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首次观测 · 记录 #1CVF · CVPR 2026:cvf:05f7f8f0cdd11364590e405a77f08cbbCVF · CVPR 2026

Seele: A Unified Acceleration Framework for Real-Time Gaussian Splatting on Mobile Devices

Zhu, He, Huang, Xiaotong, Liu, Zihan, Lin, Weikai, Liu, Xiaohong, He, Zhezhi, Leng, Jingwen, Guo, Minyi 等 9 位作者

原文摘要

3D Gaussian Splatting (3DGS) has become a crucial rendering technique for many real-time applications. How- ever, the limited hardware resources on today's mobile platforms hinder these applications, as they struggle to achieve real-time performance. In this paper, we propose SEELE, a general framework designed to accelerate the 3DGS pipeline for resource-constrained mobile devices. Specifically, we propose two GPU-oriented techniques: hybrid preprocessing and contribution-aware rasterization. Hybrid preprocessing alleviates the GPU compute and memory pressure by reducing the number of irrelevant Gaussians during rendering. The key is to combine our view-dependent scene representation with online filtering. Meanwhile, contribution-aware rasterization improves the GPU utilization at the rasterization stage by prioritizing Gaussians with high contributions while reducing computations for those with low contributions. Both techniques can be seamlessly integrated into existing 3DGS pipelines with minimal fine-tuning. Collectively, our framework achieves up to 6.3x speedup and 39.1% model reduction while achieving superior rendering quality compared to existing methods. Our codes will be released upon publication.

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: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:29d2ddd03c06342269d1448e5019a533CVF · CVPR 2026

TimeRipples: Accelerating vDiTs by Understanding the Spatio-Temporal Correlations in Latent Space

Mao, Wenxuan, Sun, Yulin, Chen, Aiyue, Lin, Jing, Yao, Yiwu, Gan, Yiming, Zhao, Jieru, Leng, Jingwen 等 10 位作者

原文摘要

The recent surge in video generation has shown the growing demand for high-quality video synthesis using large vision models. Existing video generation models are predominantly based on the video diffusion transformer (vDiT), however, they suffer from substantial inference delay due to self-attention. While prior studies have focused on reducing redundant computations in self-attention, they often overlook the inherent spatio-temporal correlations in video streams and directly leverage sparsity patterns from large language models to reduce attention computations. In this work, we take a principled approach to accelerate self-attention in vDiTs by leveraging the spatio-temporal correlations in the latent space. We show that the attention patterns within vDiT are primarily due to the dominant spatial and temporal correlations at the token channel level. Based on this insight, we propose a lightweight and adaptive reuse strategy that approximates attention computations by reusing partial attention scores of spatially or temporally correlated tokens along individual channels. We demonstrate that our method achieves significantly higher computational savings (85%) compared to state-of-the-art techniques over 4 vDiTs, while preserving almost identical video quality (<0.06% loss on VBench).

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:31427727023775ea9882d766f45f2bcaCVF · CVPR 2026

PosterIQ: A Design Perspective Benchmark for Poster Understanding and Generation

Feng, Yuheng, Zhang, Wen, Duan, Haodong, Zou, Xingxing

原文摘要

We present PosterIQ, a design-driven benchmark for poster understanding and generation, annotated across composition structure, typographic hierarchy, and semantic intent. It includes 7,765 image-annotation instances and 822 generation prompts spanning real, professional, and synthetic cases. To bridge visual design cognition and generative modeling, we define tasks for layout parsing, text-image correspondence, typography/readability and font perception, design quality assessment, and controllable, composition-aware generation with metaphor. We evaluate state-of-the-art MLLMs and diffusion-based generators, finding persistent gaps in visual hierarchy, typographic semantics, saliency control, and intention communication; commercial models lead on high-level reasoning but act as insensitive automatic raters, while generators render text well yet struggle with composition-aware synthesis. Extensive analyses show PosterIQ is both a quantitative benchmark and a diagnostic tool for design reasoning, offering reproducible, task-specific metrics. We aim to catalyze models' creativity and integrate human-centred design principles into generative vision-language systems.

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:470b5c4a27367c7694a469b99cee7133CVF · CVPR 2026

Spatial-Frequency Collaborative Learning for Occluded Visible-Infrared Person Re-Identification

Yu, Jian, Feng, Yujian, You, Shuai, Zhou, Zhongkai, Wu, Fei, Jing, Zhengjun, Ji, Yimu

原文摘要

Occluded visible-infrared person re-identification (Occluded VI-ReID) remains difficult due to modality heterogeneity and occlusions, both of which break structural consistency and weaken cross-modality feature alignment. Existing methods rely mainly on spatial-domain cues (such as local body parts and salient patches), but their discriminability degrades severely under varying imaging conditions or partial visibility. To address these issues, we introduce a spatial-frequency collaborative perspective that offers global perception and cross-location consistency. Specifically, we propose a Spatial-Frequency Collaborative Learning (SFCL) framework that uses frequency information to complement spatial representations. SFCL comprises a Cross-Modality Frequency Alignment Module (CFAM), a Spatial-Frequency Interaction Module (SFIM), and a Frequency-Aware Discriminative (FAD) loss. The CFAM models the spectral features of visible/infrared images in the frequency domain, establishing modality-consistent spectral priors. The SFIM injects these priors into spatial features, promoting dual-domain interaction and complementary representations of spatial and frequency semantics. In addition, the FAD loss jointly enforces cross-modality frequency alignment and semantic consistency, thus enhancing robustness and discriminability under occlusions. For real-occlusion evaluation, we construct two occluded datasets, Occ-SYSU-MM01 and Occ-RegDB, on which SFCL outperforms the state-of-the-art.

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:9b25111edc5535a6cdf716e9c0d9ceb0CVF · CVPR 2026

Bilevel Layer-Positioning LoRA for Real Image Dehazing

Zhang, Yan, Ma, Long, Feng, Yuxin, Huang, Zhe, Zhou, Fan, Su, Zhuo

原文摘要

Learning-based real image dehazing methods have achieved notable progress, yet they still face adaptation challenges in diverse real haze scenes. These challenges mainly stem from the lack of effective unsupervised mechanisms for unlabeled data and the heavy cost of full model fine-tuning. To address these challenges, we propose the haze-to-clear text-directed loss that leverages CLIP's cross-modal capabilities to reformulate real image dehazing as a semantic alignment problem in latent space, thereby providing explicit unsupervised cross-modal guidance in the absence of reference images. Furthermore, we introduce the Bilevel Layer-positioning LoRA (BiLaLoRA) strategy, which learns both the LoRA parameters and automatically search the injection layers, enabling targeted adaptation of critical network layers. Extensive experiments demonstrate our superiority against state-of-the-art methods on multiple real-world dehazing benchmarks.

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:aa98e5fdb63780af6f48a3f9f2db6ef8CVF · CVPR 2026

Reasoning-Driven Anomaly Detection and Localization with Image-Level Supervision

Jin, Yizhou, Feng, Yuezhu, Zhang, Jinjin, Wang, Peng, Liu, Qingjie, Wang, Yunhong

原文摘要

Multimodal large language models (MLLMs) have recently demonstrated remarkable reasoning and perceptual abilities for anomaly detection. However, most approaches remain confined to image-level anomaly detection and textual reasoning, while pixel-level localization still relies on external vision modules and dense annotations. In this work, we activate the intrinsic reasoning potential of MLLMs to perform anomaly detection, pixel-level localization, and interpretable reasoning solely from image-level supervision, without any auxiliary components or pixel-wise labels. Specifically, we propose Reasoning-Driven Anomaly Localization (ReAL), which extracts anomaly-related tokens from the autoregressive reasoning process and aggregates their attention responses to produce pixel-level anomaly maps. We further introduce a Consistency-Guided Reasoning Optimization (CGRO) module that leverages reinforcement learning to align reasoning tokens with visual attentions, resulting in more coherent reasoning and accurate anomaly localization. Extensive experiments on four public benchmarks demonstrate that our method significantly improves anomaly detection, localization, and interpretability. Remarkably, despite relying solely on image-level supervision, our approach achieves performance competitive with MLLM-based methods trained under dense pixel-level supervision. Code is available at https://github.com/YizhouJin313/ReADL.

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:af3988412428fe5e983d51601ac5aa16CVF · CVPR 2026

Focal-General Diffusion Model with Semantic Consistent Guidance for Sign Language Production

Yu, Yiheng, Liu, Sheng, Feng, Yuan, Jin, Zhelun, Jiang, Yining, Xu, Min

原文摘要

Sign Language Production (SLP) aims to translate spoken language into sign sequences, where the main challenge lies in generating coherent and natural poses from discrete glosses (G2P). Existing G2P methods typically treat each pose as an indivisible unit, limiting their ability to capture fine-grained joint-level dependencies and thus degrading pose quality. To address this, we propose the Focal-General Diffusion Model (FGDM), characterized by a pioneering two-stage denoising framework that harmonizes local joint-level dependencies and global coherence. Specifically, in the Focal stage, a novel Adaptive Sign GCN (ASGCN) adaptively models each pose based on contextual correlations, skeletal topology, and semantic conditions, ensuring precise generation of local details. In the General stage, a Transformer-based module refines the entire pose sequence to enhance global coherence and naturalness. Moreover, we introduce a Semantic Consistent Guidance (SCG) mechanism that seamlessly integrates semantic supervision into diffusion training, enforcing tighter alignment between generated pose sequences and their intended gloss semantics. Extensive experiments on PHOENIX14T and USTC-CSL demonstrate that FGDM achieves SOTA performance. Our project page is available at https://yuyiheng-eu.github.io/fgdm/.

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:b15cf50602fcaf6b31b58b5261e95a32CVF · CVPR 2026

Benchmarking Endoscopic Surgical Image Restoration and Beyond

Pei, Jialun, Guo, Diandian, Yang, Donghui, Li, Zhixi, Feng, Yuxin, Ma, Long, Du, Bo, Heng, Pheng-Ann

原文摘要

In endoscopic surgery, a clear and high-quality visual field is critical for surgeons to make accurate intraoperative decisions. However, persistent visual degradation, including smoke generated by energy devices, lens fogging from thermal gradients, and lens contamination due to blood or tissue fluid splashes during surgical procedures, severely impairs visual clarity. These degenerations can seriously hinder surgical workflow and pose risks to patient safety. To systematically investigate and address various forms of surgical scene degradation, we introduce a real-world open-source surgical image restoration dataset covering laparoscopic environments, called SurgClean, which involves multi-type image restoration tasks from two medical sites, i.e., desmoking, defogging, and desplashing. SurgClean comprises 3,113 images with diverse degradation types and corresponding paired reference labels. Based on SurgClean, we establish a standardized evaluation benchmark and provide performance for 22 representative generic task-specific image restoration approaches, including 12 generic and 10 task-specific image restoration approaches. Experimental results reveal substantial performance gaps relative to clinical requirements, highlighting a critical opportunity for algorithm advancements in intelligent surgical restoration. Furthermore, we explore the degradation discrepancies between surgical and natural scenes from structural perception and semantic understanding perspectives, providing fundamental insights for domain-specific image restoration research. Our work aims to empower restoration algorithms and improve the efficiency of clinical procedures. Sources can be available at: https://github.com/PJLallen/Surgical-Image-Restoration.

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:df7a595b37f59ae898361858147a9940CVF · CVPR 2026

ProSoftArena: Benchmarking Hierarchical Capabilities of Multi-modal Agents in Professional Software Environments

Ai, Jiaxin, Feng, Yukang, Zhang, Fanrui, Sun, Jianwen, Li, Zizhen, Li, Chuanhao, Chang, Yifan, Wu, Wenxiao 等 11 位作者

原文摘要

Multi-modal agents are making rapid progress on general computer-use tasks. However, existing benchmarks remain largely confined to web browsers and rudimentary applications, failing to capture the professional software workflows that dominate real-world scientific and industrial practices. To bridge this gap, we introduce ProSoftArena, a comprehensive benchmark and platform specifically designed for evaluating multi-modal agents in professional software environments. We establish the first five-level capability hierarchy for professional software manipulation, and curate a benchmark of 456 realistic tasks spanning 6 disciplines and 13 core professional applications. To ensure reliable assessment, we build an executable real-computer environment with an execution-based evaluation framework, and uniquely incorporate a human-in-the-loop evaluation paradigm to quantify agents' collaborative efficiency. Extensive experiments show that even the best-performing agent achieves only a 20.6% success rate on software-level tasks (L2) and completely fails on multi-software workflows (L3). Our in-depth analysis further provides valuable insights in current agent limitations and suggests effective design principles for building more capable agents in professional software settings. This project is available at: https://prosoftarena.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 ↗