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首次观测 · 记录 #1CVF · CVPR 2026:cvf:06fa2bcba49641fdbe8a02b7fa2a47caCVF · CVPR 2026

Adaptive Capacity Autoregressive Visual Tracking

Lin, Tong, Bai, Yifan, Liang, Shiyi, Niu, Ruigang, Wei, Xing

原文摘要

We present ARTrack-AC, a new step in the autoregressive tracking paradigm that introduces adaptive capacity inference to achieve both temporal consistency and dynamic efficiency. While existing autoregressive trackers predict object states sequentially with fixed inference capacity, they fail to accommodate the fluctuating temporal difficulty of real videos. ARTrack-AC addresses this limitation by equipping the tracker with the ability to modulate its inference capacity over time. A diffusion-based difficulty estimator anticipates the stability of upcoming segments, guiding a controller to switch between an accurate (high-capacity) and an efficient (low-capacity) mode while maintaining autoregressive consistency. This system-level autoregression extends conventional sequence modeling beyond "what to predict" toward "how to predict," forming a self-regulated tracking process that aligns inference cost with temporal complexity. Despite its simplicity, ARTrack-AC achieves state-of-the-art accuracy-speed trade-off on major benchmarks--66.7% AUC on LaSOT and 47.5% AUC on LaSOText--running 2.9xfaster than its predecessor.

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:23b084e80b8b7475c8f1904b425cdf0aCVF · CVPR 2026

When Lines Meet Textures: Spatial-Frequency Aligned Diffusion Features for Cross-Sparsity Correspondence

Zhu, Mingrui, Wang, Fengzhi, Wei, Xin, Wang, Jun, Wang, Nannan, Gao, Xinbo

原文摘要

Establishing accurate correspondence between sparse line representations and rich textured imagery remains a formidable challenge. While diffusion features excel in semantic correspondence, they struggle to bridge the fundamental gap between abstract sketches and texture-rich photographs. We identify two critical disparities: spatial domain misalignment from structural abstraction differences, and frequency domain inconsistencies from texture density variations. Based on this analysis, we propose SFA-DIFT, a novel approach that learns spatial-frequency aligned diffusion features for robust cross-modal correspondence. Unlike previous methods focusing solely on spatial alignment, our key innovation performs dual-domain alignment by learning unified clean diffusion features while strategically aggregating low-frequency components in the frequency domain. This comprehensive spatial-frequency alignment enables equitable understanding between sparse abstractions and rich textures. To validate our approach, we extend the existing sketch-photo correspondence dataset (PSC6K) by generating multi-style textured imagery, creating MS-PSC6K, a comprehensive correspondence benchmark. Extensive experiments demonstrate that SFA-DIFT achieves state-of-the-art performance, delivering substantial improvements with an average of 0.87% on PCK@1, 2.20% on PCK@5, and 0.95% on PCK@10 over previous best methods, validating the effectiveness and robustness of our dual-domain alignment approach. Codes are available on https://github.com/Mofr77/SFA-DIFT.

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:259c370ba2217236461e1cb345537207CVF · CVPR 2026

Exploring Visual Pretraining for Learning Language Intelligence

Zhao, Zhonghan, Zhang, Yiming, Zhang, Wenwei, Zhao, Haiteng, Wei, Xingguang, Gao, Zhangwei, Liu, Kuikun, Gu, Yuzhe 等 17 位作者

原文摘要

While the most fundamental pretraining paradigm typically trains modality-specific models on their respective datasets, the Platonic Representation Hypothesis that representations eventually align across modalities as data and model scale suggests an intriguing possibility: large language models (LLMs) could be pretrained on visual corpora to reach parity with text-pretrained models, thereby expanding data sources to break the text-scaling bottlenecks, and leveraging richer visual cues for more comprehensive corpus understanding. This paper makes the first attempt to demonstrate the feasibility of this implication by introducing Masked Autoregressive Pretraining for Learning language intelligencE (MAPLE), a novel visual pretraining paradigm for LLMs that leverages raw document images to improve language intelligence. MAPLE is universal to integrate masked auto-regressive models with various LLM backbones, where the LLMs are incentivized to generate latent hypotheses for the masked regions based on the unmasked regions. We verify MAPLE in the domain of math reasoning with multiple LLM backbones and show that MAPLE consistently surpasses text-only pretraining relatively by at most 40.2% on average accuracy across four math reasoning benchmarks. Further analyses show that visually pretrained LLMs learn a shared latent space that aligns document visuals with text and exploits layout and structural cues, supporting visual pretraining as a feasible and scalable route to stronger language models.

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:305cb32933d807b6a99229fce930b32fCVF · CVPR 2026

Omni-AD: A Large-scale and Versatile Benchmark for Industrial Anomaly Detection

Shi, Dahu, He, Chengshen, Zhang, Shaochen, Qian, Bo, Quan, Xiaochen, Zhang, Wencong, Wei, Xing

原文摘要

Industrial Anomaly Detection (IAD) has attracted significant attention and witnessed rapid development. However, the advancement in this field is hindered by two key issues: the performance saturation of existing benchmarks, limiting discriminative evaluation of different IAD methods, and the absence of benchmarks tailored to assess recent multi-modal large language models (MLLMs) in anomaly detection. To this end, we present Omni-AD, a comprehensive IAD benchmark featuring: i) Large scale: The dataset consists of approximately 35K images (6xlarger than MVTec AD) with 150 product categories (10xlarger than MVTec AD) spanning 16 industrial sectors, delivering unprecedented category diversity and greater dataset scale compared with existing datasets. ii) Versatility: The benchmark supports both conventional unsupervised and emerging MLLM-based IAD evaluation protocols. The latter is achieved by defining three subtasks of progressive difficulty, with two structured as visual question answering (VQA) and one as visual grounding. iii) Challenge: Extensive experimental results of state-of-the-art methods reveal that the Omni-AD benchmark is more challenging than existing benchmarks, which can drive the future development of the IAD field.

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:67df0e8d39379cc4e5a67de217e91a22CVF · CVPR 2026

MICo-150K: A Comprehensive Dataset Advancing Multi-Image Composition

Wei, Xinyu, Cen, Kangrui, Wei, Hongyang, Guo, Zhen, Li, Bairui, Wang, Zeqing, Zhang, Jinrui, Zhang, Lei

原文摘要

In controllable image generation, synthesizing coherent and consistent images from multiple reference inputs, i.e., **Multi-Image Composition** (MICo), remains a challenging problem, partly hindered by the lack of high-quality training data.To bridge this gap, we conduct a systematic study of MICo, categorizing it into 7 representative tasks and curate a large-scale collection of high-quality source images and construct diverse MICo prompts.Leveraging powerful proprietary models, we synthesize a rich amount of balanced composite images, followed by human-in-the-loop filtering and refinement, resulting in **MICo-150K**, a comprehensive dataset for MICo with identity consistency.We further build a Decomposition-and-Recomposition (**De&Re**) subset, where 11K real-world complex images are decomposed into components and recomposed, enabling both real and synthetic compositions.To enable comprehensive evaluation, we construct **MICo-Bench** with 100 cases per task and 300 challenging De&Re cases, and further introduce a new metric, **Weighted-Ref-VIEScore**, specifically tailored for MICo evaluation.Finally, we fine-tune multiple models on**MICo-150K** and evaluate them on **MICo-Bench**. The results show that MICo-150K effectively equips models without MICo capability and further enhances those with existing skills.Notably, **Qwen-MICo**, fine-tuned from Qwen-Image-Edit, matches **Qwen-Image-2509** in 3-image composition while supporting arbitrary multi-image inputs beyond the latter's limitation.Our dataset and benchmark will be valuable resources for advancing MICo research.

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:69df2868aad0d749fac910dcadd07e2aCVF · CVPR 2026

Seeing Through the Noise: Improving Infrared Small Target Detection and Segmentation from Noise Suppression Perspective

Yuan, Maoxun, Meng, Duanni, Xi, Ziteng, Zhao, Tianyi, Zhao, Shiji, Dai, Yimian, Wei, Xingxing

原文摘要

Infrared small target detection and segmentation (IRSTDS) is a critical yet challenging task in defense and civilian applications, owing to the dim, shapeless appearance of targets and severe background clutter. Recent CNN-based methods have achieved promising target perception results, but they only focus on enhancing feature representation to offset the impact of noise, which results in the increased false alarm problem. In this paper, through analyzing the problem from the frequency domain, we pioneer in improving performance from noise suppression perspective and propose a novel noise-suppression feature pyramid network (NS-FPN), which integrates a low-frequency guided feature purification (LFP) module and a spiral-aware feature sampling (SFS) module into the original FPN structure. The LFP module suppresses the noise features by purifying high-frequency components to achieve feature enhancement devoid of noise interference, while the SFS module further adopts spiral sampling to fuse target-relevant features in feature fusion process. Our NS-FPN is designed to be lightweight yet effective and can be easily plugged into existing IRSTDS frameworks. Extensive experiments on the IRSTD-1k and NUAA-SIRST datasets demonstrate that our method significantly reduces false alarms and achieves superior performance on IRSTDS task.

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:81b734d84fd0a26529548caddf1cd4a1CVF · CVPR 2026

RxnCaption: Reformulating Reaction Diagram Parsing as Visual Prompt Guided Captioning

Song, Jiahe, Wang, Chuang, Jiang, Bowen, Wang, Yinfan, Zheng, Hao, Wei, Xingjian, Liu, Chengjin, Nie, Rui 等 16 位作者

原文摘要

Large-scale chemical reaction datasets are crucial for AI research in chemistry. However, existing chemical reaction data often exist as images within papers, making them not machine-readable and unusable for training machine learning models. In response to this challenge, we propose the RxnCaption framework for the task of chemical Reaction Diagram Parsing (RxnDP). Our framework reformulates the traditional coordinate prediction driven parsing process into an image captioning problem, which Large Vision Language Models (LVLMs) handle naturally. We introduce a strategy termed "BBox and Index as Visual Prompt" (BIVP), which uses our state-of-the-art molecular detector, MolYOLO, to pre-draw molecular bounding boxes and indices directly onto the input image. This turns the downstream parsing into a natural-language description problem. Extensive experiments show that the BIVP strategy significantly improves structural extraction quality while simplifying model design. We further construct the U-RxnDiagram-15k dataset, an order of magnitude larger than prior real-world literature benchmarks, with a balanced test subset across four layout archetypes. Experiments demonstrate that RxnCaption-VL achieves state-of-the-art performance on multiple metrics. We believe our method, dataset, and models will advance structured information extraction from chemical literature and catalyze broader AI applications in chemistry.

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:f036c6ca82d59c93ec0eb9ef8183c3b6CVF · CVPR 2026

Boosting Document Parsing Efficiency and Performance with Coarse-to-Fine Visual Processing

Cui, Cheng, Sun, Ting, Liang, Suyin, Gao, Tingquan, Zhang, Zelun, Liu, Jiaxuan, Wang, Xueqing, Zhou, Changda 等 18 位作者

原文摘要

Document parsing is a fine-grained task where image resolution significantly impacts performance. While advanced research leveraging vision-language models benefits from high-resolution input to boost model performance, this often leads to a quadratic increase in the number of vision tokens and significantly raises computational costs. We attribute this inefficiency to substantial visual regions redundancy in document images, like background. To tackle this, we propose PaddleOCR-VL, a novel coarse-to-fine architecture that focuses on semantically relevant regions while suppressing redundant ones, thereby improving both efficiency and performance. Specifically, we introduce a lightweight Valid Region Focus Module (VRFM) which leverages localization and contextual relationship prediction capabilities to identify valid vision tokens. Subsequently, we design and train a compact yet powerful 0.9B vision-language model (PaddleOCR-VL-0.9B) to perform detailed recognition, guided by VRFM outputs to avoid direct processing of the entire large image. Extensive experiments demonstrate that PaddleOCR-VL achieves state-of-the-art performance in both page-level parsing and element-level recognition. It significantly outperforms existing solutions, exhibits strong competitiveness against top-tier VLMs, and delivers fast inference while utilizing substantially fewer vision tokens and parameters, highlighting the effectiveness of targeted coarse-to-fine parsing for accurate and efficient document understanding. The source code and models are publicly available at https://github.com/PaddlePaddle/PaddleOCR.

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 ↗