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首次观测 · 记录 #1CVF · CVPR 2026:cvf:051c939fbf9b7209f3462c00d6a505c8CVF · CVPR 2026

Thinking in 360deg: Humanoid Visual Search in the Wild

Yu, Heyang, Han, Yinan, Zhang, Xiangyu, Yin, Baiqiao, Chang, Bowen, Han, Xiangyu, Liu, Xinhao, Zhang, Jing 等 12 位作者

原文摘要

Humans rely on the synergistic control of head (cephalomotor) and eye (oculomotor) to efficiently search for visual information in 360deg. However, prior approaches to visual search are limited to a static image, neglecting the physical embodiment and its interaction with the 3D world. How can we develop embodied visual search agents as efficient as humans while bypassing the constraints imposed by real-world hardware? To this end, we propose humanoid visual search where a humanoid agent actively rotates its head to search for objects or paths in an immersive world represented by a 360deg panoramic image. To study visual search in visually-crowded real-world scenarios, we build H* Bench, a new benchmark that moves beyond household scenes to challenging in-the-wild scenes that necessitate advanced visual-spatial reasoning capabilities, such as transportation hubs, large-scale retail spaces, urban streets, and public institutions. Our experiments first reveal that even top-tier proprietary models falter, achieving only 30% success in object and path search. We then use post-training techniques to enhance the open-source models, increasing its success rate by over threefold for both object search (14.83% - 47.38%) and path search (6.44% - 24.94%) on the smallest 3B model. Notably, the lower ceiling of path search reveals its inherent difficulty, which we attribute to the demand for sophisticated spatial commonsense. Our results not only show a promising path forward but also quantify the immense challenge that remains in building MLLM agents that can be seamlessly integrated into everyday human life.

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:38a8ea4a5ba106503f1b21319d015d8dCVF · CVPR 2026

PGA: Prior-free Generative Attack for Practical No-box Scenario

Peng, Hongyu, Yuan, Xiang, Cheng, Gong

原文摘要

The unrealistic reliance on abundant prior information in traditional transferable attacks has spurred the Practical No-box Scenario (PNS), where attackers can access only limited unlabeled images. However, existing methods rely on iterative optimization to produce adversarial examples with inherently limited inference speed and transferability. Conversely, faster generative attacks fundamentally conflict with the PNS due to their critical dependence on abundant prior information that is explicitly absent in this scenario. To bridge this gap, we propose Prior-free Generative Attack (PGA), the first generative attack tailored for the PNS. Specifically, we introduce the Curriculum-Guided Micro-Robust Optimization that progressively incorporates more challenging discriminative tasks to mitigate the degenerate solutions common in self-supervised learning with limited data, yielding robust and transferable surrogates for downstream attacks. Furthermore, the Region-Aware Consistent Perturbation Learning guides the generator to produce fine-grained and spatially coherent perturbations, mitigating the common pitfall of generative attacks falling into local optima under insufficient supervision. Extensive experiments demonstrate that our PGA achieves remarkable transferability across various settings with high inference speed. This work provides a more practical benchmark for future research on transferable attacks, revealing the great potential of generative attacks under the PNS.

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:84b6ec89c5bfd5541fd3a4e6478fe69eCVF · CVPR 2026

StreamRAG: Enhancing Real-Time Video Understanding with Retrieval Augmentation

Xie, Junlin, Zheng, Quanlong, Zhang, Ruifei, Wang, Kuo, Zhang, Yanhao, Luo, Jinguo, Lu, Haonan, Wan, Xiang 等 9 位作者

原文摘要

The transition of Retrieval-Augmented Generation (RAG) from offline video analysis to online, streaming scenarios presents a set of critical, unexplored challenges. These include the need for on-the-fly semantic segmentation of continuous video, the inherent tension between low-latency processing and high-quality knowledge extraction, and the demand for query-specific temporal reasoning. We propose StreamRAG, a novel framework designed to overcome these hurdles. StreamRAG is built upon three core technical pillars: (1) a Stream Event Segmentation (SES) module that performs real-time boundary detection to chunk the stream into meaningful units; (2) a Token-Reusing Accelerator that drastically cuts down captioning latency by leveraging computational overlap between consecutive frames; and (3) a Dynamic Retrieval Gate that modulates the retrieval scope and strategy based on the query's temporal sensitivity and contextual similarity. Empirical evaluation confirms that StreamRAG establishes a new state-of-the-art, delivering superior accuracy with minimal latency in streaming video comprehension.

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:90c7cead83d2d0adf2873a88f24197ccCVF · CVPR 2026

Scaling Spatial Intelligence with Multimodal Foundation Models

Cai, Zhongang, Wang, Ruisi, Gu, Chenyang, Pu, Fanyi, Xu, Junxiang, Wang, Yubo, Yin, Wanqi, Yang, Zhitao 等 28 位作者

原文摘要

Despite remarkable progress, multimodal foundation models still exhibit surprising deficiencies in spatial intelligence. In this work, we explore scaling up multimodal foundation models to cultivate spatial intelligence within the SenseNova-SI family, built upon established multimodal foundations including visual understanding models (i.e., Qwen3-VL and InternVL3) and unified understanding and generation models (i.e., Bagel). We take a principled approach to constructing high-performing and robust spatial intelligence by systematically curating SenseNova-SI-8M: eight million diverse data samples under a rigorous taxonomy of spatial capabilities. SenseNova-SI demonstrates unprecedented performance across a broad range of spatial intelligence benchmarks: 68.8% on VSI-Bench, 43.3% on MMSI, 85.7% on MindCube, 54.7% on ViewSpatial, 47.7% on SITE, 63.9% on BLINK, 55.5% on 3DSR, and 72.0% on EmbSpatial, while maintaining strong general multimodal understanding (e.g., 84.9% on MMBench-En). More importantly, we analyze the impact of data scaling, discuss early signs of emergent generalization capabilities enabled by diverse data training, analyze the risk of overfitting and language shortcuts, present a preliminary study on spatial chain-of-thought reasoning, and validate the potential downstream application. All newly trained multimodal foundation models are publicly released.

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:b353fef959e94b4b4f7e28a4c30f5b0eCVF · CVPR 2026

Phased DMD: Few-step Distribution Matching Distillation via Score Matching within Subintervals

Fan, Xiangyu, Qiu, Zesong, Wu, Zhuguanyu, Wang, Fanzhou, Lin, Zhiqian, Ren, Tianxiang, Lin, Dahua, Gong, Ruihao 等 9 位作者

原文摘要

Distribution Matching Distillation (DMD) distills score-based generative models into efficient one-step generators, without requiring a one-to-one correspondence with the sampling trajectories of their teachers. Yet, the limited capacity of one-step distilled models compromises generative diversity and degrades performance in complex generative tasks, e.g., generating intricate object motions in text-to-video task. Directly extending DMD to multi-step distillation increases memory usage and computational depth, leading to instability and reduced efficiency. While prior works propose stochastic gradient truncation as a potential solution, we observe that it substantially reduces the generative diversity in text-to-image generation and slows motion dynamics in video generation, reducing performance to the level of one-step models. To address these limitations, we propose Phased DMD, a multi-step distillation framework that bridges the idea of phase-wise distillation with Mixture-of-Experts (MoE), reducing learning difficulty while enhancing model capacity. Phased DMD incorporates two key ideas: progressive distribution matching and score matching within subintervals. First, our model divides the SNR range into subintervals, progressively refining the model to higher SNR levels, to better capture complex distributions. Next, to ensure accurate training within each subinterval, we derive rigorous mathematical formulations for the objective. We validate Phased DMD by distilling state-of-the-art image and video generation models, including Qwen-Image-20B and Wan2.2-28B. Experiments demonstrate that Phased DMD enhances motion dynamics, improves visual fidelity in video generation, and increases output diversity in image generation. Our code and models are available at https://x-niper.github.io/projects/Phased-DMD/.

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:d35a9d5da9dae269b50a34da4658ba3aCVF · CVPR 2026

Think with 3D: Geometric Imagination Grounded Spatial Reasoning from Limited Views

Chen, Zhangquan, Zhang, Manyuan, Yu, Xinlei, Luo, Xufang, Sun, Mingze, Pan, Zihao, An, Xiang, Feng, Yan 等 11 位作者

原文摘要

Though recent advances in vision-language models (VLMs) have achieved remarkable progress across a wide range of multimodal tasks, understanding 3D spatial relationships from limited views remains a significant challenge. Previous reasoning methods typically rely on pure text (e.g., topological cognitive maps) or on 2D visual cues. However, their limited representational capacity hinders performance in specific tasks that require 3D spatial imagination. To address this limitation, we propose 3DThinker, a framework that can effectively exploit the rich geometric information embedded within images while reasoning, like humans do. Our framework is the first to enable 3D mentaling during reasoning without any 3D prior input, and it does not rely on explicitly labeled 3D data for training. Specifically, our training consists of two stages. First, we perform supervised training to align the 3D latent generated by VLM while reasoning with that of a 3D foundation model (e.g., VGGT). Then, we optimize the entire reasoning trajectory solely based on outcome signals, thereby refining the underlying 3D mentaling. Extensive experiments across multiple benchmarks show that 3DThinker consistently outperforms strong baselines and offers a new perspective toward unifying 3D representations into multimodal reasoning. Our code is available at https://github.com/zhangquanchen/3DThinker.

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:e0aea2276fbecdd2b14660303fb361a4CVF · CVPR 2026

Towards Streaming Referring Video Segmentation via Large Language Model

Zhang, Wenkang, Yang, Kaicheng, An, Xiang, Li, Qiang, Feng, Ziyong, Yang, Wankou, Deng, Jiankang

原文摘要

Current referring video segmentation methods typically operate in an offline manner, where sparse frames are first selected for image-level referring segmentation, and the resulting masks are then propagated across the video. Although video sampling captures global context, its isolated processing steps not only complicate optimization but also restrict applicability to real-world streaming scenarios. In this paper, we propose a simple but efficient MLLM-based framework StreamingRVOS, which can extend image-level segmentation to video-level via a streaming pipeline without introducing extra parameters. Specifically, we employ a Semantic Embedding Recycling (SER) method to propagate temporal context across frames, enabling the model to perceive semantic representation in the video. Then, we propose an Online Mask Consistency Perception (OMCP) strategy to adaptively invoke the MLLM to re-perceive the current scene and regenerate the semantic embedding. We conduct extensive experiments on multiple downstream datasets to prove the effectiveness of StreamingRVOS. Compared to previous methods, our method achieves excellent performance in referring video segmentation (1B variant improves upon Sa2VA by 19.2 on the MeViS dataset), while operating at an average speed of 7 FPS under streaming inference on 1 xA800 GPU.

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 ↗