cs.CV · 2026-08-04 · No. 74
Computer Vision and Pattern Recognition, 2026-08-04.
20 new papers in cs.CV. Titles, authors,
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01 — The papers
20 entries-
01
UEmbed: Unified Sparse and Dense Multimodal Embeddings
Tingyu Song, Mingxin Li, Yanzhao Zhang, Dingkun Long, Pengjun Xie, Zhijie Nie, Yilun Zhao, Shu Wu
cs.CV · cs.AI · cs.CL · cs.IR
Sparse retrieval underpins modern search systems, from web search to retrieval-augmented generation. Existing work has introduced Learned Sparse Retrieval (LSR) to push beyond exact lexical matching toward richer semantics. Yet LSR has so far remained tied to encoder-style bidirectional architectures, and its extension to multimodal settings still relies heavily on auxiliary cross-modal modules. To address these limitations, we introduce...
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02
DyFrDet: Towards Accurate Small Object Detection via Dynamic Frequency Suppression with Label Disambiguation
Zihan Yang, Yang Guo, Hongxing Zhang, Dan Lu, Siyuan Yao
cs.CV · cs.AI
Despite the remarkable progress over the past decades, accurately identifying small objects remains challenging because of their insufficient visual cues. Previous works typically attempt to construct discriminative representation of the small objects. However, the wide range frequency domain noises and label ambiguities have been greatly overlooked, which significantly hinders the accurate localization. To address these issues, we propose a...
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03
Action-grounded tissue affordance enables anticipatory auto-framing that lowers surgeon cognitive workload during laparoscopic surgery
Jiayu Gu, Yiwei Wang, Jie Zhang, Guojun Cao, Keshen Lyu, Song Zhou, Yimeng Chen, Haorui Wang, Qingmin Feng, Shenchao...
cs.CV · cs.AI
Computational attention models could help surgeons manage the visual demands of laparoscopy, but they require dense spatial labels that are difficult to obtain because surgical intent is highly specialized and tacit. Here, we introduce DiffeoAfford, an action-grounded tissue affordance framework that retrospectively derives visual attention supervision from completed surgical procedures. By combining diffeomorphism-constrained tissue tracking...
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04
Grounding Agentic VLMs with Dedicated Segmentation for Fine-Grained Vehicle Damage Assessment
Vishwajeet Shivaji Hogale, Anjali Pai, Nitya Ravi
cs.CV · cs.AI
Vision-language models (VLMs) are increasingly deployed as reasoning agents in real-world visual assessment pipelines, yet their spatial grounding remains unreliable for fine-grained, visually ambiguous targets. We study this gap in the context of automated vehicle damage assessment, where fine-grained defects such as scratches and hairline cracks occupy few pixels, produce weak gradient signal, and are easily confused with reflections and...
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05
GROVE: Growing and Reasoning over Temporally Stratified Memory from Streaming Video Experience
Sitong Gong, Caixin Kang, Tianyu Yan, Guo Chen, Bo Zheng, Kaipeng Zhang, Yunzhi Zhuge, Xiang Ruan, Huchuan Lu, Yifei Huang
cs.CV · cs.AI
A wearable assistant should both answer questions about its visual history and recognize when that history is useful to the present situation. Existing video-memory systems primarily support question-conditioned recall, whereas proactive assistants typically use separate memory and control mechanisms. We introduce GROVE, a training-free framework that supports both behaviors with one memory grown causally from a continuous video stream. GROVE...
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06
Context-Aware Mixture of Domain Experts for Bodily Expression of Emotion in the Wild
Mohammad Mahdi Dehshibi, David Masip
cs.CV · cs.AI
The same body posture can convey entirely different emotions depending on its surrounding context, yet most methods for recognising bodily emotions treat scene and object cues as auxiliary feature augmentations rather than as structured priors over the plausibility of emotions. We introduce the Context-Aware Mixture of Domain Experts (CA-MoDE) for bodily emotion recognition. CA-MoDE incorporates dedicated scene and object experts to generate...
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07
The Push-Forward Transform for Continuous and Robust Comparison of Dynamic Shapes
Roua Rouatbi, Juan-Esteban Suarez Cardona, Ivo F. Sbalzarini
cs.CV · cs.LG · math.NA
We introduce a mathematical framework for shape comparison based on mapping functions from the shape domain to a common reference domain. This Push-Forward Transform enables invariant and robust comparison of shapes, preserving intrinsic geometric information. Quantitatively comparing shapes and their temporal evolution is a fundamental challenge in image analysis. Meaningful shape comparison requires representations that are invariant to...
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08
Extended Field of View Analysis for VideoGAN-based Trajectory Generation
Annajoyce Mariani, Kira Maag, Hanno Gottschalk
cs.CV · cs.LG
Realistic and diverse trajectory generation is central to enabling higher levels of vehicle automation. While rule-based and classical learning-based methods may struggle to capture the complexity of traffic behavior, generative models have already demonstrated in other fields that they can handle a comparable level of complexity. In this paper, we build upon previous work on generative adversarial network (GAN)-based semantic bird's-eye-view...
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09
Open-Set Visual Text Forensics via Sparse-Constraint Rectified Flow
Jiangling Zhang, Shuxuan Gao, Zeyu Chen, Yichao Liu, Yu Zhou
cs.CV · cs.AI
Rapidly evolving Generative AI enables sophisticated visual text manipulations that increasingly evade current forensic detectors. Existing discriminative models often overfit specific forgery patterns, limiting their generalization to unseen, open-set attacks. To address this challenge, we propose a generative detector that localizes tampering by estimating the local restoration cost required to align a query image with authentic visual-text...
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10
HarMoE: Multi-Source Chest Radiograph Pretraining with Dataset-Disentangled Experts
Haozhe Luo, Ziyu Zhou, Shelley Zixin Shu, Mauricio Reyes
cs.CV · cs.AI
Recent vision-language models for chest X-ray understanding are largely built on image-report alignment and therefore rely heavily on MIMIC-CXR as the dominant pretraining source. While effective at scale, this paradigm underexplores an important alternative source of supervision: a range of existing multi-label classification datasets, which provide cleaner and more explicit disease signals than free-text reports, and can offer broader...
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11
PhyCheck: Fine-Grained Evidence-Grounded Dataset for Physical Law Understanding in Video-LLMs
Zhongjie Ba, Shengwang Xu, Peng Cheng, Jinyang Zou, Ting Yu, Zhibo Wang, Zhan Qin
cs.CV · cs.AI
Embodied intelligence and world models require video understanding systems to go beyond recognizing objects and actions and develop an understanding of physical regularities. However, despite their strong performance on general video understanding tasks, current video-language models still struggle to reliably determine whether an observed event conforms to specific physical laws. Existing benchmarks primarily assess the physical quality of...
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12
UniqueSplat: View-conditioned 3D Gaussian Splatting for Generalizable 3D Reconstruction
Haixu Song, Xiaoke Yang, Shengjun Zhang, Jiwen Lu, Yueqi Duan
cs.CV · cs.AI
In this paper, we propose UniqueSplat, a view-conditioned feed-forward 3D Gaussian Splatting model to reconstruct customized 3D radiance fields for each view query. Existing feed-forward methods such as pixelSplat and MVSplat aim to generate fixed Gaussians across all views of each scene by minimizing the error between rendered views and ground-truth images. However, such fixed Gaussians generally render images from all views and lack the...
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13
STEAM:ASpatio-TEmporal Alignment Mixture-of-Experts Model with Hierarchical Pre-training for EEG Decoding
Zhu Chen, Dingkun Liu, Yuheng Chen, Dongrui Wu
cs.CV · cs.LG
Brain-computer interfaces (BCIs) have been widely used in motor rehabilitation, disease diagnosis, and other neural engineering scenarios. However, conventional neural signal decoding algorithms often suffer from limited generalizability and high adaptation costs, motivating recent interest in BCI foundation models. Existing approaches still struggle to jointly achieve general transferability, accurate decoding, and efficient downstream...
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14
TBSG-Net: Temporal Bipartite Scene Graph Network for Fine-Grained Video Moment Retrieval
Ji Huang, Yongsheng Dai, Tianyu Ren, Barry Devereux, Hui Wang
cs.CV · cs.AI
Recent advances in proposal-free Video Moment Retrieval (VMR) have highlighted the effectiveness of Static Scene Graphs (SSGs). By modeling objects and their relations at the frame level, SSGs enrich retrieval-oriented video representations. However, integrating SSGs into VMR remains constrained by two inherent limitations: (1) Lack of Temporal Dynamics. SSGs fail to model how objects and their relationships evolve over time, leading to the...
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15
Déjà Cue: Localizing States in Object Histories via Vocabulary-Relative Coordinates
Haofan Cao, Zhichao You, Yunkai Yang, Liang Guo, Jie Wang, Chongshou Li
cs.CV · cs.LG · cs.MM
Tracking links observations of the same object through visual change, yet cannot by itself determine when the object is empty or filled, intact or cut. We formulate identity-conditioned state-moment retrieval: given a tracked-object history and alternative state descriptions, localize an interval in which each described state holds. Absolute image-text similarity scores descriptions independently; because every visible frame depicts the same...
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16
SPARE: Structural Parameter-Free Affinity Regularization for Flow Matching
Zong-Wei Hong, Jinglun Li, Shen Zhang, Yuhan Liu, Linze Li, Yao Tang
cs.CV · cs.AI
Denoising diffusion transformers achieve strong generation quality but converge slowly during training. Regularizing their internal representations has emerged as an effective accelerator, yet existing methods split into two families with complementary costs. Target-based methods strengthen representations by aligning them to external features, which requires an external encoder and a learnable projection head to bridge feature spaces....
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17
AdaThinkV: Adaptive Thinking for Token-Efficient Video Reasoning
Jingqi Tian, Haoji Zhang, Lin Chen, Hongbo Jin, Haonan Xu, Tianrui Zhu, Xingming Shui, Shilin Ma, Wenjing Yang, Yansong Tang
cs.CV · cs.AI
Chain-of-thought (CoT) reasoning can improve performance on difficult video questions but often wastes decoding tokens on simple ones. We study whether a video multimodal large language model can adapt its reasoning effort to each question. We propose AdaThinkV, an adaptive framework for video reasoning that learns whether to reason explicitly without offline difficulty labels, manually tuned confidence thresholds, or an external router....
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18
FAST-GS: Frequency Aware Space-time Gaussian Splatting for Photorealistic Dynamic Novel View Synthesis
Zhengyang Zhang, Ziyu Lu, PengCheng Li, Hongbo Duan, Yi Liu, Pengting Luo, Peiyu Zhuang, Xinghui Li, Shaohua Ma
cs.CV · cs.AI
4D Gaussian Splatting (4DGS) excels in dynamic 3D reconstruction and real-time novel view synthesis via efficient 4D Gaussian representations and parallelizable rendering. However, existing 4DGS approaches rely on a single polynomial to model motion, which limits performance in complex dynamic scenes where high-frequency motion components are prevalent, and fails to ensure long-term stability due to cumulative trajectory drift. To address...
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19
Recompute or Reuse? Diagnosing and Mitigating Textual Shortcuts in VLM Self-Reflection
Wenxiao Fan, Jingling Fu, Fang Li, Luohang Liu, Yu He, Lichen Ma, Zhiyang Yu, Weishan Bi, Junshi Huang, Yan Li, Gu...
cs.CV · cs.AI
Vision-language models (VLMs) are expected to revise their reasoning when visual evidence changes. Failures to do so are often attributed to insufficient visual attention or contextual inertia, leaving unclear what models reuse instead of recomputing from the current image. We show that evidence-bearing reasoning in a prior chain of thought (CoT) can form a textual shortcut that competes behaviorally with visual recomputation. Across 16 VLMs,...
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20
SpatioLM: Towards General Physical Spatial Intelligence in Vision-Language Models
Jing Wu, Jianhua Wu, Jiayi Guan, Jiahong Chen, Jinghui Lu, Hangjun Ye, Bingzhao Gao, Long Chen
cs.CV · cs.CL · cs.LG
Vision-Language Models (VLMs) perform well on commonsense reasoning tasks but struggle with visual spatial reasoning. Most existing solutions introduce extra 3D prior inputs or external spatial encoders, which increase complexity and degrade the underlying VLMs' general-purpose capabilities after spatial fine-tuning. To this end, we propose a parameter-efficient \textit{\textbf{Spatio}-vision \textbf{L}anguage \textbf{M}odels (SpatioLM)},...
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