cs.CV · 2026-09-09 · No. 110
Computer Vision and Pattern Recognition, 2026-09-09.
19 new papers in cs.CV. Titles, authors,
abstracts. Links to arXiv. Want this in your inbox every morning? Subscribe →
01 — The papers
19 entries-
01
Canonical Color as a Lens into Concept Decodability in Vision Encoders and VLMs
Xiaofu Chen, Stella Frank, Yova Kementchedjhieva
cs.CV · cs.AI
Visual encoders construct a representation of the image input for Vision-Language models. How much conceptual, as opposed to immediately visible, information does this representation contain? We use canonical color as a controlled test case to ask whether vision encoders make canonical-color information linearly accessible, even when color is removed from the input image. We construct a dataset of objects with canonical colors, and probe...
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02
GoDeep: Annotation-Free Open-Vocabulary 3D Scene Understanding via Language-Space Lifting
Thodoris Betsas, Anastasios Doulamis, Andreas Georgopoulos
cs.CV · cs.AI
Open vocabulary 3D semantic segmentation methods typically lift CLIP features into 3D. This embeds points in a joint vision-language space known to behave like a bag-of-words on compositional tasks. Furthermore, even annotation free variants often require a large 3D training corpus and a dedicated 3D encoder per domain. Instead we use a vision-language model purely as a translator. It produces structured, entity-level descriptions of each...
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03
Hi-FLoop: Hierarchical State-Feedback Loops for Multi-Timescale World Modeling
Rx Fan, Zhan H
cs.CV · cs.AI
Multi-agent traffic simulation seeks diverse, coordinated, and physically realistic futures from maps and observed history. Long-horizon closed-loop generation must reconcile multiple decision time scales while its context evolves with generated states. Existing methods often unfold long futures from the initial scene and resolve intent, interaction, and motion monolithically, weakening cross-scale consistency and adaptation. We present...
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04
Kairos: A Dataset for Fine-Grained Video-Language Modeling over Space, Time, and Dynamics
Ruibo Ming, Lei Sun, Deheng Zhang, He Zhang, Jialu Li, Jian Wang, Zhendong Li, Mengshun Hu, Danda Pani Paudel, Luc...
cs.CV · cs.AI
Many emerging video language modeling tasks require systems to move beyond clip-level abstraction and model visual content as it unfolds over extended time horizons. However, most existing video datasets rely on coarse or sparsely aligned supervision, which compresses temporal variation and limits the ability of models to learn reusable representations of continuous visual dynamics. We introduce Kairos, a video dataset for video-language...
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05
CausalChapter: Improving Long-Video Chaptering with Interventional Dependency Modeling
Xinran Duan, Guozhang Li, Yaoyao Zhong, Mei Wang, Lizhi Wang, Hua Huang
cs.CV · cs.AI
Long-form instructional videos require automatic chaptering to support browsing, navigation, and knowledge access. Recent long-context language models can perform chaptering from textualized video inputs, but they remain costly and brittle for content-dense lecture videos with long transcripts, smooth topic transitions, and detailed chapter outputs. A scalable segment-then-caption paradigm reduces this cost, but introduces two new challenges:...
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06
TriCCOT: Tri-part Convolutional Conformal Transformer for Onboard Space Object Detection
Adrien Dorise, Marjorie Bellizzi, Julia Cohen, Stéphane May
cs.CV · cs.AI
Onboard object detection in Earth observation is constrained by limited computational resources and the absence of fully corrected imagery. While convolutional detectors are hardware-efficient, they often struggle to extract robust representations from raw and noisy data. Conversely, transformer-based models provide stronger global reasoning capabilities but remain difficult to deploy on FPGA accelerators due to quadratic attention complexity...
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07
Charts Are Beyond Pixels: Probing for Layer-Wise Chart Understanding and Editing
Xiaochuan Zhong, Yifan Hou, Chenxi Pang, Shaobo Cui
cs.CV · cs.LG
Charts are structured visual compositions whose elements have distinct functional roles, semantic correspondences, and visibility relations. This structural view motivates evaluating whether models can understand and manipulate charts at the layer level. Existing chart benchmarks, however, primarily assess the correctness or fidelity of final outputs and do not directly evaluate these layer-wise behaviors. We present LayerWiseBench, a...
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08
From Where to How: Continuous 4D Interaction Forecasting from Egocentric Video
Qiaohui Chu, Haoyu Zhang, Meng Liu, Haoxiang Shi, Dongmei Jiang, Liqiang Nie
cs.CV · cs.AI
Egocentric 4D interaction forecasting aims to anticipate both where future interactions will occur in 3D and how the human body will move to realize them, providing an important capability for assistive robotics and human-computer interaction. Existing methods struggle to translate semantic understanding into precise continuous 3D localization and to balance motion diversity with structural consistency in pose forecasting. More fundamentally,...
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09
SynthRCT: Scalable Conditional Deformation Synthesis for Synthetic Repeat CT Generation
Tomas Guija-Valiente, Blanca Rodriguez-Gonzalez, Norberto Malpica
cs.CV · cs.AI · cs.LG
In proton therapy, plans are typically optimized on a single planning CT, making robustness evaluation essential under anatomical changes. However, current scenarios often rely on simplified perturbations that poorly capture complex, patient-specific variability. We propose SynthRCT, a scalable conditional generative framework for 3D anatomical deformation synthesis. Based on a conditional variational autoencoder, SynthRCT learns a latent...
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10
Temporal State Transport in Video Generation: Diagnosing and Correcting Spectral Imbalance
Luyao Tang, Bingjun Luo, Dong Yi, Jialin Guo, Haoning Xi, Cheng Chen, Yizhou Yu, Chaoqi Chen
cs.CV · cs.LG
Reliable video generation requires more than high-quality frames to form a coherent story: a model must maintain a persistent state, transporting visual attributes such as identity, scene layout, motion, and fine details across time. Existing training-free methods mainly strengthen cross-frame attention or analyze local attention entropy, but these views do not reveal whether temporal interactions stay in a healthy transport regime. In this...
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11
AirAnchor: Bridging Local and Global Spatial Information for Zero-Shot Aerial Vision-and-Language Navigation
Shanwei Fan, Bin Zhang, Zhiwei Xu, Yingxuan Teng, Siqi Dai, Lin Cheng, Guoliang Fan
cs.CV · cs.AI · cs.RO
Aerial Vision-and-Language Navigation requires drones to follow natural-language instructions and navigate through complex urban environments. Accurate navigation relies on both local and global spatial information, which support immediate action grounding and long-horizon path planning, respectively. However, existing zero-shot methods typically operate at a single spatial scale, relying either on local representations constructed online...
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12
Towards Embodied Air-Ground Cooperative Object Search: Benchmark, Dataset and Agentic Method
Boao Yu, Zimo Chen, Junreng Rao, Yue Hu, Zhengqiu Zhu, Yong Zhao, Rusheng Ju
cs.CV · cs.AI
Air-Ground Object Search (AGOS) in urban environments is a challenging embodied task, which requires an Unmanned Aerial Vehicle (UAV) and an Unmanned Ground Vehicle (UGV) to jointly search for and verify a specified target vehicle from multi-view visual references. To study this underexplored problem, we introduce AGOS-Bench, the first dedicated benchmark for evaluating whether general-purpose Vision-Language Models (VLMs) can integrate...
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13
To Adapt or Not to Adapt? Selective Adaptation for Vision-Language Models
Siru Jiang, Yuwei Liang, Jian Liang, Ran He, Tieniu Tan
cs.CV · cs.LG
Test-time adaptation (TTA) has emerged as a prominent strategy for adapting vision-language models to distribution shifts during inference. We conduct a per-sample analysis of model predictions before and after adaptation, and observe two failure modes in existing TTA methods that echo previous work. Adaptations are frequently negligible, yielding no change in the model's predictions, and more severely, they can be detrimental by flipping...
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14
Segment Any Motion with Radar: Robust Multimodal Moving-Object Segmentation and Tracking
Jue Wang, Xuan Wang, Hao Zhou, Ruixiang Zhou, Yixuan Zhou, Tianshuo Yuan, Jieming Ma, Jie Zhang, Fei Luo
cs.CV · cs.AI
Moving-object perception must decide which image regions correspond to real motion and keep every instance identified over time. Methods that read motion from appearance, optical flow, or estimated trajectories lose that evidence under poor illumination, adverse weather, reflections, and occlusion. Radar is a natural remedy because it measures radial velocity directly instead of inferring it from photometric correspondence. However, existing...
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15
CoVeR: Coverage-Based Token Pruning for Multi-View 3D Reasoning in VLMs
Nhat-Tan Bui, Varshini Elangovan, Arun Reddy Anugu, Sreyas Mohan, Wei Ye, Dilin Wang, JQ Huang, Rakesh Ranjan,...
cs.CV · cs.LG
Representing a 3D scene as multi-view images allows 2D VLMs to reason in 3D by reusing priors from pre-training, sidestepping the scarcity of annotated 3D data. However, it produces thousands of redundant visual tokens whose cost grows with every view. Existing visual token pruners fall into two families, each limited in the 3D multi-view setting. Learned importance methods rank tokens by attention or encoder features; because redundancy here...
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16
FPicker: Topology-Guided Evolution for Filament Tracing in Low-SNR Microscopy
Tingyin Zhao, Mingtao Huang, Yuan Shen
cs.CV · cs.AI
Automating filament tracing in Cryo-Electron Microscopy (Cryo-EM) is essential for 3D helical reconstruction but challenged by intersecting topologies and extremely low Signal-to-Noise Ratios ($\text{SNR} = σ_s^2/σ_n^2$ < 0.1 or -10 dB). Existing paradigms fail: pixel-wise segmenters suffer from severe topological fracturing, box-based detectors face ghost center drift, sequential trackers derail due to error accumulation, and traditional...
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17
CS-CLIP: Compositional Scene Graph-guided CLIP for Robust Compositional Reasoning
SeongJun Jeong, Minjoon Jung, Woo Suk Choi, Youwon Jang, Byoung-Tak Zhang
cs.CV · cs.AI
Vision-language models (VLMs) demonstrate strong performance across compositional reasoning benchmarks, which require reasoning over semantic perturbations of objects, attributes, relations, and their interactions. However, our controlled analysis reveals that existing compositionality-aware VLMs exhibit element-specific biases, often underperforming vanilla CLIP on certain compositional elements. To address this, we propose Compositional...
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18
ActionSplice: In-Flight Action Editing for Interactive World Models
Pardis Taghavi, Tingyu Guo, Jonas Lossner, Gaurav Pandey, Reza Langari
cs.CV · cs.LG
Chunk-autoregressive video world models typically condition each generated chunk on one action. An action received during sampling must therefore wait for the next chunk, condition future solver evaluations on a state produced under the previous action, or trigger rollback that repeats completed evaluations. We introduce ActionSplice, an inference framework that formulates this problem as Counterfactual State Transport (CST). A lightweight...
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19
WSPolypNet: Weakly Supervised Polyp Localization in Colonoscopy Videos
Giseong Hwang, Minjae Jo, Yeonghyeon Park, Kyeonghun Kim, Seoyeon Han, Donghoon Han, Haneul Kim, Yului Jeong, Insung...
cs.CV · cs.AI
Because dense frame-level annotation of colonoscopy videos is costly, we propose WSPolypNet, a weakly supervised framework for polyp localization using only video-level labels. WSPolypNet employs a 3D convolutional neural network trained with video-level supervision to generate class activation maps (CAMs), which identify candidate polyp regions without requiring frame-level spatial annotations. The CAM-derived localization cues are further...
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