM) serves as a cornerstone in virology enabling the visualization of viral particles and is widely used in research, diagnostics and morphological characterization of pathogens. Computer-assisted image analysis can help facilitate and speed up this labor-intensive method, however, typical EM virus datasets are small and sparsely labeled, resulting in a lack of readily available deep learning models due to training limitations. To overcome data shortages and leverage content-rich, diverse sample backgrounds, we employed background-based data augmentation. To this end, we propose Bg-Mix, a form of data augmentation method, which decomposes training images into foreground objects and backgrounds and subsequently rearranges both into novel augmented images. This not only enhances the performance of object detection models but also increases the diversity of the training data. Using Bg-Mix provides an increase between +0.8 and +3.3 box mean average precision (mAP) points over the baseline when tested across two datasets using two architectures, thereby demonstrating its effectiveness.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2508.03069

SSFMamba: Learning Symmetry-driven Spatial-Frequency Modeling for Physically Consistent 3D Medical Image Segmentation

Bo Zhang, Yifan Zhang, Shuo Yan, Yu Bai, Zheng Zhang, Wu Liu, Xiuzhuang Zhou, Wendong Wang

原文摘要

Accurate 3D medical image segmentation requires a delicate balance between fine-grained local details and global contextual understanding. While spatial-domain models often struggle with long-range dependencies, existing frequency-based approaches frequently overlook intrinsic spectral properties such as Hermitian symmetry, leading to suboptimal feature integration. In this paper, we propose SSFMamba, a Mamba-based Symmetry-driven Spatial-Frequency fusion framework tailored for 3D medical imaging. Our architecture employs a complementary dual-branch design: the spatial branch models voxel sequences to preserve intricate anatomical textures, while the frequency branch operates on spectral components, each of which aggregates information from the entire volume, to capture global context. A core innovation is the 3D Multi-Directional Scanning Mechanism (MDSM), which integrates Hermitian symmetry with the causal nature of State Space Models (SSMs) to enable direction-aware global modeling. Crucially, by shifting the modeling focus to frequency-domain spectral components, SSFMamba captures the underlying structural characteristics of anatomical tissues. This leads to a highly adaptable framework that excels in both MRI and CT applications, regardless of the significant variations in intensity distributions. Extensive evaluations on the BraTS2020, BraTS2023, and BTCV datasets demonstrate that SSFMamba consistently outperforms state-of-the-art methods. Notably, our approach achieves exceptional performance on low-contrast organs such as the pancreas (81.97% Dice), underscoring its potential as a unified and physically consistent perception framework for diverse 3D clinical applications.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06168

Reasoning-Aware Cross-Modal Association Unlearning in Multimodal Large Reasoning Models

Xinghao Li, Chenchen Tan, Youyang Qu, Shujie Cui, Xingliang Yuan, Cunjian Chen, Chi Liu, Longxiang Gao

原文摘要

Multimodal large reasoning models (MLRMs) integrate visual inputs with textual and parametric knowledge, allowing sensitive information to be accessed through cross-modal associations during reasoning. This raises privacy concerns when a visual input should no longer provide access to such information. Machine unlearning can remove this influence without retraining the model from scratch. However, multimodal unlearning methods mainly target samples, concepts, or knowledge content, without separating the knowledge from the cross-modal association through which it is accessed. In reasoning models, target information may disappear from the final answer while remaining in intermediate reasoning, or be replaced by incorrect substitute knowledge. We therefore study cross-modal association unlearning, where the forget target is a designated visual-to-knowledge association: the model should suppress target-bearing behavior under the designated visual context while preserving access to the same knowledge in permitted contexts. We propose Visual-Keyed Counterfactual Rebinding (VKCR), a parameter-efficient framework for cross-modal association editing. VKCR first localizes the edit to a compact association-conditioned parameter subspace. It then reduces support for the designated target at task outputs and counterfactually rebinds the affected reasoning behavior by discouraging target-bearing or unsupported continuations while favoring valid non-sensitive alternatives. We further introduce CLEAR-R and MLLMU-R to evaluate target leakage, hallucinated reassociation, reasoning preservation, and modality-specific utility. Experiments show that VKCR substantially reduces image-conditioned target leakage and hallucinated reassociation, including across heterogeneous task formulations, while preserving non-sensitive visual reasoning and permitted text-only knowledge.

cs.CV · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.37969

SoL-Refiner: Speed-of-Light One-Step Refinement for High-Resolution Video

Haozhe Liu, Tian Ye, Shuchen Xue, Sensen Gao, Yitong Li, Junsong Chen, Haopeng Li, Jincheng Yu 等 14 位作者

原文摘要

High-resolution video generation is expensive, as its cost grows rapidly with the number of spatiotemporal tokens. A practical alternative first generates a lower-resolution video and then applies a refiner, but conventional multi-step refinement introduces a second sampling bottleneck. We present SoL-Refiner, a one-step video refiner that transforms low-resolution model outputs into 4K videos with a single denoising step. Our three-stage recipe combines high-resolution continual training, reinforcement learning (RL) post-training, and a final one-step distillation. We introduce Refiner-Bench, a video refinement benchmark constructed from the outputs of different video generators, and use a shared-input protocol to compare refiners at approximately 2K output resolution. With LTX-2.3 Refiner as the base model, the one-step SoL-Refiner outperforms all external refiners at 2K on the VBench and UniPercept averages, while at $3840\!\times\!2176$ it improves both metrics over the three-step LTX-2.3 Refiner. With the complete acceleration stack, SoL-Refiner achieves an $8.91\times$ speedup in refinement latency over the same baseline in our 2K latency setting. With MiniMax H3 as the base model, SoL-Refiner produces 2K videos in three refinement steps and is preferred over the official MiniMax H3 2K API in 87% of user-study judgments.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2608.10805

Fast and Memory-Efficient Wavelet Convolutions via I/O-Aware Reformulation

Amit Aflalo, Shahaf E. Finder, Roy Amoyal, Eran Treister, Oren Freifeld

原文摘要

Wavelet convolution (WTConv) has emerged as an increasingly popular drop-in replacement for standard convolutions, expanding a network's receptive field exponentially with the number of decomposition levels while keeping the parameter count linear. However, its reference implementation is severely memory-bound due to excessive data movement through high-bandwidth memory (HBM). We develop an I/O model of WTConv to characterize this bottleneck and use it to guide three algebraic reformulations: (1) recomputing the inexpensive Haar analysis butterfly on chip, (2) collapsing the multi-level synthesis cascade into a single closed-form pass indexed by output-coordinate bits, and (3) folding learned per-channel scales into the convolution weights. Together, these reformulations enable an I/O-aware fused implementation that substantially reduces HBM traffic. We evaluate the WTConvNeXt configuration across decomposition levels and a broad range of tensor shapes. Despite performing comparable arithmetic, the reference WTConv is substantially slower than the depthwise convolution it replaces. Our reformulation reduces modeled HBM traffic by approximately $2.55\times$, yielding up to a $4.35\times$ training speedup over the reference while roughly halving peak memory usage. Thus, our reformulation preserves the benefits of WTConv while substantially reducing its execution time and memory footprint, removing the systems overhead that previously limited its practical efficiency.

cs.CV · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.22215

FutureLift: Lifting Trajectory Proposals into Bidirectional Future Graphs for Motion Forecasting

Daniel Grimm, J. Marius Zöllner

原文摘要

Accurate motion prediction is critical for safe autonomous driving, yet even strong forecasters leave residual errors in their multimodal proposals. Existing refiners compress complete proposals or update their segments sequentially, preventing the full proposed future from serving as explicit relational context for correction. We introduce FutureLift, a backbone-agnostic refiner that represents each multimodal proposal as a heterogeneous future graph, with spatially grounded temporal nodes connected to agent histories, map elements, alternative modes, and, for joint prediction, other agents future trajectories. Bidirectional temporal edges allow later evidence to revise earlier segments in a single pass, and the updated nodes predict residual coordinate and confidence corrections. Requiring only proposals, scores, and scene context, FutureLift provides a common refinement interface for structurally different forecasting backbones. On Argoverse2 and the Waymo Open Motion Dataset, it consistently improves frozen proposals and outperforms native and specialized refiners across marginal and joint forecasting. On the WOMD motion-prediction leaderboard, FutureLift ranks first in minADE (0.5049m) and minFDE (0.9942m) among the compared standard-input methods, without LiDAR or ensembling, while achieving a competitive 0.4874 Soft mAP.

cs.CV · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06160

DSRS: Teaching MLLMs to Perceive, Reason, and Segment in Complex Driving Scenes

Sicen Guo, Zhengfei Song, Kuanhsun Chen, Xu Huang, Ping Zhong, Wei Ye, Shaoyi Du, Rui Fan

原文摘要

Existing reasoning segmentation frameworks typically employ multimodal large language models (MLLMs) with broad world knowledge to extract semantics in a human-like manner and generate corresponding segmentation masks. Nevertheless, these frameworks lack the necessary scene-specific awareness. This paper introduces driving scene reasoning segmentation (DSRS), a scene-specific task that requires models to interpret implicit instructions, infer spatial relations, detect potential risks, handle challenging conditions, and comprehend underlying traffic rules, ultimately generating accurate segmentation masks. Meanwhile, we establish a benchmark dataset to facilitate future research on the DSRS task. To pioneer this task, we propose DSRS-Net, a novel architecture with advanced reasoning segmentation capability for automated driving. Specifically, DSRS-Net seamlessly infuses geometric features for deeper reasoning segmentation, without compromising the original multimodal fusion mechanism. This strategy effectively mitigates the modality representation mismatch in MLLMs caused by RGB-only pretraining, while enhancing the integration of geometric, contextual, and linguistic features. Extensive experiments demonstrate the superior performance of DSRS-Net in both driving scene reasoning segmentation and other language-driven segmentation tasks across various public datasets. The source code and dataset will be released upon publication.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06143

PTQ4SAM3: Semantics-Preserving Post-Training Quantization for Segment Anything Model 3

Xi Xiao, Yunbei Zhang, Lin Zhao, Janet Wang, Xinru Jiang, Yanshu Li, Chenrui Ma, Pan Wang 等 10 位作者

原文摘要

Segment Anything Model 3 (SAM 3) can segment objects from text prompts and track them across video, but its memory and computation costs make deployment difficult. Post-training quantization (PTQ) reduces these costs by using fewer bits. However, a quantized model can select the wrong object or lose track of its target across frames. We call this prompt-alignment drift. Controlled tests show that perturbing low-variance feature directions strongly affects target selection. We propose Semantics-Preserving Quantization (SPQ), which gives more calibration weight to concept-sensitive parameters, preserves residual features, and corrects the mean error in video memory. Fixed text concepts guide calibration without task annotations. All three components operate offline and add no inference operators. With a W4A8 image encoder, SPQ improves RefCOCO Pr@0.5 from 42.1 to 72.5 over PTQ4SAM while retaining 54.8 COCO mask AP. Experiments on LVIS, RefCOCO variants, and SA-V show consistent gains. A CLIP study also shows improved image-text retrieval.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06141

RDT-3D: Resolution-Decoupled Training for High-Fidelity 3D Geometry Generation

Jiale Zhang, Yang Liu, Chengyang Li, Jin Yuan, Qianxi Jia, Taicheng Huang

原文摘要

High-fidelity 3D geometry generation requires maintaining global structural coherence alongside fine-grained surface details. While recent coarse-to-fine frameworks leverage Rotary Position Embeddings (RoPE) to condition local queries, we reveal a critical training-inference resolution discrepancy: coordinate guidance and geometric supervision are aligned during training, whereas inference decodes high-resolution geometry from coarse spatial coordinates. This mismatch introduces a severe distribution shift that degrades fine details. To overcome this, we propose Resolution-Decoupled Training (RDT), which anchors RoPE guidance at 512 resolution while supervising geometry at 1536, enabling robust detail extrapolation. Additionally, to eliminate high-resolution data bottlenecks, we present Dual3D-300K, a large-scale paired dataset comprising 300K watertight 3D point clouds explicitly aligned across 512 and 1536 resolutions. Extensive experiments demonstrate that RDT substantially improves sharp geometric regions, while Dual3D-300K offers a scalable benchmark for high-resolution 3D generation.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v5arXiv:2504.15669

UINO-FSS: Unifying Representation Learning and Few-shot Segmentation via Hierarchical Distillation and Mamba-HyperCorrelation

Wei Zhuo, Zhiyue Tang, Wufeng Xue, Hao Ding, Junkai Ji, Linlin Shen

原文摘要

Few-shot semantic segmentation has attracted growing interest for its ability to generalize to novel object categories using only a few annotated samples. To address data scarcity, recent methods incorporate multiple foundation models to improve feature transferability and segmentation performance. However, they often rely on dual-branch architectures that combine pre-trained encoders to leverage complementary strengths, a design that limits flexibility and efficiency. This raises a fundamental question: can we build a unified model that integrates knowledge from different foundation architectures? Achieving this is, however, challenging due to the misalignment between class-agnostic segmentation capabilities and fine-grained discriminative representations. To this end, we present UINO-FSS, a novel framework built on the key observation that early-stage DINOv2 features exhibit distribution consistency with SAM's output embeddings. This consistency enables the integration of both models' knowledge into a single-encoder architecture via coarse-to-fine multimodal distillation. In particular, our segmenter consists of three core components: a bottleneck adapter for embedding alignment, a meta-visual prompt generator that leverages dense similarity volumes and semantic embeddings, and a mask decoder. Using hierarchical cross-model distillation, we effectively transfer SAM's knowledge into the segmenter, further enhanced by Mamba-based 4D correlation mining on support-query pairs. Extensive experiments on PASCAL-5$^i$ and COCO-20$^i$ show that UINO-FSS achieves new state-of-the-art results under the 1-shot setting, with mIoU of 80.6 (+3.8%) on PASCAL-5$^i$ and 64.5 (+4.1%) on COCO-20$^i$, demonstrating the effectiveness of our unified approach.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06120

ReactWorld: Structuring Interaction Responses for Embodied Video Generation

Hanwei Liu, Huijia Zhang, Hanzhang Wang, Guangyu Liu, Hanbei Zhan, Linjie Hu, Bin Xiao, Xuan Zhou 等 9 位作者

原文摘要

Embodied video world models predict scene changes caused by robot actions, yet generated object responses may remain inconsistent with task intent and contact dynamics. Critical responses in embodied scenes often concentrate in local regions and specific time intervals. Inspired by chain-of-thought reasoning, we introduce ReactWorld, an embodied video generation model guided by structured interaction anticipation. In the generation backbone's feature space, an interaction response field predictor anticipates how the interaction will unfold, explicitly describing participant roles, future motion response intensity, and local motion changes. A response feedback adapter forms increments to object features from the difference between the object branch's outputs with predicted and zeroed local dynamics inputs. It also converts predicted displacements into positional biases for temporal attention. These two mechanisms guide object feature updates and information retrieval across frames, respectively. Through interaction anticipation and generative feedback, ReactWorld achieves an average score of 77.51 on RBench, outperforming PhysisForcing by 3.29 points with improvements in all nine categories. On EZS-Bench, it achieves a Domain score of 86.98 and an average score of 81.87.

cs.CV · cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06113

Semantic Prior-Guided Incomplete Cross-Modal Retrieval with Noisy Labels

Jiali Chen, Ruitao Pu, Yingke Chen, Dezhong Peng

原文摘要

Cross-modal retrieval (CMR) aims to bridge the semantic gaps between modalities by learning shared representations. Unfortunately, due to the dynamism and inconsistencies in real-world data acquisition, multimodal datasets frequently encounter severe modality incompleteness. Existing incomplete CMR methods adhere to a label-driven paradigm, relying heavily on categorical annotations as the semantic benchmark. However, label noise is difficult to avoid and often coexists with modality incompleteness. To address this challenge, we propose a novel semantic prior-guided framework for incomplete cross-modal retrieval with noisy labels, termed SPIRN. Specifically, SPIRN uses fixed semantic priors as a consistent reference for robust representation learning, noisy-label handling, and missing-modality completion. Based on this reference, Semantic Priors Guidance (SPG) establishes a stable category-level semantic space, Quality-aware Robust Learning (QRL) dynamically evaluates sample quality to distinguish clean samples from noisy ones and handles noise via reverse learning, Prior-guided Global Label Calibration (PGLC) rectifies corrupted annotations for refined supervision, and Confidence-driven Selective Imputation (CSI) recovers missing modalities from a cross-modal memory bank while filtering unreliable imputations with an increasing threshold. To the best of our knowledge, this is the first framework to jointly tackle the dual challenges of modality incompleteness and label noise in the CMR community. Extensive experiments on four widely used benchmark datasets demonstrate that SPIRN outperforms 11 state-of-the-art methods in various scenarios involving both missing modalities and noisy labels.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.23975

FrameScout: Scouting Query-Relevant Frames for Long Video Understanding

Haonan Hu, Shuhao Chen, Weisen Jiang, Lizhao Gao, James Kwok, Yu Zhang

原文摘要

Long video understanding requires VideoLLMs to answer queries about videos spanning tens of minutes to hours, but such long videos produce far more visual tokens than current VideoLLMs can process. To address this issue, keyframe selectors are proposed to select the most query-relevant frames by computing the similarity between frame and query embeddings. Existing selectors either encode frames independently without temporal context or process chunks in isolation with ranking objectives that are difficult to optimize. To alleviate those limitations, we propose FrameScout with two core designs: (i) a streaming selector architecture that leverages a sliding-window KV cache and successor aggregation to produce temporally-contextualized frame embeddings; (ii) a frame-query contrastive objective that aligns frame and query embeddings in a shared space, directly training the selector to distinguish relevant frames from irrelevant ones. Extensive experiments on three long-video benchmarks and six base VideoLLMs demonstrate that FrameScout consistently achieves state-of-the-art performance. Code is available at https://github.com/Hubert-1102/FrameScout.

cs.CV · cs.LG

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新论文 · v1arXiv:2611.06105

XuanJian-DINOSAR: A SAR Foundation Model Based on Global and Local Semantic Consistency

Chunxiao Wu, Huixin Huang, Jialei Pan, Qiang Liang, Tao Wu, Cheng Wu, Long Huang, Yuanji Li 等 12 位作者

原文摘要

Self-supervised pre-training on synthetic aperture radar (SAR) imagery enables transferable representations, but the optical-SAR domain gap and speckle perturbations limit representation stability. Most SAR foundation models use masked image modeling (MIM) through pixel reconstruction or structural feature prediction, which is sensitive to speckle or relies on predefined operators. Joint modeling of cross-view semantics and perturbation stability at corresponding spatial locations remains underexplored. We propose XuanJian-DINOSAR, a SAR foundation model based on global and local semantic consistency. We curate over 360K publicly available SAR target chips, detection images, and scene images spanning diverse targets, scenes, and sensors, and adapt an optically pre-trained DINOv2 model to SAR through self-supervised learning. The framework combines DINO global semantic self-distillation, iBOT local masked modeling, and KoLeo feature-distribution regularization to learn globally discriminative and locally structure-aware representations. We introduce a local semantic consistency branch tailored to SAR intensity statistics and coherent imaging characteristics. Shared geometric transformations produce spatially aligned teacher weak views and student strong views. Intensity transformations and Gamma multiplicative speckle perturbations are applied to student views, while a cosine consistency loss aligns patch tokens at corresponding locations, promoting stability under radiometric perturbations. This objective complements the cross-region and cross-scale semantic consistency of DINOv2 multi-crop training, jointly enforcing global and local semantic consistency. Experiments on multiple downstream classification and detection datasets demonstrate state-of-the-art performance on most evaluation metrics, validating the generality and transferability of the learned representations.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.28631

AEON: Unifying Video and 3D World Models

Weiqi Zhang, Wenyuan Zhang, Yu-Shen Liu, Junsheng Zhou

原文摘要

Current world models for explicit world simulation typically follow two distinct pathways: (1) video generative models (e.g., Sora), which simulate realistic appearances but fail to preserve 3D consistency; and (2) 3D generative models (e.g., Marble), which generate geometrically consistent scenes but often yield static, less realistic observations. We present AEON, a unified framework that bridges video-based and 3D-based world models and combines the benefits of both. The key insight of AEON lies in a generative model that operates within the text- or image-conditioned video latent space while producing renderable 4D graphics primitives---specifically, 4D Gaussian Splatting (4DGS)---as the explicit world representation. We train a 4D reconstruction model to predict both 4DGS and camera poses from monocular videos, serving as our initialized 4D decoder. Furthermore, we design a domain adapter that translates generative video latents into reconstructive 4D latents through a distillation process on the video VAE. AEON naturally supports video-based world modeling by rendering 4DGS at predicted camera trajectories, and constructs a persistent 4D environment with scene dynamics for realistic world simulation. Consequently, AEON empowers a diverse range of world modeling applications and achieves state-of-the-art performance on standard benchmarks. Project Page: https://weiqi-zhang.github.io/AEON

cs.CV

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新论文 · v1arXiv:2611.06096

Many Pixels, One Composition: Self-Supervised Spectra for Specimen-Level Prediction

Eleftheria Tetoula-Tsonga, George Arvanitakis, Theodoros Giannakas

原文摘要

Can hyperspectral (HS) imaging be used to predict the elemental composition of physical specimens found on the Earth's surface? We approach this problem as a supervised regression task, using an open dataset of specimens, each comprising a HS cube and elemental characterization. In particular, we train an encoder in a self-supervised fashion that operates on pixel-level; then freeze the encoder and train a lightweight attention-based regression head in a supervised manner, by pairing many representations of a given specimen with its corresponding elemental label. In test specimens, our approach significantly outperforms traditional spectral matching and unmixing baselines. It also outperforms supervised learning models based on the mean spectrum (of similar model capacity), while full model fine-tuning achieves the strongest performance, at substantially higher model capacity.

cs.CV · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06093

Drift-adaptive Representation Preservation for Domain-Incremental Learning

Hyunmin Kong, Gyutae Oh, Jitae Shin

原文摘要

Pretrained vision models provide strong general-purpose representations, but in Domain-Incremental Learning (DIL), where the input distribution changes sequentially, adapting to new domains often degrades performance on previously learned ones. We observe that forgetting is not uniform across domains and classes and is not explained by training order alone. This motivates us to track each past domain-class concept individually. We use representation drift, defined as the deviation of a past concept's current representation from a fixed anchor, as a measurable surrogate for forgetting risk. The measured drift then serves two complementary roles. A consolidation loss assigns stronger preservation to high-drift pairs, while prototype construction down-weights highly drifted domains to reduce their influence on the class reference. Preservation is thus allocated by measured drift rather than assumed importance. To obtain this signal during training without storing past raw data, we repurpose a Joint-Embedding Predictive Architecture (JEPA). For each domain-class pair, we store only a compact context summary in the backbone feature space and rerun it through the learned context-to-target relation to measure its drift from the fixed anchor. Across DomainNet, Office-Home, CORe50, and CDDB-Hard, our method sustains a strong accuracy-forgetting balance on every benchmark, using substantially less training memory than accuracy-competitive methods and the smallest storage among compared methods.

cs.CV · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06089

STARE: Scene Text-Aware multimodal Retrieval for Everyday objects

Kento Tokura, Ryosuke Korekata, Daichi Yashima, Kei Katsumata, Kanon Amemiya, Miyu Goko, Komei Sugiura

原文摘要

In this study, we focus on service robots that retrieve and transport everyday objects based on free-form instructions that may refer to visual features and scene text. This is challenging because it requires a multimodal understanding of the visual features and spatial relationships of objects, as well as scene text embedded in the environment. To address this, we propose Scene Text-Aware multimodal Retrieval for Everyday objects (STARE), which leverages visual and textual cues in response to free-form instructions. STARE introduces Crosslingual Visual Prompt to enhance scene text interpretation, and models the complex relationships between named entities in the instructions and their corresponding objects. We evaluate STARE using two novel benchmarks, GoGetIt and TextCaps-test, both of which consist of real-world indoor/outdoor images and instructions involving object manipulation and navigation. The results show that STARE outperforms all baseline methods on standard multimodal retrieval metrics. Furthermore, in physical experiments conducted under a zero-shot transfer setting, STARE achieves an overall success rate of 80%. Our project page and code are provided in the Supplementary Materials.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06085

GeoBiaset: A Counterfactual Benchmark for Demographic Bias in World-Level Geolocalization

Niccolò Niccoli, Silvia Dani, Marco Mistretta, Andrew D. Bagdanov, Marco Bertini, Lorenzo Seidenari

原文摘要

World-level image geolocalization is an increasingly important task, yet the biases of current models remain poorly understood. Prior work has identified coarse dataset imbalances noting, for instance, that some cities are absent from training sets and that models tend to perform better in wealthier regions. However, no one has yet examined which visual cues drive these disparities. To do so, we introduce GeoBiaset, the first benchmark designed to measure how geolocalization models are influenced by the apparent race of a person inserted into the image foreground. The dataset consists of 9,541 images, pairing existing geotagged background images with counterfactual edits spanning seven races, holding the ground-truth coordinates fixed. Controlled masking experiments show that occlusion and background drift do not fully explain geolocalization degradation. Alongside the benchmark, we introduce the Person-Origin Perturbation Indicators (POPI), novel metrics that quantify model resilience to these demographic perturbations by measuring whether predictions are pulled toward the region associated with the inserted group. Our evaluation of state-of-the-art geolocalization and multimodal large language models reveals a 16-47% drop in continent-level performance when a subject from a demographically mismatched group is inserted. A landmark versus no-landmark analysis and gradient-weighted heatmaps show that models preferentially attend to geographic landmarks when present, but shift attention to facial features in their absence.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06083

SleuthVLM: Do We Really Need Audio-in-Video for Omni Video Understanding?

Peiran Wu, Fangfei Gou, Zhaoxi Mu, Changhao Zhang, Jian Liu, Weiqiang Wang, Yuntao Wang, Yuanchun Shi 等 9 位作者

原文摘要

Omni models answer questions about videos by jointly processing sampled frames and audio in one turn, with progress driven largely by scaling model size and omni training data. We ask whether a model needs to hear the audio directly at all, and present SleuthVLM, an agent harness built on a VLM that never hears the audio directly: speech will be transcribed into text, and the VLM will sleuth the video on its own without any predefined workflow. On four omni benchmarks with videos up to 90 minutes, SleuthVLM achieves the best average accuracy: \textbf{75.0%}, 2.6 points above Gemini 3.8 Flash, and it also tops the Video-MME-v2 leaderboard with \textbf{77.8%}; with an open-source 27B VLM, it beats all open-source omni models. We draw three findings: SleuthVLM beats a model that glances; hearing is not the bottleneck, agentic ability is; omni understanding needs better agentic ability, not just more omni data for modality alignment. So we provide a simple but effective starting point for doing omni video understanding. Omni understanding needs better agents, not just bigger models that hear.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06070

RouTex: Multi-reference 3D Texture Generation by Reprogramming Rotary Position Encoding

Chengyang Li, Yang Liu, Jiale Zhang, Qiang Rao, Wei Zhang, Taicheng Huang

原文摘要

While single-image 3D texture generation has advanced significantly, conditioning texture synthesis on multi-view reference images remains largely under-explored, despite its necessity for resolving surface ambiguity in unobserved regions. In this paper, we propose RouTex, a novel multi-reference 3D texture generation framework based on in-context Diffusion Transformers (DiT). Our key insight is that native 3D Rotary Position Encoding (RoPE) inherently possesses spatial addressing capabilities. We introduce Positional Routing (PR) to reprogram 3D RoPE along spatial and semantic dimensions, establishing explicit reference-to-view correspondence without introducing additional parameters. To execute this routing efficiently, Dual-Granularity Attention (DGA) combines per-view local attention for fine texture fidelity with global joint attention over deduplicated references for cross-view consistency. Extensive experiments demonstrate that RouTex achieves new state-of-the-art performance in multi-reference 3D texture generation.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2608.16745

VicEdit: Learning to Edit Videos from Visual In-Context Examples

Yuji Wang, Teng Hu, Yuheng Chen, Ran Yi, Han Feng, Weijian Cao, Chengjie Wang, Lizhuang Ma 等 9 位作者

原文摘要

Despite remarkable progress in instruction-based video editing, textual descriptions remain limited in expressing fine-grained appearance, spatial transformations, and temporal dynamics. We introduce Visual In-context Editing, a new paradigm that shifts video editing from text-only instructions to visual demonstrations, where users specify desired edits through heterogeneous visual references including single images, image pairs, and video pairs. To establish this paradigm, we curate VicEdit-400K, the first large-scale dataset for visual in-context video editing, containing 400K high-quality samples across ten editing tasks with comprehensive semantic and visual consistency filtering. Building upon this foundation, we propose VicEdit, a unified framework that enables effective reasoning over diverse visual contexts and textual instructions. Specifically, we introduce Modality-Adaptive Semantic Distillation to extract modality-aware visual editing semantics and Dual-Context Injection to align visual contexts with textual editing intents. Extensive experiments on VicEdit-Bench demonstrate that VicEdit achieves state-of-the-art performance on both conventional instruction-based editing and visual in-context editing. Further controlled studies and human evaluations verify that VicEdit effectively leverages heterogeneous visual references, establishing visual in-context editing as a promising paradigm for controllable video editing.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06061

TouchéWorld tech report : Towards Online Unified Camera--Hand Exploration in Generative Worlds

Jiaao Yu, Yanjian Zhang, Haoyang Jiang, Guillaume Wisniewski, Nadi Tomeh, Shijie Jia

原文摘要

Recent video world models allow users to explore generated environments through camera navigation. Continuous hand motion provides a fine-grained action channel for interacting with the people and objects encountered during exploration. We present TouchéWorld, a framework that integrates gesture-driven interaction into a camera-controlled video world model. Given an initial image and a scene-description prompt, the model generates video conditioned on temporally aligned camera and hand controls. We construct joint camera--hand training data from heterogeneous egocentric videos through annotation completion, consistency-based filtering, and temporal alignment. Building on a pretrained camera-controlled world model, we combine spatial hand-skeleton conditioning with numerical hand-motion features through parameter-efficient adaptation. Causal adaptation and few-step distillation support chunk-level online generation, allowing users to update their controls based on generated visual feedback. We further introduce TouchéBench to evaluate hand-motion following, camera--hand coordination, online interaction, and object response. Together, these components establish a framework for studying continuous exploration and hand-driven interaction within generative worlds.

cs.CV · cs.AI

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