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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:57ea8c95cb4309a7f97d0347fff7f03cCVF · CVPR 2026

WEAVE: Unleashing and Benchmarking the In-context Interleaved Comprehension and Generation

Chow, Wei, Pan, Jiachun, Liang, Yongyuan, Zhou, Mingze, Song, Xue, Jia, Liyu, Zhang, Saining, Tang, Siliang 等 13 位作者

原文摘要

Recent unified multimodal models (UMMs) have achieved remarkable progress in visual comprehension and generation. However, existing datasets and benchmarks focus predominantly on single-turn interactions, overlooking the multi-turn, context-dependent nature of real-world image creation and editing. To bridge this gap, we introduce W E A V E, the first comprehensive suite for in-context interleaved cross-modality comprehension and generation, comprising two complementary components. W E A V E-100k is a large-scale dataset of 100,000 interleaved samples spanning over 370,000 dialogue turns and 500,000 images, encompassing comprehension, editing, and generation tasks that demand reasoning over prior context. W E A V E-Bench is a human-annotated benchmark of 100 items with 480 images, equipped with a hybrid VLM judge evaluation framework that jointly leverages reference images and original-image-instruction pairs to assess multi-turn generation, visual memory, and world-knowledge reasoning across diverse domains. Experiments show that training on W E A V E-100k substantially improves vision comprehension, image editing, and comprehension-generation collaboration, while further enabling the emergence of visual-memory capabilities in UMMs. Extensive evaluations on W E A V E-Bench reveal persistent limitations of current approaches in multi-turn, context-aware image generation and editing. We hope W E A V E provides both a perspective and a foundation for advancing in-context interleaved comprehension and generation in the multimodal 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:917e025b6fbb86bba8f3a744b0481e1fCVF · CVPR 2026

Modeling Cross-vision Synergy for Unified Large Vision Model

Wu, Shengqiong, Wu, Lanhu, Bao, Mingyang, Xu, Wenhao, Zhang, Hanwang, Yan, Shuicheng, Fei, Hao, Chua, Tat-Seng

原文摘要

Recent advances in large vision models (LVMs) have shifted from modality-specific designs toward unified architectures that jointly process images, videos, and 3D data. However, existing unified LVMs primarily pursue functional integration, while overlooking the deeper goal of cross-vision synergy: the ability to reason over complementary priors across visual modalities. To address this, we present PolyV, a unified LVM that achieves cross-vision synergy at both the architectural and training levels. Architecturally, PolyV adopts a sparse Mixture-of-Experts LVM coordinated by a dynamic modality router, allowing each expert to specialize in modality-specific priors while enabling bidirectional interaction and mutual refinement across modalities. Training-wise, a synergy-aware paradigm combines modality-specific pretraining with coarse-to-fine synergy tuning via knowledge distillation and object-/relation-level alignment. Extensive experiments on 10 benchmarks spanning image, video, and 3D understanding, including synergy-focused datasets requiring spatial or temporal priors, demonstrate that PolyV consistently outperforms existing models, achieving over 10% average improvement over its backbone. Overall, PolyV establishes a unified framework for synesthetic visual reasoning, advancing toward truly synergistic LVMs.

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:fb8cdbaa7e058817416ca1d370a151b0CVF · CVPR 2026

DeepAlign: Mitigating Modality Conflict through Modality-Specific Alignment

Li, Shuo, Miao, Bingchen, Bu, Wendong, Li, Juncheng, Zhang, Hanwang, Wu, Fei

原文摘要

Multimodal Large Language Models (MLLMs) have demonstrated promising advancements in augmenting the capabilities of LLMs to comprehend visual input. However, modality misalignment between vision and text remains a key challenge in MLLM, which can be attributed to two aspects: misalignment of modality-specific representations and depletion of modality-specific details. To address the issue of modality misalignment, we propose DeepAlign, a novel multimodal alignment framework to mitigate modality conflict, which employs representation intervention and structure-induced knowledge distillation to prevent the misalignment and depletion of modality-specific information. Extensive experiments demonstrate that DeepAlign significantly mitigates modality conflicts, leading to substantial performance improvements compared to backbone models across multiple vision-language tasks. It also stimulates some emergent abilities in MLLMs, such as multimodal in-context learning on interleaved text-image sequences.

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 ↗