Computer Vision
SGDet3D++: Geometry-Grounded Semantics for 4D Radar and Camera 3D Object Detection
Xiaokai Bai, Zhenyu Fan, Lianqing Zheng et al. (+3 more)
4D radar complements dense image semantics with long-range geometry and radial motion, but existing radar–camera detectors largely solve where to align the modalities while leaving whether a piece of evidence supports an evolving object hypothesis implicit. An image
token may describe an occluder, a nearby radar return may belong to another object, and a pose-aligned
memory slot may carry incompatible motion. We formulate hypothesis-conditioned evidence grounding, which separates candidate access from evidence use: semantic,
geometric, or temporal evidence is filtered or conditioned by the evolving 3D state before updating the corresponding query. \sgdetpp{} instantiates this principle through Anchor-Grounded Semantic
Retrieval (AGR), which conditions deformable image
retrieval on pooled anchor-consistent radar support; Geometry-Consistent Anchor Refinement (GCR), which attentively aggregates individual associated returns; and Doppler-Verified Correspondence (DVC), which replaces history only when current radial motion contradicts it. \sgdetpp{} improves the strongest compared method by 3.82 mAP and 6.82 ODS on OmniHD-Scenes and by 6.82 mAP and 9.22 NDS on ManTruckScenes, while also leading the listed methods in the TJ4DRadSet test comparison. Mechanism-targeted evaluations show that AGR improves strict AP in every projected-occlusion bin, the yaw-aligned box gate raises target-return purity from 29.95% to 58.87%, and DVC preserves 96.11% of motion-consistent history while retaining 75.90% contradiction recall. Code will be released.
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All authors Xiaokai Bai, Zhenyu Fan, Lianqing Zheng, Songkai Wang, Si-Yuan Cao, Hui-liang Shen
Computer Vision
InGuard: Towards Generalized Inner Guardrail for Safe Text-to-Image Generation
Zeyu Wang, Xiaodan Li, Zhiwen Li et al. (+2 more)
Modern text-to-image (T2I) models generate high-quality images from arbitrary user
prompts, yet they can just as easily produce not-safe-for-work (NSFW) content. Conventional outer guardrails consist of two components: a
prompt classifier that checks for risk before generation, and a post-hoc image classifier that checks the fully generated image. In this design, both classifiers operate outside the generation pipeline and do not use the model's own representations. This separation can limit
prompt-screening accuracy, while the image-side check runs only after the full generation cost has been spent. Moreover, a flagged
prompt can only be rejected, even when it could be adjusted to produce a safe image. In this work, we propose the Inner Guardrail (InGuard), a safety framework that works inside the pipeline on the model's own representations, leaving base-model parameters untouched. First, a risk classifier grades each
prompt as unsafe, risky, or benign based on the text encoder's
embeddings, with no external
language model. Second, SAGE (Soft-gated Asymmetric Guardrail for
Embeddings) modifies the
embeddings of risky
prompts, aiming to return a safe image instead of a refusal. Third, a latent detector checks the one-step clean latent estimate midway through denoising, reaching nearly image-level performance and halting generation when risk is detected. We also construct the RevGen Safety
Benchmark to evaluate T2I safety under realistic conditions: 10,000
prompts built through real-image reverse generation, with a rewriting step that supplies controlled intellectual-property (IP) characters, covering graded porn/gore risks, categorical IP risks, and benign negatives. Across five open-weight T2I models, InGuard reaches 97.9-98.8% safety rate, matching or exceeding the outer guardrail, with 57.5-73.5% less benign disturbance, ~3.7x fewer parameters, and 50-55.6% of denoising steps skipped.
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All authors Zeyu Wang, Xiaodan Li, Zhiwen Li, Yuefeng Chen, Hui Xue
Computer Vision
A generalizable structural brain MRI foundation model built through dual-priority federated pretraining
Zhen Yu, Yang Liu, Xiahai Zhuang et al. (+1 more)
Foundation models hold promise for generalizable analysis of structural
brain magnetic resonance imaging (MRI) across development, aging and disease. However, existing models are typically built through centralized pretraining on pooled data, despite
privacy and governance constraints. Such pooling optimization can overemphasize cohort size and overlook complementary information from smaller, specialized cohorts. Here we present BrainFedFM, a structural
brain MRI foundation model federatively pretrained on 164,707 three-dimensional scans drawn from diverse real-world data distributions and organized across 42
federated sites. BrainFedFM uses dual-priority
federated pretraining, coupling spatial-priority masking at each site with site-priority aggregation at the server to emphasize informative anatomical regions locally and prioritize site contributions globally. Across 20 downstream
datasets spanning 17 classification, regression and
segmentation tasks, BrainFedFM achieved the state-of-the-art performance (mean rank 1.68, 50% gain) across seven models, including four centralized foundation models, while showing particularly consistent advantages in classification and regression and robustness across underrepresented populations. These findings demonstrate the generalizability of BrainFedFM and highlight
federated pretraining as a practical strategy for developing neuroimaging foundation models from distributed data without pooling raw images.
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All authors Zhen Yu, Yang Liu, Xiahai Zhuang, Qingchao Chen
Computer Vision · Cross-listed from cs.MM
When Visual Quality Misleads: Intent Recognition under Rendered Avatar Distortions
Ning-Hsuan Chang, Kai-Siang Ma and Yu-Chih Chen
Avatar-streaming systems are commonly evaluated with image and
video quality assessment (IQA/VQA) metrics, implicitly treating visual fidelity as a proxy for communicative success. We test this assumption through a controlled behavioral study of rendered 3D avatars across a pristine condition and fourteen
geometric, photometric, temporal, and combined distortions. Fifty-nine participants contributed 2,688 judgments of perceived action, response confidence, and visual quality. We identify Misleading Quality in this
dataset as distorted renderings that retain above-average perceived quality but yield below-average action-recognition accuracy. We also derive an Intent Quality Score (IQS) combining recognition correctness and confidence as the behavioral target for objective metrics. Among 126 distorted content–condition
cells, 31 (24.6%) exhibited Misleading Quality; temporal and
geometric distortions showed the highest rates, at 50.0% and 31.1%, respectively. The results reveal a quality–accuracy dissociation where distortion families affect appearance and communication differently. Across 24 direct-scoring IQA/VQA metrics and three supervised feature-regression baselines,
alignment with IQS remained limited; at $\lambda=0.5$, the best leave-one-content-out baseline reached PLCC $=0.4435$. Under this controlled protocol, visual fidelity alone is insufficient for avatar communication, motivating intent-aware quality assessment and streaming objectives.
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Computer Vision
ICM: Intra-class Mixing for Domain Adaptation in Adverse Weather
Boying Li, Chang Liu, Britta Ayano Wilde et al. (+4 more)
Unsupervised domain adaptation (UDA) for semantic
segmentation remains challenging under adverse weather conditions because severe appearance changes enlarge the domain gap and degrade the reliability of pseudo labels in the target domain. To address this problem, we propose an Intra-Class Mixing Consistency (ICM) framework that enforces prediction consistency between an intra-class mixed image and its original counterpart. Unlike previous mixing-based consistency methods that combine regions across different images or domains and may introduce unrealistic semantic inconsistencies, ICM performs mixing within the same image and semantic class, preserving realistic semantic layout for consistency regularization. With ICM, we establish a new state-of-the-art performance for clear-to-adverse-weather unsupervised domain adaptation (UDA) in semantic
segmentation. On the Cityscapes $\rightarrow$ ACDC
benchmark, our method achieves 75.7% mIoU, outperforming the previous state of the art by +1.9 pp, demonstrating its effectiveness in mitigating class confusion under challenging environmental conditions. The code is provided in the supplementary material.
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All authors Boying Li, Chang Liu, Britta Ayano Wilde, György Kovács, Tosin Adewumi, Björn Backe, Hamam Mokayed
Computer Vision
M3D-Net: Hierarchical Coordination of Spatial Context, Feature Reuse, and Differential Attention for Mammography Classification
Zheng Yu, Xinhang Li, Jiabao Gao et al. (+2 more)
Breast image classification requires local detail and global tissue context, yet these cues can weaken as representations deepen. We present M3D-Net, a mammography encoder that hierarchically coordinates multi-scale coordinate
attention, bounded dynamic feature reuse, and differential
attention through resolution-aware operator placement. Within-stage
retrieval preserves access to earlier features, coordinate-aware aggregation integrates local and global context, and differential
attention operates at coarse resolutions. We evaluate image-only classification on AISSLab mammography and an adapted image–clinical model on BrEaST ultrasound. Against EdgeNeXt, RepViT, and TransXNet, the proposed implementations achieve the highest recorded validation accuracy and late-training accuracy, with the lowest endpoint cross-
entropy loss. Validation accuracies reach 97.78% and 80.39%, respectively. These results support further evaluation of hierarchical coordination across breast imaging settings; repeated-seed, component-controlled, and independent evaluations remain necessary.
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All authors Zheng Yu, Xinhang Li, Jiabao Gao, Boyang Wang, Xiang Li
Computer Vision
NV-Reason-CT: 3D Visual Language Model for CT Analysis
Andriy Myronenko, Dong Yang, Yucheng Tang et al. (+15 more)
We present NV-Reason-CT, a generative
vision–
language model for chest and abdominal CT combining native 3D visual encoding with radiologist-guided
reasoning. The model couples a native 3D
vision transformer with a
language model, passing all visual
tokens and their explicit 3D coordinates into
language decoding without further spatial
token merging. This retains volumetric spatial information within the
vision encoder and through the
language model's positional encoding during joint processing with text. We train on a curated corpus of approximately 550,000
multimodal instruction examples from 70,111 unique CT image inputs, combining standardized reports, abnormality-focused and anatomy-specific questions, multi-turn interactions, and radiologist-authored
reasoning from recorded and transcribed expert CT interpretations. Expert annotations provide direct supervision and guide additional report-grounded synthetic
reasoning. End-to-end supervised fine-tuning (SFT) is followed by Group Relative Policy Optimization (GRPO), with verifiable rewards over chest and abdominal abnormality sets. The model supports abnormality classification, report generation, and interactive
reasoning with reviewable observations, differential diagnoses, and uncertainty. Evaluation spans public CT
benchmarks and a held-out NIH cohort. On CT-RATE, NV-Reason-CT achieves a macro-F1 of 0.614 and macro-AUROC of 0.871 without a task-specific classification head; generated reports achieve a report-derived macro-F1 of 0.592. In a preliminary study with expert radiologists, AI-assisted review received favorable confidence ratings and was associated with a 50% reduction in average reported interpretation and reporting time. We release the model and training code to support reproducible research on explainable AI for volumetric medical imaging.
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All authors Andriy Myronenko, Dong Yang, Yucheng Tang, Baris Turkbey, Benjamin Simon, Stephanie Harmon, Rikhil Makwana, Mariam Aboian, Sena Azamat, Ibrahim Ethem Hamamci, Sezgin Er, Bjoern Menze, Zongwei Zhou, Wenxuan Li, Marc Edgar, Yufan He, Pengfei Guo, Daguang Xu
Computer Vision
Know-Your-Scene (KYS)-SLAM: Hierarchical Semantic-Motion Priors for Feature Matching in Stereo Visual SLAM
Preeti Chatterjee, Jin Lu, Jin Sun et al. (+1 more)
Stereo visual SLAM systems built on local descriptors suffer from semantic ambiguity, instance-level confusion, and independently moving objects, each corrupting data association and accumulating as trajectory drift. Prevailing semantic and dynamic SLAM methods address this through binary feature rejection, sacrificing correspondence density for outlier suppression. We contend that contextual implausibility is better expressed as a graded quantity than an exclusion criterion. We present Know-Your-Scene (KYS)-SLAM, a modular extension of ORB-SLAM3 that supplants feature rejection with continuous correspondence modulation. The contribution is the reframing of contextual evidence as correspondence cost, applied within feature matching and leaving the
geometric backend unmodified. Each keypoint is augmented with semantic, panoptic, and motion priors fused through a hierarchical compatibility formulation, in which semantic class and instance identity enforce structural plausibility while a zero-shot motion score down-weights features on independently moving objects. That score comes from a training-free module fitting a depth-aware ego-motion model to background optical flow and classifying panoptic segments via self-calibrating, coverage-aware thresholds, so only segments with sufficient motion evidence are penalized and static structure is left unpenalized. Penalizing correspondences rather than discarding them preserves the
geometric support bundle adjustment depends on. Under one fixed configuration, no coefficient retuned per sequence or
dataset, KYS-SLAM reduces per-sequence ATE RMSE by 17.4% on outdoor KITTI and 27.7% on indoor EuRoC across 21 stereo sequences with no regressions, and by 6.6% on dynamic subsets of KITTI Tracking and 17.8%, up to 31.2%, on Virtual KITTI 2 – cross-domain transfer across outdoor driving, indoor flight, and synthetic imagery under one set of constants.
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All authors Preeti Chatterjee, Jin Lu, Jin Sun, Suchendra M. Bhandarkar
Computer Vision
Information Capacity of Generative Video Compression: Quantifying the Rate-Compute Exchange at Identical Quality
Cheng Yuan, Jiawei Shao and Xuelong Li
Under the AI Flow framework, communication networks distribute intelligence across devices, edge servers, and clouds, and computation at the receiver becomes a resource that can substitute for transmitted bits. Generative
video compression (GVC) embodies this exchange by sending compact
tokens with ultra-low bitrate and letting a generative decoder synthesize the
video, yet how much bandwidth savings a unit of decoder compute actually achieves has never been quantified. To fill this vacancy, we model reconstruction quality as a two-factor power law in data rate and decoder compute, which fits measured DISTS of two GVC decoders with a mean error below 3%, and define the information capacity (IC) as the negative logarithmic slope along an iso-quality contour, namely the fraction of rate saved per fractional increase in compute at identical quality. IC is dimensionless and unit-invariant, thus enabling an architecture-agnostic comparison. It forms a field over the operating plane, locating where additional denoising steps are worth their cost. Across five
datasets, the 14B decoder trades more compute for fewer rate about ten times more efficiently than the 1.3B decoder. IC also varies significantly across
datasets, indicating imbalanced performance on the rate-compute trade-off in GVC methods.
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Computer Vision
DeltaS: Reading the Gated Linear Attention State for KV Cache Eviction in Streaming Video
Taeyoun Kwon, Seungjin Kim, Hyeonyu Kim et al. (+1 more)
Recent
video-
language models increasingly adopt hybrid architectures that interleave linear and full
attention layers for efficient long-context processing. While the recurrent state of linear
attention remains fixed in size, the KV cache of full
attention continues to grow with the
video stream, making eviction necessary under a bounded
memory budget. The key challenge in streaming is that eviction must occur before the question arrives, so what to retain has to be decided without the question. Existing eviction methods derive
token scores from the KV cache itself, using position,
attention, or key-value representations, and
attention-based scores further require proxy queries or extra computation. Hybrid backbones offer another source of signal. In gated-delta linear
attention, the recurrent state is updated by the residual between each input and what can already be retrieved from the state, so its change over a chunk of frames reflects how much new information the chunk brings. We propose DeltaS, a query-agnostic, training-free method that retains
video chunks inducing larger normalized state change, or state drift. In a controlled comparison with the budget and retention policy held fixed, state drift outperforms position-,
attention-, and key-value-based signals. With a signal costing only 1.9% of the forward pass, DeltaS surpasses the strongest query-agnostic bounded-
memory baseline by 2.1 points on average across six long-
video benchmarks and by 5.6 points on the longest
benchmark. These results suggest that the two
memories of hybrid architectures can work cooperatively. Code is available at https://github.com/MaumAI-Company/DeltaS.
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All authors Taeyoun Kwon, Seungjin Kim, Hyeonyu Kim, Moon Hwan Kim
Computer Vision
CereVLA: Cerebellum-Inspired Consequence-Aware Residual Governance for Efficient Vision-Language-Action Execution
Shuai Zeng, Yuxuan Liang, Hangmiao Hu et al. (+4 more)
Action-chunked
vision-
language-action (VLA) policies improve inference efficiency, but limited feedback within committed action chunks can lead to accumulated execution errors. Residual adaptation can correct such deviations without retraining the VLA; however, existing corrections are typically optimized for reference-action consistency without explicitly considering their downstream consequences. To address this limitation, we present Cerebellum-Inspired Consequence-Aware Residual Governance (CereVLA), a unified framework that integrates lightweight residual refinement and predictive consequence evaluation into frozen VLA execution. Corrective actions are first generated by flow-based residual refinement, and their short- and interval-horizon consequences are then evaluated by a recurrent state-space model and a history-aware classifier. Residual corrections predicted to be unfavorable are selectively suppressed by a lightweight governor. Comparisons with state-of-the-art methods on LIBERO-10 and LIBERO-GOAL demonstrate the effectiveness of CereVLA. On SO-101, CereVLA increases task success from 57.5% to 90.0% and reduces mean control steps by 19.6% among successful trials, relative to the frozen SmolVLA baseline.
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All authors Shuai Zeng, Yuxuan Liang, Hangmiao Hu, Fobao Zhou, Zixiang Wang, Wenxi Hong, Hang Zhao
Computer Vision
Hybrid Gaussians for Robust Open-Vocabulary 3D Segmentation with Multi-View Object Association and Boundary Refinement
Xueqi Qiu, Yueming Sun, Tianyu Zhang et al. (+2 more)
Open-vocabulary 3D
segmentation localizes objects from free-form text queries, but remains challenging in real image sequences: incomplete or noisy 2D supervision destabilizes multi-view identity assignment, while full-scene semantic learning weakens object-level discriminability. We introduce Hybrid Gaussians, a unified 3D representation jointly modeling object association and
language-aligned semantics. Its Multi-View Object Association mechanism combines Observation Fusion and Semantic Contrastive Learning to improve identity consistency and semantic discrimination. Boundary Reconstruction Optimization further refines local boundary structure to improve contour quality. Experiments on LERF and 3D-OVS demonstrate strong quantitative and qualitative performance. Our method achieves 59.1% mIoU on LERF, yielding a 13.4% relative gain over the baseline. Project page: https://nora202.github.io/hybridgaussians.
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All authors Xueqi Qiu, Yueming Sun, Tianyu Zhang, Yuxuan Xia, Yang Long
Computer Vision
Invisible in Space, Visible in Time: Motion Vision CAPTCHA against GUI Agents
Zeyu Zhang, Dingyi Rong, Zijian Chen et al. (+3 more)
Most existing visual CAPTCHAs remain spatially solvable: the required information is exposed by static appearance, local structure, and interface state. This assumption is weakened by advances in
multimodal large
language models (MLLMs) and Graphical User Interface (GUI)
agents, which exhibit strong visual perception,
reasoning, and browser interaction capabilities. We propose Motion
Vision CAPTCHA (MVCAP), a hierarchical motion-based CAPTCHA framework in which target semantics are instantiated as motion-defined foreground structures and become recoverable only through temporal segregation from a dynamically evolving background. Built on this shared principle, MVCAP is instantiated in three perceptually progressive levels: coherent motion, structural motion, and biological motion. To evaluate this framework, we introduce MVCAP-Bench, a browser-based
benchmark with 600 live CAPTCHA instances, together with a matched foreground-only control
benchmark, MVCAP-Bench-FG. We evaluate humans, Browser Use
agents, native computer use
agents, and a supplementary offline VQA setting derived from the same instances. Results reveal a substantial human–
agent gap: on the full MVCAP-Bench, human accuracy reaches 99.6%, whereas the best GUI
agent achieves only 16.8%, close to the six-way chance level. The foreground-only control further shows that the key difficulty comes from dynamic background camouflage rather than answer format or browser interaction alone. These findings identify a measurable human–
agent perception gap and position MVCAP-Bench as a
benchmark for studying motion-defined perception in current
agents.
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All authors Zeyu Zhang, Dingyi Rong, Zijian Chen, Zicheng Zhang, Xiongkuo Min, Guangtao Zhai
Computer Vision
Beyond Balanced Accuracy: A Resolution and Parity-Controlled Benchmark for Vision-Language and Vision-Only Defect Assessment in UAV Power-Line Inspection
Linghao Zhang, Siyu Xiang, Junwei Kuang et al. (+1 more)
Vision-
language models (VLMs) are often reported to outperform task-specific
vision backbones for unmanned aerial vehicle (
UAV) power-line defect assessment. We test that claim on ElecVQA-Bench, a 56,972-item
benchmark derived from the public InsPLAD
dataset, across six evaluation choices: partition, evaluated item set, label space, replication, input resolution, and side information. On a matched partition, a Swin
Transformer and the strongest adapted VLM differ by only 0.03 points at binary screening. At seven-way defect typing, increasing the
vision backbones from 224 px to the measured pixel budget of the VLM preprocessor narrows the gap against InternVL3.5-8B from +20.53 to -0.57 points for ResNet-50 and from +23.67 to +4.70 points for Swin-T. A pixel-budget audit shifts Qwen3-VL-8B macro recall by 10.78 points, yet a source-pixel-matched InternVL control still leaves Qwen ahead by 7.43 to 13.61 points while using 56% fewer visual
tokens, so neither source pixels nor
token budget explains the difference between the two VLMs. A two-seed global replication changes Qwen binary accuracy and seven-way macro recall by 0.86 and 1.02 points. After split-specific retraining, Qwen does not lead at crop or image level, and a 14-tower, three-seed replication reverses the sign across seeds, giving mean common-six macro recall of 0.9085 for Qwen against 0.9509 for ResNet-50. No split regime yields a family-level advantage that survives multiple-comparison correction. The study supports a
benchmark-audit contribution rather than a general claim of VLM superiority.
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All authors Linghao Zhang, Siyu Xiang, Junwei Kuang, Peiyu Yi
Computer Vision
LeWAM: Latent evolving World Action Model
Xueji Fang, Boqiang Duan, Hua Wu et al. (+2 more)
World Action Models (WAMs) jointly model action generation and environment dynamics and are mostly built on pretrained
Video Diffusion Models (VDMs). In VDM-based WAMs, observations are first encoded by a VAE, and the resulting compressed latents are then processed by large
video diffusion backbones to extract effective features for action generation. However, this paradigm ties WAM performance and training cost to large-scale
video generation pretraining, limiting WAM efficiency and scalability. In this paper, we theoretically and empirically investigate how visual representations affect action generation in WAMs. Our results show that predictive
embeddings from Joint-
Embedding Predictive Architecture (JEPA) encoders better support action generation than compressed VAE latents, with I-JEPA performing best in our encoder comparison. Based on these findings, we propose LeWAM, which conditions action generation on JEPA
embeddings and models environment
evolution by predicting future
embeddings in the same space, without relying on a
video diffusion backbone. We further find that imitation learning matches demonstrated actions but does not distinguish better actions from worse ones, even though small action deviations can greatly affect task success. To address this limitation without additional environment interaction or the human oversight required for resets and safety, we introduce Demonstration-Guided DPO (DemoDPO), an offline preference refinement stage that derives preference supervision directly from demonstrations. With only 0.4B trainable parameters, LeWAM achieves an average success rate of 92.28% on RoboTwin 2.0, comparable to that of state-of-the-art VLAs and WAMs, and maintains practical effectiveness on real-world manipulation tasks.
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All authors Xueji Fang, Boqiang Duan, Hua Wu, Jingdong Wang, Guo-Jun Qi
Computer Vision
SatUnreal: A High-Precision Synthetic Dataset for Satellite Stereo Matching via Unreal Engine
Han-Gyeol Kim, JaeWan Park, Junmin Park et al. (+1 more)
3D reconstruction from satellite imagery is essential for large-scale topographic analysis, yet the lack of high-fidelity training
datasets with accurate occlusion labels remains a primary bottleneck. Existing
benchmarks, such as US3D and WHU-Stereo, face inherent challenges in spatio-temporal mismatch – environmental changes and shadow displacements between multi-view acquisitions – and provide ambiguous ground truth in occluded regions due to
LiDAR sparsity. In this paper, we propose SatUnreal, a high-precision synthetic
dataset designed to fundamentally overcome these limitations through an Unreal Engine-based simulation pipeline. SatUnreal provides 10,000 stereo pairs with high resolution (0.3m GSD) and is characterized by: (1) Physical Geometry Simulation, replicating realistic satellite orbits by systematically varying baselines and azimuths; (2) Spatio-temporal Consistency, eliminating temporal noise through fixed virtual environments; (3) Topographic Diversity, spanning dense urban canyons to low-texture natural terrains; and (4) Mathematical Label Integrity, utilizing a novel two-step linetrace
algorithm to generate flawless occlusion masks. Experimental results using SOTA iterative models demonstrate that models trained exclusively on SatUnreal achieve superior zero-shot transfer performance on real-world
benchmarks (US3D, WHU-Stereo) compared to those trained on real
datasets. Our findings prove that physically accurate synthetic data provides a more effective supervisory signal for learning
geometric features than complex real-world observations, establishing a new paradigm for Sim-to-Real transfer in Earth Observation. Code and
dataset are available at https://github.com/jmp-Telepix/SatUnreal_A_High-Precision_Synthetic_
Dataset_for_Satellite_Stereo_Matching_via_UnrealEngine
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All authors Han-Gyeol Kim, JaeWan Park, Junmin Park, Darongsae Kwon
Computer Vision
Overlapping Visual Grouping Without Semantic Priors
Teemu Saukkio, Hashem Haghbayan and Juha Plosila
Most computer-
vision systems organize visual input toward a predefined interpretation, such as semantic categories, prompted regions, learned object-like representations, or a single spatial partition. This work considers an earlier stage of visual organization: the formation of candidate perceptual units directly from sensor measurements before their identity, meaning, or task relevance is known. We introduce Domain Parent Grouping (DPG), a sensor-grounded grouping method in which complementary measurement relationships are represented in separate processing domains. Spatially connected groups formed within these domains are related through cross-domain overlap, yielding a non-exclusive grouping representation rather than a single mutually exclusive
segmentation. This representation retains broader and more localized groups, as well as alternative grouping boundaries over the same image locations, simultaneously available. DPG also includes a native mechanism for reprocessing selected group content, in which input-relative measurement ranges allow the observational resolution to change while preserving previously formed groups. DPG is implemented using three domains representing locally contextualized luminance, direct chromatic relationships, and contextual chromatic relationships. Experiments on the BSDS500
dataset demonstrate the benefit of combining the three domains. The results further show that DPG forms measurement-supported groups corresponding to low-level image structure, and that these groups exhibit measurable correspondence with human-annotated regions and boundaries. This demonstrates that structured visual organization can emerge directly from relationships among sensor measurements.
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Computer Vision
S2A:Semantic-to-Spatial Alignment for Alignment-Free RGB-T Salient Object Detection
Qiangqiang Zhou, Yang Luo, Yong Chen et al. (+1 more)
Alignment-free RGB-T salient object detection (RGB-T SOD) aims to identify salient objects from unregistered RGB and thermal image pairs without costly pre-
alignment. However, spatial misalignment breaks pixel-wise correspondence and causes feature contamination during cross-modal fusion. To address this issue, we propose S2A, a semantic-to-spatial
alignment framework for
alignment-free RGB-T SOD. Specifically, a global-guided hierarchical fusion module (GGHF) first exploits global semantic guidance to suppress background interference and refine hierarchical intra-modal features. Subsequently, the
alignment-free cross-modal channel
attention module (AFCA) globally exchanges complementary semantic information through channel-wise interaction, effectively overcoming the interference caused by local spatial misalignments. Finally, a spatial deformable cross-
attention module (SDCA) predicts adaptive sampling offsets to recover local cross-modal spatial correspondence. Through this semantic-to-spatial paradigm, S2A first enables reliable cross-modal semantic interaction and subsequently performs local spatial calibration, effectively reducing misalignment-induced feature contamination. Without bells and whistles, S2A achieves highly competitive performance on multiple public
alignment-free RGB-T
benchmarks, demonstrating its effectiveness in alleviating misalignment-induced feature contamination.
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All authors Qiangqiang Zhou, Yang Luo, Yong Chen, Jiawei Xu
Computer Vision
What Looks Like a Capability Limit in Vision-Language Models Is a Readout Limit
Alfredo F. Frontera Del Valle
Benchmarks for
vision-
language models offer their answer choices in some convention: a letter, a color name, a pixel coordinate. That convention is treated as neutral. We find it is not, and that the limits a
benchmark reports can belong to the readout rather than to the model. On 200 COCO photographs, Qwen3-VL-4B picks the correct one of nine locations for a named object 68.5% of the time when the locations are given in English and 20.0% when the same locations are given as pixel coordinates. Chance is 11.1%. The cost arises when the answer options are coordinates; giving the model a coordinate in the question instead costs 3.5 points and is not significant. The gap holds on a 4x4 grid, under 8-bit rather than 4-bit quantization, and in every slice by object size, boundary distance and category. It also decides which model wins. Two models that tie under English names differ by 39 points in one coordinate system and by 54 in the other, in opposite directions. On the color task, three of the four open models capable of the task show the penalty; on photographs, two of three open models do, and so does Gemini, at 11.1 points on parseable answers (p = 1e-4). GPT-4o does not. To ask whether a model reads a coordinate at all, we attach the wrong name to each one and record which the model follows. Color options written as hue angles are followed below chance; a normalized pixel convention is followed at four times chance. This tells apart conventions a model can use from ones it cannot, though it did not predict accuracy on two untried conventions. Five models also name the same color wheel five different ways, so a fixed answer vocabulary is not neutral across models either. Five times during this work we measured a capable model as incapable because our scorer and the model disagreed about what an answer looks like. We report each case. They are the phenomenon in miniature.
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Computer Vision · Cross-listed from cs.RO
Automotive mmWave Spinning Radar Place Recognition with Spatially Gated Feature-Correlation Representation
Saimunur Rahman, Sagun Singh Shrestha, Abdelwahed Khamis et al. (+1 more)
Automotive spinning FMCW radar provides dense, $360^\circ$ sensing and remains reliable under poor illumination and adverse weather, making it well-suited to autonomous
navigation. Place recognition uses these observations to identify previously visited locations for re-localization and long-term
navigation. However, heading changes appear as circular shifts in the polar radar representation, and conventional global aggregation can lose relationships among radar responses that are important for distinguishing similar places. We propose SGCA-Net, a spinning radar place recognition framework that combines rotation-robust feature extraction with Spatially Gated Correlation Aggregation (SGCA). SGCA learns spatial weights to reduce the influence of unstable and ambiguous radar regions, while aggregating pairwise correlations among local responses to preserve informative feature relationships. Experiments on the MulRan
dataset show that SGCA-Net consistently outperforms SOTA methods across urban, campus, and open-road environments, while remaining robust to substantial heading variation. Evaluation on the HeRCULES
dataset further demonstrates that SGCA-Net generalizes to unseen environments and radar sensors without fine-tuning.
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All authors Saimunur Rahman, Sagun Singh Shrestha, Abdelwahed Khamis, Peyman Moghadam
Computer Vision · Cross-listed from cs.HC
ASAP: Visual Analytics for Identifying and Analyzing Image Patterns in AI-generated Images
Jinbin Huang, Yuki Ueno, Chen Chen et al. (+4 more)
Generative image models can produce highly realistic images, raising concerns about potential misuse in creating deceptive content. Current deepfake approaches face several challenges, including limited generalizability, lack of interpretability, and poor actionability. To help address these, we present ASAP, an interactive visualization system designed to empower users in the analysis and summarization of deceptive patterns in AI-generated images. ASAP introduces a novel CLIP-adapted image encoder that generates interpretable representations, enabling the extraction of influential pixel regions via calculated masks. This approach facilitates the identification of key deceptive features through influence measurement techniques. These backend techniques are integrated into a visual analytics dashboard that allows users to quantify and analyze authenticity-indicative patterns in image collections containing both authentic and AI-generated images. This approach also supports the comparative analysis of various generative models, including GANs and
diffusion models. We demonstrate ASAP's efficacy through a user study and two application scenarios using established fake image detection
benchmarks, showcasing its ability to effectively extract and quantify deceptive patterns.
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All authors Jinbin Huang, Yuki Ueno, Chen Chen, Aditi Mishra, Bum Chul Kwon, Zhicheng Liu, Chris Bryan
Computer Vision
Geometry-Conditioned Visual Place Recognition in Natural Environments
Walter Nedov, Saimunur Rahman, Kavindie Katuwandeniya et al. (+3 more)
Visual Place Recognition (VPR) in natural environments remains challenging due to repetitive vegetation, sparse distinctive landmarks, and substantial appearance and viewpoint variation across traversals. While visual observations of the same place can change considerably, their underlying spatial structure is often more persistent. We exploit this complementary
geometric consistency through Depth-Aware
Distillation (DAD), which conditions the
token representations of a pretrained
Vision Foundation Model (VFM) on geometry inferred by a
Geometric Foundation Model (GFM), without any depth sensor. Rather than treating geometry as an additional input modality, DAD projects image-aligned depth into the VFM
token space and selectively modulates visual representations through channel-wise
geometric conditioning. A two-stage teacher-guided learning strategy first anchors the geometry-conditioned representation to the pretrained appearance space, before refining it for place discrimination. Evaluated on the WildCross
benchmark, DAD improves average inter-sequence Recall@1 from 61.41% to 66.37% and Recall@5 from 65.86% to 72.49% over a matched appearance-only baseline, with the largest gains under reverse traversal and long-term appearance variation. These results show that GFM-derived geometry can provide a persistent structural prior for VPR when visual appearance becomes unreliable.
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All authors Walter Nedov, Saimunur Rahman, Kavindie Katuwandeniya, David Hall, Kaushik Roy, Peyman Moghadam
Computer Vision
Beyond Mean Foils: Auditing Worst-Foil Specificity in Frozen CLIP Region Explanations
Kaixin Liu, Zhipeng Ye, Feng Jiang et al. (+2 more)
A region can overlap a target object yet contribute more to another class. We test regions selected by Cluster-based Concept Importance (CCI) in frozen CLIP. Across COCO and VOC with two checkpoints, 41.08-64.78% of regions that pass overlap and mean-contrast checks fail against the strongest competing class. Removing competitors annotated in the image leaves 39.69-63.64% failing. We then test all eight candidate regions per image. An alternative passes the test for 6.25-7.84% of failures on COCO and 27.40-31.15% on VOC. Requiring it to preserve the original target-score drop within $\epsilon = 0.02$ reduces these rates to 0.16-0.98%. Available regions and target-drop tolerance constrain repair; relaxing the tolerance increases repair opportunities.
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All authors Kaixin Liu, Zhipeng Ye, Feng Jiang, Zhenghao Wang, Qihang Wu
Computer Vision
Can Vision-Language Models Analyze Human-Centered Video? Mapping Model Capabilities and Human-AI Collaborative Workflows
Xiyuan Shen, Jiuyang Lyu, Seokhyun Hwang et al. (+4 more)
Video provides a rich record of human behavior, interaction, and situated contexts, offering important evidence for understanding people and conducting human-centered research. As
vision-
language models (VLMs) become increasingly capable of analyzing
video, they offer opportunities to automate this traditionally human-intensive process. Yet a central question remains: when can VLMs analyze human-centered
video independently, and when does reliable analysis still require human involvement? To address this question, we first characterize
video analysis practices in human-centered research. We systematically analyze all 1,702 CHI 2026 full papers and identify 125 that annotate
videos. Through iterative coding, we derive a five-dimensional taxonomy spanning analytic purpose, viewpoint, phenomenon,
reasoning requirement, and annotation authority. Grounded in recurring annotation tasks captured by this taxonomy, we construct a
benchmark of 15 representative tasks from open
datasets to map the capabilities and limitations of a general-purpose VLM. We examine the division of labor between humans and VLMs by comparing three annotation workflows: VLM alone, human alone, and human verification of VLM outputs. Across tasks, VLM-alone annotation approaches human accuracy on average (HNS = 97.0, where 100 denotes human-alone performance), demonstrating substantial potential to automate human-centered
video analysis. Human verification achieves the highest accuracy (HNS = 121.5) while reducing human annotation time by 48.9% and monetary cost by 31.3%-44.5% relative to human-alone annotation. Our findings connect real-world human-centered
video analysis tasks and current VLM capabilities, and clarify how human-AI collaboration can make VLM-assisted analysis reliable and efficient.
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All authors Xiyuan Shen, Jiuyang Lyu, Seokhyun Hwang, Huanfen Yao, Shwetak Patel, Zhihan Zhang, Jacob O. Wobbrock
Computer Vision
Breaking Weather-Content Coupling: Type-Severity Guided Progressive Disentanglement for All-in-One Infrared Restoration
Xinyao Wang, Lijun He, Zhihan Ren et al. (+1 more)
Infrared (IR) imaging is crucial for autonomous driving, remote sensing, and other perception tasks. However, adverse weather may introduce fake structural responses that are entangled with real thermal structures. Existing IR restoration methods are typically designed for a single degradation type or directly reconstruct from degradation-entangled representations. Consequently, they struggle to distinguish intrinsic thermal structures from weather-induced fake responses and to accommodate spatially varying degradation severity, leading to artifacts or the over-suppression of weak but meaningful thermal responses. To address these issues, we propose TSGPD-IR, a type-severity guided progressive disentanglement network for all-in-one infrared restoration that factorizes restoration guidance into task-level weather semantics and region-level degradation severity. Specifically, a Weather and Semantic Co-Guided Multi-Level
Prompt Generation Module combines global weather semantics with stage-wise local features to generate adaptive
prompts that progressively suppress degradation-induced responses while preserving intrinsic thermal structures. To complement global weather semantics with spatial restoration control, a Proxy-Supervised Regional Degradation
Estimator derives severity supervision without manual annotations and predicts spatially varying degradation priors. Guided by these cues, a Multi-Source Collaborative Expert Selection Strategy uses a shared branch to preserve weather-invariant thermal structures and hierarchical routing to select weather-specific expert pools and severity-compatible regional experts. This design progressively separates degradation interference from genuine thermal content and enables region-adaptive restoration, reducing both residual artifacts and over-suppression.
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All authors Xinyao Wang, Lijun He, Zhihan Ren, Fan Li
Computer Vision
High Dynamic Range Video Reconstruction from Single-Exposure Raw Sequences
Tao Zhang, Peixian Su, Xingyu Gao et al. (+6 more)
Due to the limited dynamic range of conventional image sensors, captured low dynamic range (LDR)
video often suffers from highlight clipping and shadow detail loss, making high-quality high dynamic range (HDR) reconstruction from single-exposure sequences highly challenging without alternating exposures or extra hardware. Alternating-exposure HDR methods sacrifice frame rate and struggle with motion
alignment, making them impractical for real-world capture. To address this, we propose RawHDRV, an end-to-end framework for single-exposure Raw
video HDR reconstruction, that fundamentally exploits the linear response and channel-specific characteristics of Bayer data. Specifically, it features a channel-decomposition temporal
alignment and fusion strategy that processes Bayer channels separately to exploit their distinct exposure characteristics, together with exposure-aware weighted fusion. It further incorporates an exposure complementarity mask-guided restoration module that leverages inter-frame exposure redundancy to adaptively fuse reliable information and suppress saturation artifacts, and introduces a mask-guided color loss that combines normalized error constraints with gradient smoothing to enhance highlight recovery. Furthermore, we construct a large-scale mobile Raw-HDR
video dataset with per-frame HDR annotations. Experiments show that our method achieves the state-of-the-art results in all metrics, demonstrating superior spatial quality and temporal stability under extreme exposure conditions. The code is available at https://github.com/supeixian/RawHDRV.
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All authors Tao Zhang, Peixian Su, Xingyu Gao, Yunhao Zou, Yu Lu, Zunjie Zhu, Bolun Zheng, Ying Fu, Chenggang Yan
Computer Vision
GaussPDE: Graph-Based Partial Differential Equation-Driven Rendering for 3D Gaussian Splatting
Haoyuan Yue, Fengyuan Ye and Ziyin Li
We present GaussPDE, a framework that injects physically structured partial differential equation (PDE) dynamics into pretrained 3D Gaussian scenes without mesh extraction, voxelization, or retraining. Our key observation is that PDE rendering requires not only accurate appearance, but also a reliable discrete computational domain. We therefore first introduce camera-aware regularization during 3DGS reconstruction to suppress camera-near floaters and oversized primitives that would create unstable graph
topology. We then construct an active Gaussian graph using covariance-aware distances and opacity, appearance, and boundary-aware conductance, enabling mass-weighted graph
Laplacian PDE
evolution directly over Gaussian primitives. The evolving scalar PDE state is coupled back to rendering by modifying the direct-current spherical harmonic color coefficients while preserving geometry, opacity, and view-dependent rendering behavior. Experiments on real and synthetic scenes show that GaussPDE produces stable, controllable, and spatially coherent dynamic visualizations, with reduced cross-boundary leakage compared with baselines.
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Computer Vision · Cross-listed from cs.LG
What Converges in the Platonic Representation Hypothesis? Structure over Geometry
Junwon You, Mihyun Jang, Sangwoo Mo et al. (+1 more)
The Platonic Representation Hypothesis suggests that increasingly capable models converge toward shared representations. Recent work narrows this claim to shared local neighborhood relationships, finding that capacity-dependent trends in several global similarity measures largely disappear after calibration. We challenge this interpretation by showing that prior local-global comparisons confound structural scale (local versus global) with what is compared: relational structure, defined by which samples are related, versus metric geometry, characterized by quantitative relations such as distances, similarities, or correlations. To disentangle these factors, we construct a controlled $2\times2$ framework that evaluates both relational structure and metric geometry at local and global scales. We introduce $H_0$ skeleton overlap as a global counterpart to mutual $k$-nearest neighbors, together with matched distance-aware variants. Across
vision-
language models, relational structure exhibits robust representational convergence at both scales after calibration, whereas increasingly stringent distance agreement substantially weakens
alignment and progressively flattens the capacity-dependent trend. We further extend the analysis beyond ambient Euclidean geometry by evaluating distance agreement under a Riemannian metric approximation and recover the same structure-geometry pattern. The pattern is also reproduced in
video-text representations. Together, these results show that relational convergence extends beyond local neighborhoods to global spanning structure, whereas metric geometry exhibits substantially weaker convergence.
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All authors Junwon You, Mihyun Jang, Sangwoo Mo, Jae-Hun Jung
Computer Vision
Strip Convolution and Direction-Aware Exclusion Loss for Oriented Ship Detection
Bin Chen, Yuanyuan Liu, Peng Yang et al. (+1 more)
Oriented ship detection in very high resolution (VHR) remote sensing imagery remains challenging due to elongated hull geometry and dense target distributions in complex port scenes. Existing methods typically address
geometric representation and duplicate suppression separately. To jointly tackle these issues, we propose an oriented ship detector with two complementary components. The C3k2_Strip module employs orthogonal strip convolutions to better capture elongated hull structures, while the Class-Aware Direction-Aware Exclusion Loss (CA-DAEL) suppresses redundant predictions using class, direction, and confidence cues. Experiments on HRSC2016 and DIOR-R achieve 78.45% and 53.71% mAP50:95, respectively, with only 2.91M parameters. On HRSC2016, the proposed method improves mAP50:95 by 6.32 percentage points over the YOLOv11-OBB baseline, demonstrating its effectiveness for accurate oriented ship detection.
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All authors Bin Chen, Yuanyuan Liu, Peng Yang, Chao Lu
Computer Vision
Surgical Kinematics from Monocular Video with Learned Articulated Motion Constraints
Mehmet Kerem Turkcan, Soham Samal and Zoran Kostic
Objective assessment of robotic surgery uses instrument kinematics, which must be reconstructed when only
video is available. We introduce a kinematic reconstruction network for estimating instrument position, orientation and jaw angle from
monocular video. Our visual representation combines global
attention pooling of frozen DINOv3 features with local pooling at instrument landmarks from fine-tuned SAM 3.1 masks. Our shared
Transformer encoder and temporal convolutional heads integrate this representation with mask geometry,
monocular depth and visual state estimates from arm-specific multilayer regression networks. Our position branch predicts displacement magnitude and direction separately to preserve traveled distance. We fit trajectories to predicted state observations and motion increments by differentiable weighted least squares, expressing quaternion observations relative to cumulative predicted rotations to obtain a quadratic orientation objective. We evaluate reconstruction across 2,802 Open-H episodes. Compared with LiveMAE on the main Open-H
benchmark, our method reduces path-length mean absolute error from 0.45 to 0.34\,cm and increases temporal mean average precision for motion
segmentation from 44.54% to 54.44%.
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