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首次观测 · 记录 #1CVF · CVPR 2026:cvf:02e9be8c671e48c063bd551f85707f49CVF · CVPR 2026

Adapting Point Cloud Analysis via Multimodal Bayesian Distribution Learning

Zhu, Xingyu, Yi, Liang, Wang, Shuo, Zhu, Wenbo, Wu, Yongliang, Zhu, Beier, Zhang, Hanwang

原文摘要

Large multimodal 3D vision-language models show strong generalization across diverse 3D tasks, but their performance still degrades under domain shifts. This has motivated recent studies on test-time adaptation (TTA), which enables models to adapt online using test-time data. Among existing TTA methods, cache-based mechanisms are widely adopted to leverage previously observed samples for online prediction refinement. However, they store limited historical information, leading to progressive information loss as the test stream evolves. In addition, their prediction logits are fused heuristically, making adaptation unstable. To address these limitations, we propose BayesMM, a multimodal Bayesian distribution learning framework for test-time point cloud analysis. BayesMM models textual priors and streaming visual features of each class as Gaussian distributions. Textual parameters are derived from semantic prompts, while visual parameters are updated online with arriving samples. The two modalities are fused via Bayesian model averaging, which automatically adjusts their contributions based on posterior evidence. This yields a unified prediction that adapts to evolving test-time data without training. Extensive experiments on multiple point cloud benchmarks demonstrate that BayesMM maintains robustness under distributional shifts, yielding over 4 percent average improvement.

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:07d44555621779f80b6877f72408d1feCVF · CVPR 2026

Blink: Dynamic Visual Token Resolution for Enhanced Multimodal Understanding

Feng, Yuchen, Zhang, Zhenyu, Gu, Naibin, Chen, Yilong, Fu, Peng, Lin, Zheng, Wang, Shuohuan, Sun, Yu 等 11 位作者

原文摘要

Multimodal large language models (MLLMs) have achieved remarkable progress on various vision-language tasks, yet their visual perception remains limited. Humans, in comparison, perceive complex scenes efficiently by dynamically scanning and focusing on salient regions in a sequential "blink-like" process. Motivated by this strategy, we first investigate whether MLLMs exhibit similar behavior. Our pilot analysis reveals that MLLMs naturally attend to different visual regions across layers and that selectively allocating more computation to salient tokens can enhance visual perception. Building on this insight, we propose Blink, a dynamic visual token resolution framework that emulates the human-inspired process within a single forward pass. Specifically, Blink includes two modules: saliency-guided scanning and dynamic token resolution. It first estimates the saliency of visual tokens in each layer based on the attention map, and extends important tokens through a plug-and-play token super-resolution (TokenSR) module. In the next layer, it drops the extended tokens when they lose focus. This dynamic mechanism balances broad exploration and fine-grained focus, thereby enhancing visual perception adaptively and efficiently. Extensive experiments validate Blink, demonstrating its effectiveness in enhancing visual perception and multimodal understanding.

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:407209ea7f40aaa6bbd04e53c9dfde02CVF · CVPR 2026

Progress-Think: Semantic Progress Reasoning for Vision-Language Navigation

Wang, Shuo, Wang, Yucheng, Lian, Guoxin, Wang, Yongcai, Chen, Maiyue, Wang, Kaihui, Zhang, Bo, Su, Zhizhong 等 12 位作者

原文摘要

Vision-Language Navigation requires agents to act coherently over long horizons by understanding not only local visual context but also how far they have advanced within a multi-step instruction.However, recent Vision-Language-Action models focus on direct action prediction and earlier progress methods predict numeric achievements; both overlook the monotonic co-progression property of the observation and instruction sequences.Building on this insight, Progress-Think introduces semantic progress reasoning, predicting instruction-style progress from visual observations to enable more accurate navigation.To achieve this without annotations, we propose a three-stage framework.In the initial stage, Self-Aligned Progress Pretraining bootstraps a reasoning module via a novel differentiable alignment between visual history and instruction prefixes.Then, Progress-Guided Policy Pretraining injects learned progress states into the navigation context, guiding the policy toward consistent actions.Finally, Progress-Policy Co-Finetuning jointly optimizes both modules with tailored progress-aware reinforcement objectives.Experiments on R2R-CE and RxR-CE show substantial gains in success, efficiency, and interpretability, demonstrating semantic progress provides a more consistent and generalizable representation of navigation advancement.

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:4ad8e048c395fe9efb71046bab42263bCVF · CVPR 2026

History to Future: Evolving Agent with Experience and Thought for Zero-shot Vision-and-Language Navigation

Dai, Guangzhao, Wang, Shuo, Wang, Zihan, Xie, Guo-Sen, Yang, Yang, Pan, Jinshan, Sun, Qianru, Shu, Xiangbo

原文摘要

Vision-and-Language Navigation in Continuous Environment (VLN-CE) requires an agent to follow language instructions to navigate the target destination. With the advancement of large language models (LLMs), recent efforts have explored adapting them for zero-shot VLN-CE, offering a promising solution in addressing the drawbacks of poor generalization in the training-based paradigm. However, existing LLM-based works primarily perform naive reasoning for decision-making and lack feedback, e.g., reviewing historical errors and predicting future potentials. Consequently, it may suffer from continuous failure for those initial error tasks. In this paper, we rethink LLM-based zero-shot VLN-CE and propose a new paradigm, named EvoNav, to improve the agent's decision-making with future thought and history experience via Future Chain-of-Thought (F-CoT) and History Chain-of-Experience (H-CoE). F-CoT predicts future actions and landmarks as thoughts to assist navigation progress estimation and direction selection, while H-CoE summarizes historical trajectories and scenes as experience to improve navigation decision reliability. Both F-CoT and H-CoE cooperatively evolve the agent's decision-making. Extensive experiments in both the simulator and real-world environments demonstrate the effectiveness of our EvoNav.

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:6e3352faff4e459ff18779348a1df2ddCVF · CVPR 2026

Adaptive Action Chunking at Inference-time for Vision-Language-Action Models

Liang, Yuanchang, Wang, Xiaobo, Wang, Kai, Wang, Shuo, Peng, Xiaojiang, Chen, Haoyu, Chua, David Kim Huat, Vadakkepat, Prahlad

原文摘要

In Vision-Language-Action (VLA) models, action chunking (i.e., executing a sequence of actions without intermediate replanning) is a key technique to improve robotic manipulation abilities. However, a large chunk size reduces the model's responsiveness to new information, while a small one increases the likelihood of mode-jumping, jerky behavior resulting from discontinuities between chunks. Therefore, selecting the optimal chunk size is an urgent demand to balance the model's reactivity and consistency. Unfortunately, a dominant trend in current VLA models is an empirical fixed chunk length at inference-time, hindering their superiority and scalability across diverse manipulation tasks. To address this issue, we propose a novel Adaptive Action Chunking (AAC) strategy, which exploits action entropy as the cue to adaptively determine the chunk size based on current predictions. Extensive experiments on a wide range of simulated and real-world robotic manipulation tasks have demonstrated that our approach substantially improves performance over the state-of-the-art alternatives. The videos and source code are publicly available at https://lance-lot.github.io/adaptive-chunking.github.io.

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:addcc910eb79f97e1053d4c8f572b767CVF · CVPR 2026

Principled Steering via Null-space Projection for Jailbreak Defense in Vision-Language Models

Zhu, Xingyu, Zhu, Beier, Wang, Shuo, Fang, Junfeng, Zhao, Kesen, Zhang, Hanwang, He, Xiangnan

原文摘要

As vision-language models (VLMs) are increasingly deployed in open-world scenarios, they can be easily induced by visual jailbreak attacks to generate harmful content, posing serious risks to model safety and trustworthy usage.Recent activation steering methods inject directional vectors into model activations during inference to induce refusal behaviors and have demonstrated effectiveness.However, a steering vector may both enhance refusal ability and cause over-refusal, thereby degrading model performance on benign inputs.Moreover, due to the lack of theoretical interpretability, these methods still suffer from limited robustness and effectiveness.To better balance safety and utility, we propose \texttt NullSteer , a null-space projected activation defense framework.Our method constructs refusal directions within model activations through a linear transformation: it maintains zero perturbation within the benign subspace while dynamically inducing refusal along potentially harmful directions, thereby theoretically achieving safety enhancement without impairing the model's general capabilities.Extensive experiments show that \texttt NullSteer significantly reduces harmful outputs under various jailbreak attacks (average ASR reduction over 15% on MiniGPT-4) while maintaining comparable performance to the original model on general benchmarks.

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:ca4c8ab0ba4bc6b891913a811ea37e4fCVF · CVPR 2026

Local Motion Matters: A Deconstruct-Recompose Paradigm for Reinforcement Learning Pre-training from Videos

Wang, Jinwen, Lin, Youfang, Hu, Xiaobo, Wang, Shuo, Lv, Kai

原文摘要

Pre-training on large-scale videos to improve reinforcement learning efficiency is promising yet remains challenging. Existing methods typically treat the agent as an indivisible entity, modeling motion patterns globally. Such global modeling is tightly coupled with the morphology, hindering transfer across domains. In contrast, despite the vast disparity in global motions, the local components exhibit similar motion patterns across different agents. Building on this insight, we propose a novel Deconstruct-Recompose Paradigm (DRP) for learning transferable local motion representations. Specifically, in the Deconstruct phase, we identify multiple local points and track their frame-wise motions, defining each as an Atomic Action. We introduce a Dual-Attention Encoder (DAE) to learn local motion representations from these Atomic Actions, capturing their spatiotemporal relationships. In the Recompose phase, we compose local motion representations with a learnable Motion Aggregation Token '[MAT]' via latent dynamics model learning. Additionally, an adapter bridges local motion and downstream action-specific dynamics to accelerate policy learning. Extensive experiments demonstrate that our method effectively transfers to diverse robotic control and manipulation tasks, significantly improving sample efficiency and performance.

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:d17c5bea1556e3e01d92f5ed5f22787bCVF · CVPR 2026

Learning to Generate via Understanding: Understanding-Driven Intrinsic Rewarding for Unified Multimodal Models

Pan, Jiadong, Li, Liang, Peng, Yuxin, Tang, Yu-Ming, Wang, Shuohuan, Sun, Yu, Wu, Hua, Huang, Qingming 等 9 位作者

原文摘要

Recently, unified multimodal models (UMMs) have made remarkable progress in integrating visual understanding and generation, demonstrating strong potential for complex text-to-image (T2I) tasks. Despite their theoretical promise, a persistent capability gap exists: UMMs typically exhibit superior visual understanding but comparatively weaker generative capabilities. This discrepancy arises largely from the intrinsic decoupling between the understanding and generation processes. While a UMM can accurately interpret fine-grained visual details, it often struggles to produce semantically coherent images from complex textual prompts. To address this challenge, we explore UMMs' internal understanding capability to enhance generation quality. We propose a token-level intrinsic text-image alignment reward mechanism, GvU, enabling the UMM to act simultaneously as teacher and student: it evaluates its own outputs using the understanding branch to guide the generations accordingly. Building upon this, we design a self-supervised reinforcement learning framework, allowing UMMs to iteratively improve their generation quality through understanding-based intrinsic reward signals--without reliance on external supervision. Experimental results show that our method substantially boosts UMMs' generation, which in turn strengthens their fine-grained visual understanding, narrowing the capability gap between UMMs' visual understanding and generation. The project page is https://matrix0721.github.io/gvu.github.io/.

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:eef48dca705bbf164f499a9abb744f87CVF · CVPR 2026

OntoAug: Rethinking Generative Data Augmentation via Ontology Guidance

Wang, Shuo, Wang, Zhichuan, Luo, Jun

原文摘要

Generative data augmentation has created new opportunities for improving image recognition models. Despite these advances, most existing augmentation methods process images holistically, without accounting for the uneven distribution of discriminative information in classification tasks, where foreground subjects typically contain richer category-relevant signals than background context. Ignoring this imbalance can introduce unintended semantic shifts in generated samples, thereby weakening the model's ability to capture the intrinsic ontology of the target object. In fact, human perception operates in a similar manner: subject identity remains stable, while contextual variations are naturally tolerated as long as overall coherence is preserved. Inspired by this observation, we propose OntoAug, an ontology-oriented data augmentation framework that explicitly distinguishes between subject and environment. OntoAug separates foreground and background through structured layout control and guides diffusion models to generate samples with consistent subjects and diverse contextual variations. By explicitly introducing foreground masks and diverse background prompts during the generation process, the framework enhances both semantic fidelity and diversity in the synthesized data. Extensive experiments show that OntoAug achieves strong performance across multiple tasks, including image classification, few-shot learning, weakly supervised object localization (WSOL), and large vision-language model 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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CVF · CVPR 2026 ↗PDF ↗