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首次观测 · 记录 #1CVF · CVPR 2026:cvf:923817a5fc30f23aa383e1b91d2fdd92CVF · CVPR 2026

Lifelong Imitation Learning with Multimodal Latent Replay and Incremental Adjustment

Yu, Fanqi, Tiezzi, Matteo, Apicella, Tommaso, Beyan, Cigdem, Murino, Vittorio

原文摘要

We introduce a lifelong imitation learning framework that enables continual policy refinement across sequential tasks under realistic memory and data constraints. Our approach departs from conventional experience replay by operating entirely in a multimodal latent space, where compact representations of visual, linguistic, and robot's state information are stored and reused to support future learning. To further stabilize adaptation, we introduce an incremental feature adjustment mechanism that regularizes the evolution of task embeddings through an angular margin constraint, preserving inter-task distinctiveness. Our method establishes a new state of the art in the LIBERO benchmarks, achieving 10-17 point gains in AUC and up to 65% less forgetting compared to previous leading methods. Ablation studies confirm the effectiveness of each component, showing consistent gains over alternative strategies. The code is available at: https://github.com/yfqi/lifelong_mlr_ifa.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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首次观测 · 记录 #1CVF · CVPR 2026:cvf:923e2c19242937d9bd329e0d3fed7337CVF · CVPR 2026

UNICBench: UNIfied Counting Benchmark for MLLM

Rong, Chenggang, Han, Tao, Zhao, Zhiyuan, Fan, Yaowu, Wan, Jia, Guo, Song, Yuan, Yuan, Gao, Junyu

原文摘要

Counting is a core capability for multimodal large language models (MLLMs), yet there is no unified counting dataset to rigorously evaluate this ability across image, text, and audio. We present UNICBench, a unified multimodal, multi-level counting benchmark and evaluation toolkit with accurate ground truth, deterministic numeric parsing, and stratified reporting. The corpus comprises 5,300 images (5,508 QA), 872 documents (5,888 QA), and 2,069 audio clips (2,905 QA), annotated with a three-level capability taxonomy and difficulty tags. Under a standardized protocol with fixed splits/prompts/seeds and modality-specific matching rules, we evaluate 45 state-of-the-art MLLMs across modalities. Results show strong performance on some basic counting tasks but significant gaps on reasoning and the hardest partitions, highlighting long-tail errors and substantial headroom for improving general counting. UNICBench offers a rigorous and comparable basis for measurement and a public toolkit to accelerate progress.

cs.CV · cs.CL

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:924d0016accd3de1516b549afb801f47CVF · CVPR 2026

Explaining CLIP Zero-shot Predictions Through Concepts

Ozdemir, Onat, Christensen, Anders, Alaniz, Stephan, Akata, Zeynep, Akbas, Emre

原文摘要

Large-scale vision-language models such as CLIP have achieved remarkable success in zero-shot image recognition, yet their predictions remain largely opaque to human understanding. In contrast, Concept Bottleneck Models provide interpretable intermediate representations by reasoning through human-defined concepts, but they rely on concept supervision and lack the ability to generalize to unseen classes. We introduce EZPC that bridges these two paradigms by explaining CLIP's zero-shot predictions through human-understandable concepts. Our method projects CLIP's joint image-text embeddings into a concept space learned from language descriptions, enabling faithful and transparent explanations without additional supervision. The model learns this projection via a combination of alignment and reconstruction objectives, ensuring that concept activations preserve CLIP's semantic structure while remaining interpretable. Extensive experiments on five benchmark datasets, CIFAR-100, CUB-200-2011, Places365, ImageNet-100, and ImageNet-1k, demonstrate that our approach maintains CLIP's strong zero-shot classification accuracy while providing meaningful concept-level explanations. By grounding open-vocabulary predictions in explicit semantic concepts, our method offers a principled step toward interpretable and trustworthy vision-language models. Code is available at https://github.com/oonat/ezpc.

cs.CV · cs.CL

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:924fad1266ef7814d3bd32631d3914daCVF · CVPR 2026

OddGridBench: Exposing the Lack of Fine-Grained Visual Discrepancy Sensitivity in Multimodal Large Language Models

Weng, Tengjin, Jiang, Wenhao, Wang, Jingyi, Li, Ming, Ma, Lin, Ming, Zhong

原文摘要

Multimodal large language models (MLLMs) have achieved remarkable performance across a wide range of vision-language tasks. However, their ability in low-level visual perception, particularly in detecting fine-grained visual discrepancies, remains underexplored and lacks systematic analysis.In this work, we introduce OddGridBench, a controllable benchmark for evaluating the visual discrepancy sensitivity of MLLMs. OddGridBench comprises over 1,400 grid-based images, where a single element differs from all others by one or multiple visual attributes such as color, size, rotation, or position.Experiments reveal that all evaluated MLLMs, including open-source families such as Qwen3-VL and InternVL3.5, and proprietary systems like Gemini-2.5-Pro and GPT-5, perform far below human levels in visual discrepancy detection. We further propose OddGrid-GRPO, a reinforcement learning framework that integrates curriculum learning and distance-aware reward. By progressively controlling the difficulty of training samples and incorporating spatial proximity constraints into the reward design, OddGrid-GRPO significantly enhances the model's fine-grained visual discrimination ability.We hope OddGridBench and OddGrid-GRPO will lay the groundwork for advancing perceptual grounding and visual discrepancy sensitivity in multimodal intelligence.All resources will be publicly released upon acceptance.

cs.CV · cs.CL

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:925ef5772dea6899d2127a40dade9a09CVF · CVPR 2026

SoccerMaster: A Vision Foundation Model for Soccer Understanding

Yang, Haolin, Rao, Jiayuan, Wu, Haoning, Xie, Weidi

原文摘要

Soccer understanding has recently garnered growing research interest due to its domain-specific complexity and unique challenges.However, prior works typically rely on task-specific expert models, which are resource-intensive and hinder a holistic view of the game.This paper aims to propose a unified framework that enables a single model to handle diverse soccer visual understanding tasks, spanning both fine-grained perception (e.g., athlete detection) and semantic reasoning (e.g., event classification).Concretely, we make the following contributions in this paper:(i) we present **SoccerMaster**, the first soccer-specific vision foundation model that unifies comprehensive understanding tasks within a single framework via **supervised multi-task pretraining**;(ii) we consolidate multiple existing soccer video datasets and develop an automated data curation pipeline, termed as **SoccerFactory**, to produce scalable multi-task training annotations;and (iii) we conduct extensive experiments demonstrating that SoccerMaster consistently outperforms task-specific expert models across diverse downstream tasks, underscoring its breadth and superiority.The data, code, and model will be publicly available to the research community.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:926741f3193f95a74189b67c412a79e4CVF · CVPR 2026

Question-guided Visual Compression with Memory Feedback for Long-Term Video Understanding

Yamao, Sosuke, Miyahara, Natsuki, Qi, Yuankai, Takeuchi, Shun

原文摘要

In the context of long-term video understanding with large multimodal models, many frameworks have been proposed. Although transformer-based visual compressors and memory-augmented approaches are often used to process long videos, they usually compress each frame independently and therefore fail to achieve strong performance on tasks that require understanding complete events, such as temporal ordering tasks in MLVU and VNBench. This motivates us to rethink the conventional one-way scheme from perception to memory, and instead establish a feedbackdriven process in which past visual contexts stored in the context memory can benefit ongoing perception. To this end, we propose Question-guided Visual Compression with Memory Feedback (QViC-MF), a framework for long-term video understanding. At its core is a Question-guided Multimodal Selective Attention (QMSA), which learns to preserve visual information related to the given question from both the current clip and the past related frames from the memory. The compressor and memory feedback work iteratively for each clip of the entire video. This simple yet effective design yields large performance gains on longterm video understanding tasks. Extensive experiments show that our method achieves significant improvement over current state-of-the-art methods by 6.1% on MLVU test, 8.3% on LVBench, 18.3% on VNBench Long, and 3.7% on VideoMME Long. The code will be available at https://github.com/FujitsuResearch/QViC-MF/.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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首次观测 · 记录 #1CVF · CVPR 2026:cvf:926ab4562ca07644189ab7faae05e0cfCVF · CVPR 2026

Keep it SymPL: Symbolic Projective Layout for Allocentric Spatial Reasoning in Vision-Language Models

Jang, Jaeyun, Shin, Seunghui, Park, Taeho, Hwang, Hyoseok

原文摘要

Perspective-aware spatial reasoning involves understanding spatial relationships from specific viewpoints--either egocentric (observer-centered) or allocentric (object-centered).While vision-language models (VLMs) perform well in egocentric settings, their performance deteriorates when reasoning from allocentric viewpoints, where spatial relations must be inferred from the perspective of objects within the scene.In this study, we address this underexplored challenge by introducing Symbolic Projective Layout (SymPL), a framework that reformulates allocentric reasoning into symbolic-layout forms that VLMs inherently handle well.By leveraging four key factors--projection, abstraction, bipartition, and localization--SymPL converts allocentric questions into structured symbolic-layout representations.Extensive experiments demonstrate that this reformulation substantially improves performance in both allocentric and egocentric tasks, enhances robustness under visual illusions and multi-view scenarios, and that each component contributes critically to these gains.These results show that SymPL provides an effective and principled approach for addressing complex perspective-aware spatial reasoning.

cs.CV · cs.CL

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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首次观测 · 记录 #1CVF · CVPR 2026:cvf:92a82b887d476e26aaefd94d38cc2216CVF · CVPR 2026

MotionCrafter: Dense Geometry and Motion Reconstruction with a 4D VAE

Zhu, Ruijie, Lu, Jiahao, Hu, Wenbo, Han, Xiaoguang, Cai, Jianfei, Shan, Ying, Zheng, Chuanxia

原文摘要

We present MotionCrafter, a framework that leverages video generators to jointly reconstruct 4D geometry and estimate dense motion from a monocular video. The key idea is a joint representation of dense 3D point maps and 3D scene flows in a shared coordinate system, together with a 4D VAE tailored to learn this representation effectively. Unlike prior work that strictly aligns 3D values and latents with RGB VAE latents--despite their fundamentally different distributions--we show that such alignment is unnecessary and can hurt performance. Instead, we propose a new data normalization and VAE training strategy that better transfers diffusion priors and greatly improves reconstruction quality. Extensive experiments on multiple datasets show that MotionCrafter achieves state-of-the-art performance in both geometry reconstruction and dense scene flow estimation, delivering 38.64% and 25.0% improvements in geometry and motion reconstruction, respectively, all without any post-optimization.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:92dd6b032c3f60d3a10aafb94149bac6CVF · CVPR 2026

Forensic-Friendly Image Manipulation via Controllable Latent Diffusion

Chen, Hanyu, Wu, Haiwei, Tian, Jinyu, LI, Jianqing, Zhou, Jiantao

原文摘要

With diffusion models demonstrating superior capabilities in image editing, more users now rely on online servers for content manipulation via textual prompts rather than traditional offline tools. Despite servers attempting to prevent the proliferation of maliciously edited content via active defense like watermarking, this approach is not conducive to passive detection by third-party forensics. To address this limitation, we propose a plug-and-play controllable denoising termed Forensic-Friendly Image Manipulation (FFIM), which simultaneously satisfies user editing requirements while facilitating forensic analysis. Specifically, FFIM comprises three phases: Controllable Projection, Implicit Detection, and Explicit Guidance. Phase I enforces orthogonality between the variance of random noise and image features to ensure clear demarcation between the edited and unedited regions. Phase II implicitly evaluates whether this demarcation meets detection requirements; if not, Phase III explicitly introduces a surrogate detection model and adversarially adjusts the random noise to maximize the feature discrepancy between these regions. Experiments across four datasets demonstrate the superiority of FFIM over baseline methods, achieving up to +6.6% F1 in pixel-level localization and +27.3% AUC in image-level detection. Importantly, these forensic gains are attained without compromising visual quality, as evidenced by comparable manipulation in both subjective user studies and objective quality assessments. We envision that the proposed method will be widely adopted by generative AI service providers, enabling more comprehensive information authenticity from a passive defensive standpoint.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:92f5f0d7fdb101a7e1778544f3216531CVF · CVPR 2026

Seeing Conversations: Communication Context Identification in Egocentric Video

Dorszewski, Tobias, Hjortkjær, Jens

原文摘要

In everyday conversations, humans effortlessly recognize communication partners using visual cues such as gaze or head orientation. Replicating this social reasoning in computer vision is challenging, especially in dynamic, multi-person settings. We introduce Communication Context Identification (CCI) in egocentric vision: Given a first-person video sequence, determine which individuals are engaged in communication with the camera wearer. To support CCI, we collected a challenging large-scale dataset comprising 68.9 hours of egocentric video captured across diverse multi-person, multi-conversation scenarios. We propose CoCoNet, a temporal interaction model for CCI that tracks social dynamics via attention across individuals over long time scales. CoCoNet flexibly handles varying group sizes, maintains predictions through occlusions, and performs robustly even with limited temporal input. Leveraging long temporal contexts, it achieves 96% balanced accuracy on CCI. Performance varies with group size and spatial scene layout, highlighting the importance of dataset diversity. Our work advances vision-based conversational awareness, enabling applications in assistive hearing that use egocentric video to enhance individuals in the user's conversation group.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:930f9921a30d8441464ef53aea3f029cCVF · CVPR 2026

SATTC: Structure-Aware Label-Free Test-Time Calibration for Cross-Subject EEG-to-Image Retrieval

Huang, Qunjie, Zhu, Weina

原文摘要

Cross-subject EEG-to-image retrieval for visual decoding is challenged by subject shift and hubness in the embedding space, which distort similarity geometry and destabilize top-k rankings, making small-k shortlists unreliable. We introduce SATTC (Structure-Aware Test-Time Calibration), a label-free calibration head that operates directly on the similarity matrix of frozen EEG and image encoders. SATTC combines a geometric expert-subject-adaptive whitening of EEG embeddings with an adaptive variant of Cross-domain Similarity Local Scaling (CSLS)--and a structural expert built from mutual nearest neighbors, bidirectional top-k ranks, and class popularity, fused via a simple Product-of-Experts rule. On THINGS-EEG under a strict leave-one-subject-out protocol, standardized inference with cosine similarities, l2-normalized embeddings, and candidate whitening already yields a strong cross-subject baseline over the original ATM retrieval setup. Building on this baseline, SATTC further improves Top-1 and Top-5 accuracy, reduces hubness and per-class imbalance, and produces more reliable small-k shortlists. These gains transfer across multiple EEG encoders, supporting SATTC as an encoder-agnostic, label-free test-time calibration layer for cross-subject neural decoding. Code is available at https://github.com/QunjieHuang/SATTC-CVPR2026

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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首次观测 · 记录 #1CVF · CVPR 2026:cvf:931e664dfbd9c0dbe711efd93b50f57eCVF · CVPR 2026

Evolving Contextual Safety in Multi-Modal Large Language Models via Inference-Time Self-Reflective Memory

Zhang, Ce, He, Jinxi, He, Junyi, Sycara, Katia, Xie, Yaqi

原文摘要

Multi-modal Large Language Models (MLLMs) have achieved remarkable performance across a wide range of visual reasoning tasks, yet their vulnerability to safety risks remains a pressing concern. While prior research primarily focuses on jailbreak defenses that detect and refuse explicitly unsafe inputs, such approaches often overlook contextual safety, which requires models to distinguish subtle contextual differences between scenarios that may appear similar but diverge significantly in safety intent. In this work, we present MM-SafetyBench++, a carefully curated benchmark designed for contextual safety evaluation. Specifically, for each unsafe image-text pair, we construct a corresponding safe counterpart through minimal modifications that flip the user intent while preserving the underlying contextual meaning, enabling controlled evaluation of whether models can adapt their safety behaviors based on contextual understanding. Further, we introduce EchoSafe, a training-free framework that maintains a self-reflective memory bank to accumulate and retrieve safety insights from prior interactions. By integrating relevant past experiences into current prompts, EchoSafe enables context-aware reasoning and continual evolution of safety behavior during inference. Extensive experiments on various multi-modal safety benchmarks demonstrate that EchoSafe consistently achieves superior performance, establishing a strong baseline for advancing contextual safety in MLLMs. All benchmark data and code will be made publicly accessible upon acceptance.

cs.CV · cs.CL

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:93253ca96f6422578d392e8dbd790817CVF · CVPR 2026

Joint Spectral Image Reconstruction and Semantic Segmentation with Cooperative Unfolding

He, Zijun, Wang, Ping, Wang, Xiaodong, Chen, Chang, Yuan, Xin

原文摘要

Coded Aperture Snapshot Spectral Imaging (CASSI) is an emerging hyperspectral image (HSI) acquisition technique for downstream semantic segmentation. Due to the ill-posedness nature of CASSI systems, typical solutions are compelled to conduct a two-stage reconstruction-then-segmentation pipeline, namely viewing them as two separate tasks. However, we observe that such two tasks are interrelated and mutually reinforcing for representation learning, and thus separating them limits the overall accuracy and efficiency. To this end, we propose the first Cooperative Reconstruction-Segmentation Deep Unfolding Network (CRSDUN) to solve the reconstruction and segmentation tasks in parallel. To make the two mutually reinforcing, we introduce the Cross-Aggregated Super-Token Attention (CASTA) mechanism to enhance the representation interactions between HSI reconstruction and semantic segmentation. Extensive experiments on both synthetic and real-world HSI reconstruction-segmentation datasets demonstrate that our method achieves state-of-the-art in both spectral reconstruction and semantic segmentation. The code is available at \href https://github.com/zjhe02/CRSDUN https://github.com/zjhe02/CRSDUN .

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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首次观测 · 记录 #1CVF · CVPR 2026:cvf:9326269781c54fa8f53c2bb8e47080a5CVF · CVPR 2026

Is Bin Generation Indispensable? A Bin-Generation-Free Dataset Quantization via Semantic Perspective

Deng, Maijie, Li, Yuhua, Zou, Yixiong, Wu, Yao, Ma, Chenru

原文摘要

Dataset quantization has recently emerged as a promising solution for mitigating the computational and memory challenges of large-scale datasets. However, existing approaches rely on a bin generation step that is computationally expensive and inefficient for large-scale datasets. Moreover, a fixed drop ratio in its patch dropping step fails to adapt to the diverse redundancy levels across samples, which degrades the representational quality of the quantized coreset. To address these limitations, we present Bin-Generation-Free Dataset Quantization (BGFDQ), a fully restructured framework that incorporates a simple yet effective KNN-based neighbor identification and neighbor-aware coreset selection strategy. We theoretically demonstrate that the proposed selection strategy achieves superior sampling efficiency compared to bin-generation-based methods. Additionally, we introduce an adaptive patch dropping strategy to further enhance the quality of the quantized dataset. Extensive experiments on four image classification benchmarks show that BGFDQ consistently outperforms state-of-the-art baselines. In particular, we achieve up to 5% validation accuracy improvement on CIFAR-100. Moreover, our framework successfully scales to datasets containing up to 10^5 same-class samples while existing bin-generation-based approaches fail due to memory constraints. Code is available at https://github.com/MaijieDeng/BGFDQ.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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首次观测 · 记录 #1CVF · CVPR 2026:cvf:9334dbbc34231d750b16d80b2d28ee86CVF · CVPR 2026

ElasticFormer: Detecting Objects in HRW Shots via Elastic Computing Vision Transformer

Li, Wenxi, Huang, Jingchen, Lyu, Chenyang, Liu, Moran, Lin, Haozhe, Ding, Guiguang, Guo, Yuchen

原文摘要

Recent advances in gigapixel-level imaging have brought High-Resolution Wide shots to the forefront of research. However, these images present significant challenges: extreme sparsity of foreground, gigapixel-level resolutions and diverse target counts. This makes traditional close-up detectors inaccurate and slow as they are overwhelmed by the background. Although previous research has explored sparse backbones, their fixed sparsity patterns lack the adaptability required to handle diverse target numbers. To address this, we introduce ElasticFormer, a sparse backbone that dynamically allocates computational resources based on foreground proportion. After scoring windows based on variance, proposed ElasticSelector module will predict the foreground proportion for top-k selection. The mechanism guides the model to select target-containing windows, scaling resources in areas where objects are clustered. We introduce a novel loss function combined with the 3-phase training strategy for ElasticSelector, allowing it to function properly when bounding box annotations are missing. A WSOD study is carried on PASCAL VOC 2007 to evaluate its extensibility. Further, ElasticNet is created to verify its backbone-agnostic nature. In experiments on the PANDA gigapixel benchmark, ElasticFormer reduces backbone FLOPs by 80% while achieving a significant improvement in AP_ 50 when compared to fixed-ratio sparse methods.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:93372d618f38cac3a96e903535d5a105CVF · CVPR 2026

PP-Brep: Few-Shot B-rep Classification with Hybrid Graph Representation

Hao, Jiacheng, Liu, Chunying, Guo, Hao, Wang, Ruohan, Gan, Hongping, Shi, Yilei

原文摘要

In industrial settings, classification of 3D CAD models are critical for efficient manufacturing. However, the limited availability of annotated CAD models presents an obstacle to achieving rapid adaptation in few-shot part classification scenarios. In this paper, we propose a hybrid graph representation and a pre-training and graph prompt framework for B-rep few-shot classification. Specifically, hybrid graph representation captures comprehensive and multi-level structural information of B-rep models by constructing local topology graph, global parallel graph and regional association hypergraph. A hierarchical graph network then fuses component-level structures with topological details in the hybrid graph. Reinforcement-augmented contrastive pre-training produces robust universal representations while in-place perturbation reduces training time. Structure-aware graph prompts finally produce node-specific cues, enabling few-shot B-rep part classification without heavy fine-tuning. Experiments on the TraceParts-11and FabWave-31 datasets show that our method outperforms existing general-purpose approaches. This work provides an efficient and state-of-the-art solution for few-shot B-rep part classification.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:935d329c6c7c1947608189fb9bdc3cf3CVF · CVPR 2026

AdaBet: Gradient-free Layer Selection for Efficient Training of Deep Neural Networks

Tenison, Irene, Chatterjee, Soumyajit, Kawsar, Fahim, Malekzadeh, Mohammad

原文摘要

To utilize pre-trained neural networks on edge and mobile devices, we often require efficient adaptation to user-specific runtime data distributions while operating under limited compute and memory resources. On-device retraining with a target dataset can facilitate such adaptations; however, it remains impractical due to the increasing depth of modern neural nets, as well as the computational overhead associated with gradient-based optimization across all layers. Current approaches reduce training cost by selecting a subset of layers for retraining; however, they rely on labeled data, at least one full-model backpropagation, or server-side meta-training, limiting their suitability for constrained devices. We introduce AdaBet, a gradient-free layer selection approach to rank important layers, followed by important channels of these layers, by analyzing topological features of their activation spaces through Betti Numbers and using forward passes alone. AdaBet allows selecting layers and channels with high learning capacity, which are important for retraining and adaptation, without requiring labels or gradients. Evaluating AdaBet on sixteen pairs of benchmark models and datasets shows AdaBet achieves an average gain of 2.5% more classification accuracy over gradient-based baselines while reducing average peak memory consumption by 40%. We open-source our code at https://github.com/Nokia-Bell-Labs/efficient_layer_selection

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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首次观测 · 记录 #1CVF · CVPR 2026:cvf:9388f2d0bea144a58815b47899fccad6CVF · CVPR 2026

STAvatar: Soft Binding and Temporal Density Control for Monocular 3D Head Avatars Reconstruction

Zhao, Jiankuo, Zhu, Xiangyu, Wang, Zidu, Lei, Zhen

原文摘要

Reconstructing high-fidelity and animatable 3D head avatars from monocular videos remains a challenging yet essential task. Existing methods based on 3D Gaussian Splatting typically bind Gaussians to mesh triangles and model deformations solely via Linear Blend Skinning, which results in rigid motion and limited expressiveness. Moreover, they lack specialized strategies to handle frequently occluded regions (e.g., mouth interiors, eyelids). To address these limitations, we propose STAvatar, which consists of two key components: (1) a UV-Adaptive Soft Binding framework that leverages both image-based and geometric priors to learn per-Gaussian feature offsets within the UV space. This UV representation supports dynamic resampling, ensuring full compatibility with Adaptive Density Control (ADC) and enhanced adaptability to shape and textural variations. (2) a Temporal ADC strategy, which first clusters structurally similar frames to facilitate more targeted computation of the densification criterion. It further introduces a novel fused perceptual error as clone criterion to jointly capture geometric and textural discrepancies, encouraging densification in regions requiring finer details. Extensive experiments on four benchmark datasets demonstrate that STAvatar achieves state-of-the-art reconstruction performance, especially in capturing fine-grained details and reconstructing frequently occluded regions. Our project is available at https://github.com/JiankuoZhao/STAvatar.git.

cs.CV

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Pixels Don't Lie (But Your Detector Might): Bootstrapping MLLM-as-a-Judge for Trustworthy Deepfake Detection and Reasoning Supervision

Kuckreja, Kartik, Gupta, Parul, Khan, Muhammad Haris, Dhall, Abhinav

原文摘要

Deepfake detection models often generate natural-language explanations, yet their reasoning is frequently ungrounded in visual evidence, limiting reliability. Existing evaluations measure classification accuracy but overlook reasoning fidelity. We propose DeepfakeJudge, a framework for scalable reasoning supervision and evaluation, that integrates an out-of-distribution benchmark containing recent generative and editing forgeries, a human-annotated subset with visual reasoning labels, and a suite of evaluation models, that specialize in evaluating reasoning rationales without the need for explicit ground truth reasoning rationales. The Judge is optimized through a bootstrapped generator-evaluator process that scales human feedback into structured reasoning supervision and supports both pointwise and pairwise evaluation. On the proposed meta-evaluation benchmark, our reasoning-bootstrapped model achieves an accuracy of 96.2%, outperforming \texttt 30x larger baselines. The reasoning judge attains very high correlation with human ratings and 98.9% percent pairwise agreement on the human annotated meta-evaluation subset. These results establish reasoning fidelity as a quantifiable dimension of deepfake detection and demonstrate scalable supervision for interpretable deepfake reasoning. Our user study indicates that humans prefer reasonings generated by our framework 70% of the time, in faithfullness, groundedness and usefulness compared to other models and datasets. All of our datasets, models, and codebase will be open-sourced.

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WHU-MARS: A Multispectral Aerial-Ground Benchmark Towards Any-Scenario Person Re-Identification

Zhao, Yuxuan, Zhou, Zhongao, Yang, Bin, Li, He, Liang, Jian, Chen, Jun, Du, Bo, Ye, Mang

原文摘要

Recent person re-identification (ReID) leverages heterogeneous sensing with multiple modalities and viewpoints to improve robustness across diverse conditions. However, most approaches target predefined scenario pairs (e.g., visible-infrared or aerial-ground) and train separate task-specific models. In contrast, real-world systems often query against galleries that cover all scenarios, making such designs inefficient and complex to deploy. To bridge this gap, we introduce Any-Scenario ReID (AS-ReID): given a query from any (modality, viewpoint) scenario, a single model retrieves the same identity from a heterogeneous gallery spanning all scenarios. Progress toward AS-ReID is limited by two factors: (i) the lack of a real-world-aligned benchmark with broad scenario coverage, and (ii) the challenge of learning representations with strong intra-identity cohesion and inter-identity discrimination under diverse scenarios. To this end, we construct WHU-MARS, a multispectral aerial-ground benchmark with 2,337 identities and 434,620 images captured by RGB, near-infrared, and thermal infrared cameras on both ground and UAV platforms. WHU-MARS spans day-night, multiple seasons, and diverse weather, enabling AS-ReID as well as conventional ReID tasks. We further propose the Unified Alignment and Discrimination (UAD) framework. Progressive Center Alignment (ProCA) aggregates multi-view features into modality centers and then aligns them toward identity centers to reduce scenario bias. Global Prototype Discrimination (GPD) contrasts samples against global identity prototypes to enforce large-margin discrimination. Extensive experiments highlight the challenges of WHU-MARS and demonstrate the effectiveness of UAD on AS-ReID.

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Towards Intrinsic-Aware Monocular 3D Object Detection

Zhang, Zhihao, Kumar, Abhinav, Liu, Xiaoming

原文摘要

Monocular 3D object detection (Mono3D) aims to infer object locations and dimensions in 3D space from a single RGB image. Despite recent progress, existing methods remain highly sensitive to camera intrinsics and struggle to generalize across diverse settings, since intrinsic governs how 3D scenes are projected onto the image plane. We propose MonoIA, a unified intrinsic-aware framework that models and adapts to intrinsic variation through a language-grounded representation.The key insight is that intrinsic variation is not a numeric difference but a perceptual transformation that alters apparent scale, perspective, and spatial geometry. To capture this effect, MonoIA employs large language models and vision-language models to generate intrinsic embeddings that encode the visual and geometric implications of camera parameters.These embeddings are hierarchically integrated into the detection network via an Intrinsic Adaptation Module, allowing the model to modulate its feature representations according to camera-specific configurations and maintain consistent 3D detection across intrinsics.This shifts intrinsic modeling from numeric conditioning to semantic representation, enabling robust and unified perception across cameras.Extensive experiments show that MonoIA achieves new state-of-the-art results on standard benchmarks including KITTI, Waymo, and nuScenes (e.g., +1.18% on the KITTI leaderboard), and further improves performance under multi-dataset training (e.g., +4.46% on KITTI Val).

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Mind the Gap: Transferring Labels to Align Object Detection Datasets

Kennerley, Mikhail, Aviles-Rivero, Angelica I., Schönlieb, Carola-Bibiane, Tan, Robby T.

原文摘要

Combining multiple object detection datasets offers a path to improved model generalisation but is hindered by inconsistencies in class semantics and bounding box annotations. Some methods to address this assume shared label taxonomies and address only spatial inconsistencies; others require manual relabelling, or produce a unified label space, which may be unsuitable when a fixed target label space is required. We propose Label-Aligned Transfer (LAT), a label transfer framework that systematically projects annotations from diverse source datasets into the label space of a target dataset. LAT begins by training dataset-specific detectors to generate pseudo-labels, which are then combined with ground-truth annotations via a Privileged Proposal Generator (PPG) that replaces the region proposal network in two-stage detectors. To further refine region features and address pseudo-label noise, a Semantic Feature Fusion (SFF) module injects class-aware context and features from overlapping proposals using a confidence-weighted attention mechanism. This pipeline preserves dataset-specific annotation granularity while enabling many-to-one label space transfer across heterogeneous datasets, resulting in a semantically and spatially aligned representation suitable for training a downstream detector. LAT thus jointly addresses both class-level misalignments and bounding box inconsistencies without relying on shared label spaces or manual re-annotation. Across multiple benchmarks, LAT demonstrates consistent improvements in detection performance, achieving gains of up to +8.4 AP over baseline methods.

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GRPO-Guard: Mitigating Implicit Over-Optimization in Flow Matching via Regulated Clipping

Wang, Jing, Liang, Jiajun, Liu, Jie, Liu, Henglin, Liu, Gongye, Zheng, Jun, Pang, Wanyuan, Ma, Ao 等 13 位作者

原文摘要

Recently, GRPO-based reinforcement learning has shown remarkable progress in optimizing flow-matching models, effectively improving their alignment with task-specific rewards. Within these frameworks, the policy update relies on importance-ratio clipping to constrain overconfident positive and negative gradients. However, in practice, we observe a systematic shift in the importance-ratio distribution--its mean falls below 1 and its variance differs substantially across timesteps. This left-shifted and inconsistent distribution prevents positive-advantage samples from entering the clipped region, causing the mechanism to fail in constraining overconfident positive updates. As a result, the policy model inevitably enters an **implicit over-optimization stage**--while the proxy reward continues to increase, essential metrics such as image quality and text-prompt alignment deteriorate sharply, ultimately making the learned policy impractical for real-world use. To address this issue, we introduce **GRPO-Guard**, a simple yet effective enhancement to existing GRPO frameworks. Our method incorporates ratio normalization, which restores a balanced and step-consistent importance ratio, ensuring that PPO clipping properly constrains harmful updates across denoising timesteps. In addition, a gradient reweighting strategy equalizes policy gradients over noise conditions, preventing excessive updates from particular timestep regions. Together, these designs act as a regulated clipping mechanism, stabilizing optimization and substantially mitigating implicit over-optimization without relying on heavy KL regularization. Extensive experiments on multiple diffusion backbones (e.g., SD3.5M, Flux.1-dev) and diverse proxy tasks demonstrate that GRPO-Guard significantly reduces over-optimization while maintaining or even improving generation quality. We provide detailed demonstrations of the over-optimization process and corresponding visualizations in https://jingw193.github.io/GRPO-Guard/.

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LumiMotion: Improving Gaussian Relighting with Scene Dynamics

Kaleta, Joanna, Wójcik, Piotr, Marzol, Kacper, Trzcinski, Tomasz, Kania, Kacper, Kowalski, Marek

原文摘要

In 3D reconstruction, the problem of inverse rendering, namely recovering the illumination of the scene and the material properties, is fundamental. Existing Gaussian Splatting-based methods primarily target static scenes and often assume simplified or moderate lighting to avoid entan- gling shadows with surface appearance. This limits their ability to accurately separate lighting effects from mate- rial properties, particularly in real-world conditions. We address this limitation by leveraging dynamic elements-- regions of the scene that undergo motion--as a supervisory signal for inverse rendering. Motion reveals the same sur- faces under varying lighting conditions, providing stronger cues for disentangling material and illumination. This the- sis is supported by our experimental results which show we improve LPIPS by 23% for albedo estimation and by 15% for scene relighting relative to next-best baseline. To this end, we introduce LumiMotion, the first Gaussian-based approach that leverages dynamics for inverse rendering and operates in arbitrary dynamic scenes. Our method learns a dynamic 2D Gaussian Splatting representation that em- ploys a set of novel constraints which encourage the dy- namic regions of the scene to deform, while keeping static regions stable. As we demonstrate, this separation is crucial for correct optimization of the albedo. Finally, we release a new synthetic benchmark comprising five scenes under four lighting conditions, each in both static and dynamic variants, for the first time enabling systematic evaluation of inverse rendering methods in dynamic environments and challenging lighting.

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VisualAD: Language-Free Zero-Shot Anomaly Detection via Vision Transformer

Hou, Yanning, Li, Peiyuan, Liu, Zirui, Wang, Yitong, Ruan, Yanran, Qiu, Jianfeng, Xu, Ke

原文摘要

Zero-shot anomaly detection (ZSAD) requires detecting and localizing anomalies without access to target-class anomaly samples. Mainstream methods rely on vision-language models (VLMs) such as CLIP: they build hand-crafted or learned prompt sets for normal and abnormal semantics, then compute image-text similarities for open-set discrimination. While effective, this paradigm depends on a text encoder and cross-modal alignment, which can lead to training instability and parameter redundancy. This work revisits the necessity of the text branch in ZSAD and presents VisualAD, a purely visual framework built on Vision Transformers. We introduce two learnable tokens within a frozen backbone to directly encode normality and abnormality. Through multi-layer self-attention, these tokens interact with patch tokens, gradually acquiring high-level notions of normality and anomaly while guiding patches to highlight anomaly-related cues. Additionally, we incorporate a Spatial-Aware Cross-Attention (SCA) module and a lightweight Self-Alignment Function (SAF): SCA injects fine-grained spatial information into the tokens, and SAF recalibrates patch features before anomaly scoring. VisualAD achieves state-of-the-art performance on 13 zero-shot anomaly detection benchmarks spanning industrial and medical domains, and adapts seamlessly to pretrained vision backbones such as the CLIP image encoder and DINOv2. Our code is publicly available at https://github.com/7HHHHH/VisualAD.

cs.CV · cs.CL

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Reinforcement-Guided Synthetic Data Generation for Privacy-Sensitive Identity Recognition

Jia, Xuemei, Du, Jiawei, Wei, Hui, Chen, Jun, Zhou, Joey Tianyi, Wang, Zheng

原文摘要

High-fidelity generative models are increasingly needed in privacy-sensitive scenarios, where access to data is severely restricted due to regulatory and copyright constraints. This scarcity hampers model development--ironically, in settings where generative models are most needed to compensate for the lack of data. This creates a self-reinforcing challenge: limited data leads to poor generative models, which in turn fail to mitigate data scarcity. To break this cycle, we propose a reinforcement-guided synthetic data generation framework that adapts general-domain generative priors to privacy-sensitive identity recognition tasks. We first perform a cold-start adaptation to align a pretrained generator with the target domain, establishing semantic relevance and initial fidelity. Building on this foundation, we introduce a multi-objective reward that jointly optimizes semantic consistency, coverage diversity, and expression richness, guiding the generator to produce both realistic and task-effective samples. During downstream training, a dynamic sample selection mechanism further prioritizes high-utility synthetic samples, enabling adaptive data scaling and improved domain alignment. Extensive experiments on benchmark datasets demonstrate that our framework significantly improves both generation fidelity and classification accuracy, while also exhibiting strong generalization to novel categories in small-data regimes.

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StyleDoctor: Towards Specialist Reward Model for Style-centric Generation Tasks

He, Xilin, Xian, Xiaole, Yue, Xiangyu, Khan, Muhammad Haris

原文摘要

Style generation has made significant progress through diffusion models. Recent efforts have explored reinforcement learning with human-preference reward models to enhance diffusion models for general downstream applications. However, we identify a critical limitation: existing human-preference reward models struggle to effectively perceive image style, resulting in suboptimal performance after reinforcement fine-tuning. To address this, we first introduce a large-scale style reward modeling dataset comprising 400K paired samples spanning 1,000 diverse style categories, augmented with textual instructions and style reward annotations. We then propose StyleDoctor, a novel style perception reward model capable of jointly evaluating style consistency between paired images and style-text alignment. StyleDoctor outperforms existing style perception models in both style retrieval and generation tasks. Extensive quantitative and qualitative experiments demonstrate the superiority of StyleDoctor over competing approaches, showcasing its efficiency and versatility in style-conditioned generation. Dataset and code are available at StyleDoctor.

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Low-Rank Residual Diffusion Models

Tan, Junfu, Yuan, Jiang

原文摘要

Residual diffusion models have achieved remarkable progress in image restoration tasks. In near-domain transformations such as image deraining, however, we observe that the residuals exhibit an inherent low-rank structure. Motivated by this property, we propose the Low-Rank Residual Diffusion Model (LRDM), which performs diffusion within a compact low-rank residual subspace for efficient and structure-preserving restoration. We formalize this observation as the Low-Rank Residual Assumption and show that the variational lower bound becomes tighter when residuals lie in a low-rank space. Building on this insight, we introduce an Asymmetric Residual Diffusion Process that constrains the forward process in the low-rank domain while maintaining full-rank flexibility in the reverse process. To accommodate the varying complexity of residuals across diffusion timesteps, we further introduce an Adaptive Rank Selection mechanism that dynamically adjusts the rank during the diffusion process. Experiments on deraining, deblurring, and deshading benchmarks show that LRDM surpasses full-rank diffusion baselines and achieves state-of-the-art performance, validating the advantage of modeling diffusion in a low-rank residual space. Project Page: \urlstyle same \hypersetup allcolors=magenta https://github.com/JF-Tan/LRDM .

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PHANTOM: Physics-Infused Video Generation via Joint Modeling of Visual and Latent Physical Dynamics

Shen, Ying, Xiong, Jerry, Yu, Tianjiao, Lourentzou, Ismini

原文摘要

Recent advances in generative video modeling, driven by large-scale datasets and powerful architectures, have yielded remarkable visual realism. However, emerging evidence suggests that simply scaling data and model size does not endow these systems with an understanding of the underlying physical laws that govern real-world dynamics. Existing approaches often fail to capture or enforce such physical consistency, resulting in unrealistic motion and dynamics.In this work, we investigate whether integrating the inference of latent physical properties directly into the video generation process can equip models with the ability to produce physically plausible videos. To this end, we propose PHANTOM, a Physics-Infused Video Generation model that jointly models the visual content and latent physical dynamics. Conditioned on observed video frames and inferred physical states, PHANTOM jointly predicts latent physical dynamics and generates future video frames.PHANTOM leverages a physics-aware video representation that serves as an abstract yet informative embedding of the underlying physics, facilitating the joint prediction of physical dynamics alongside video content without requiring an explicit specification of a complex set of physical dynamics and properties. By integrating the inference of physical-aware video representation directly into the video generation process, PHANTOM produces video sequences that are both visually realistic and physically consistent.Quantitative and qualitative results on both standard video generation and physics-aware benchmarks demonstrate that PHANTOM not only outperforms existing methods in terms of adherence to physical dynamics but also delivers competitive perceptual fidelity.

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SHands: A Multi-View Dataset and Benchmark for Surgical Hand-Gesture and Error Recognition Toward Medical Training

Ma, Le, dos Santos, Thiago Freitas, Magnenat-Thalmann, Nadia, Wac, Katarzyna

原文摘要

In surgical training for medical students, proficiency development relies on expert-led skill assessment, which is costly, time-limited, difficult to scale, and its expertise remains confined to institutions with available specialists. Automated AI-based assessment offers a viable alternative, but progress is constrained by the lack of datasets containing realistic trainee errors and the multi-view variability needed to train robust computer vision approaches. To address this gap, we present Surgical-Hands (SHANDS), a large-scale multi-view video dataset for surgical hand-gesture and error recognition for medical training. SHANDS captures linear incision and suturing using five RGB cameras from complementary viewpoints, performed by 52 participants (20 experts and 32 trainees), each completing three standardized trials per procedure. The videos are annotated at the frame level with 15 gesture primitives and include a validated taxonomy of 8 trainee error types, enabling both gesture recognition and error detection. We further define standardized evaluation protocols for single-view, multi-view, and cross-view generalization, and benchmark state-of-the-art deep learning models on the dataset. SHANDS is publicly released to support the development of robust and scalable AI systems for surgical training grounded in clinically curated domain knowledge.

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