Computer Vision
Attention, May I Have Your Decision? Localizing Generative Choices in Diffusion Models
Katarzyna Zaleska, Łukasz Popek, Monika Wysoczańska et al. (+1 more)
Text-to-image
diffusion models exhibit remarkable generative capabilities, yet their internal operations remain opaque, particularly when handling
prompts that are not fully descriptive. In such scenarios, models must make implicit decisions to generate details not explicitly specified in the text. This work investigates the hypothesis that this decision-making process is not diffuse but is computationally localized within the model's architecture. While existing localization techniques focus on
prompt-related interventions, we notice that such explicit conditioning may differ from implicit decisions. Therefore, we introduce a probing-based localization technique to identify the layers with the highest attribute separability for concepts. Our findings indicate that the resolution of ambiguous concepts is governed principally by self-
attention layers, identifying them as the most effective point for intervention. Based on this discovery, we propose ICM (Implicit Choice-Modification) - a precise steering method that applies targeted interventions to a small subset of layers. Extensive experiments confirm that intervening on these specific self-
attention layers yields superior debiasing performance compared to existing state-of-the-art methods, minimizing artifacts common to less precise approaches. The code is available at https://github.com/kzaleskaa/icm.
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All authors Katarzyna Zaleska, Łukasz Popek, Monika Wysoczańska, Kamil Deja
Computer Vision
MoViD: View-Invariant 3D Human Pose Estimation via Motion-View Disentanglement
Yejia Liu, Hengle Jiang, Haoxian Liu et al. (+2 more)
3D human pose estimation is a key enabling technology for applications such as healthcare monitoring, human-
robot collaboration, and immersive gaming, but real-world deployment remains challenged by viewpoint variations. Existing methods struggle to generalize to unseen camera viewpoints, require large amounts of training data, and suffer from high inference latency. We propose MoViD, a viewpoint-invariant 3D human pose estimation framework that disentangles viewpoint information from motion features. The key idea is to extract viewpoint information from intermediate pose features and leverage it to enhance both the robustness and efficiency of pose estimation. MoViD introduces a view
estimator that models key joint relationships to predict viewpoint information, and an orthogonal projection module to disentangle motion and view features, further enhanced through physics-grounded contrastive
alignment across views. For real-time edge deployment, MoViD employs a frame-by-frame inference pipeline with a view-aware strategy that adaptively activates flip refinement based on the estimated viewpoint. Evaluations on nine public
datasets and newly collected multiview
UAV and gait analysis
datasets show that MoViD reduces pose estimation error by over 24.2% compared to state-of-the-art methods, maintains robust performance under severe occlusions with 60% less training data, and achieves real-time inference at 15 FPS on NVIDIA edge devices.
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All authors Yejia Liu, Hengle Jiang, Haoxian Liu, Runxi Huang, Xiaomin Ouyang
Computer Vision
Spatial Orthogonal Refinement for Robust RGB-Event Visual Object Tracking
Dexing Huang, Shiao Wang, Fan Zhang et al. (+1 more)
Robust visual object tracking (VOT) remains challenging in high-speed motion scenarios, where conventional RGB sensors suffer from severe motion blur and performance degradation. Event cameras, with microsecond temporal resolution and high dynamic range, provide complementary structural cues that can potentially compensate for these limitations. However, existing RGB-Event fusion methods typically treat event data as dense intensity representations and adopt black-box fusion strategies, failing to explicitly leverage the directional
geometric priors inherently encoded in event streams to rectify degraded RGB features. To address this limitation, we propose SOR-Track, a streamlined framework for robust RGB-Event tracking based on Spatial Orthogonal Refinement (SOR). The core SOR module employs a set of orthogonal directional filters that are dynamically guided by local motion orientations to extract sharp and motion-consistent structural responses from event streams. These responses serve as
geometric anchors to modulate and refine aliased RGB textures through an asymmetric structural modulation mechanism, thereby explicitly bridging structural discrepancies between two modalities. Extensive experiments on the large-scale FE108
benchmark demonstrate that SOR-Track consistently outperforms existing fusion-based trackers, particularly under motion blur and low-light conditions. Despite its simplicity, the proposed method offers a principled and physics-grounded approach to multi-modal feature
alignment and texture rectification. The source code of this paper will be released on https://github.com/Event-AHU/OpenEvTracking
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All authors Dexing Huang, Shiao Wang, Fan Zhang, Xiao Wang
Computer Vision
BINO: Encoder Centric Self Supervised Stereo With Native Pair Input
Haokun Zhou
Stereo needs features that preserve fine cross view correspondence rather than only semantic similarity. Recent self supervised
vision models transfer well, but they are not built for this goal, and geometry focused methods often rely on a binocular decoder or another explicit linkage module during pretraining. BINO asks whether strong binocular structure can instead be learned inside a compact encoder. It does this by fusing the rectified pair at the input stage, forming stereo micro
cell tokens, and using a row aware patch phase positional encoding. Training uses one view masked
token only
distillation together with occlusion and view specific appearance mismatch. In a strict low resource setting with pretraining only on KITTI object, BINO gives the best frozen descriptor results under a no linkage probe among all compared baselines on proxy dense stereo, hard negative
retrieval, and KITTI Stereo~2012 disparity. With the same lightweight stereo head for every encoder, it stays near CroCo~v2 while using a much smaller encoder. Supplementary transfer experiments on KITTI Stereo~2015 show the same qualitative trend. These results suggest that much of the cross view
reasoning often assigned to a separate linkage module can be learned inside a compact and reusable encoder.
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Computer Vision
Wei-Yuan Su, Ruijie Zhang and Zheng Zhang
Vision Transformers (ViTs) achieve state-of-the-art performance but suffer from the $O(N^2)$ complexity of self-
attention, making inference costly for high-resolution inputs. To address this bottleneck,
token pruning has emerged as a critical technique to accelerate inference. Most existing methods rely on the [CLS]
token to estimate patch importance. However, we argue that the [CLS]
token can be unreliable in early layers where semantic representations are still immature. As a result,
pruning in the early layer often leads to inaccurate importance estimation and unnecessary information loss. In this work, we propose a training-free
token importance metric, namely Col-Ln, which is derived from Rényi
entropy that enables the identification of informative
tokens from the first layer of the network, thereby enabling more reliable
pruning in
token reduction. Extensive experiments on ViTs and Large
Vision-
Language Models (LVLMs) demonstrate that our approach consistently outperforms state-of-the-art
pruning methods across diverse
benchmarks.
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Computer Vision
SAGE: Sink-Aware Grounded Decoding for Multimodal Hallucination Mitigation
Tripti Shukla and Zsolt Kira
Large
vision-
language models (VLMs) frequently suffer from
hallucinations, generating content that is inconsistent with visual inputs. Existing methods typically address this problem through post-hoc filtering, additional training objectives, or external verification, but they do not intervene during the decoding process when
hallucinations arise. In this work, we introduce SAGE, a Sink-Aware Grounded Decoding framework that mitigates
hallucinations by dynamically modulating self-
attention during generation.
Hallucinations are strongly correlated with
attention sink
tokens - punctuation or function
tokens that accumulate disproportionate
attention despite carrying limited semantic content. SAGE leverages these
tokens as anchors to monitor grounding reliability in real time. At each sink trigger, the method extracts semantic concepts from the generated sequence, estimates their visual grounding using both self-
attention maps and gradient-based attribution, and measures their spatial agreement. Based on this signal, self-
attention distributions are adaptively sharpened or broadened to reinforce grounded regions or suppress unreliable ones. Extensive experiments across diverse
hallucination benchmarks demonstrate that SAGE consistently outperforms existing decoding strategies, achieving substantial reductions in
hallucination while preserving descriptive coverage, without requiring model retraining or architectural modifications. Our method achieves an average relative improvement of 10.65% on MSCOCO and 7.19% on AMBER across diverse VLM architectures, demonstrating consistent gains in
hallucination mitigation.
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Computer Vision
Poppy: Polarization-based Plug-and-Play Guidance for Enhancing Monocular Normal Estimation
Irene Kim, Sai Tanmay Reddy Chakkera, Alexandros Graikos et al. (+2 more)
Monocular surface normal
estimators trained on large-scale RGB-normal data often perform poorly in the edge cases of reflective, textureless, and dark surfaces. Polarization encodes surface orientation independently of texture and albedo, offering a physics-based complement for these cases. Existing polarization methods, however, require multi-view capture or specialized training data, limiting generalization. We introduce Poppy, a training-free framework that refines normals from any frozen RGB backbone using single-shot polarization measurements at test time. Keeping backbone weights frozen, Poppy optimizes per-pixel offsets to the input RGB and output normal along with a learned reflectance decomposition. A differentiable rendering layer converts the refined normals into polarization predictions and penalizes mismatches with the observed signal. Across seven
benchmarks and three backbone architectures (
diffusion, flow, and feed-forward), Poppy reduces mean angular error by 23-26% on synthetic data and 6-16% on real data. These results show that guiding learned RGB-based normal
estimators with polarization cues at test time refines normals on challenging surfaces without retraining.
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All authors Irene Kim, Sai Tanmay Reddy Chakkera, Alexandros Graikos, Dimitris Samaras, Akshat Dave
Computer Vision
Wan-R1: Verifiable-Reinforcement Learning for Video Reasoning
Ming Liu, Yunbei Zhang, Shilong Liu et al. (+2 more)
Video generation models produce visually coherent content but struggle with tasks requiring spatial
reasoning and multi-step planning. Reinforcement learning (RL) offers a path to improve generalization, but its effectiveness in
video reasoning hinges on reward design – a challenge that has received little systematic study. We investigate this problem by adapting Group Relative Policy Optimization (GRPO) to flow-based
video models and training them on maze-solving and robotic
navigation tasks. We first show that
multimodal reward models fail catastrophically in this setting. To address this, we design verifiable reward functions grounded in objective task metrics. For structured game environments, we introduce a multi-component trajectory reward. For robotic
navigation, we propose an
embedding-level verifiable reward. Our experiments show that RL fine-tuning with verifiable rewards improves generalization. For example, on complex 3D mazes, our model improves exact match accuracy by 29.1% over the SFT baseline, and on trap-avoidance tasks by 51.4%. Our systematic reward analysis reveals that verifiable rewards are critical for stable training, while
multimodal reward models could lead to degenerate solutions. These findings establish verifiable reward design as a key enabler for robust
video reasoning. Code will be publicly available.
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All authors Ming Liu, Yunbei Zhang, Shilong Liu, Liwen Wang, Wensheng Zhang
Computer Vision · Cross-listed from cs.GR
ImagenWorld: Stress-Testing Image Generation Models with Explainable Human Evaluation on Open-ended Real-World Tasks
Samin Mahdizadeh Sani, Max Ku, Nima Jamali et al. (+23 more)
Advances in
diffusion, autoregressive, and hybrid models have enabled high-quality image synthesis for tasks such as text-to-image, editing, and reference-guided composition. Yet, existing
benchmarks remain limited, either focus on isolated tasks, cover only narrow domains, or provide opaque scores without explaining failure modes. We introduce ImagenWorld, a
benchmark of 3.6K condition sets spanning six core tasks (generation and editing, with single or multiple references) and six topical domains (artworks, photorealistic images, information graphics, textual graphics, computer graphics, and screenshots). The
benchmark is supported by 20K fine-grained human annotations and an explainable evaluation schema that tags localized object-level and segment-level errors, complementing automated VLM-based metrics. Our large-scale evaluation of 14 models yields several insights: (1) models typically struggle more in editing tasks than in generation tasks, especially in local edits. (2) models excel in artistic and photorealistic settings but struggle with symbolic and text-heavy domains such as screenshots and information graphics. (3) closed-source systems lead overall, while targeted data curation (e.g., Qwen-Image) narrows the gap in text-heavy cases. (4) modern VLM-based metrics achieve Kendall accuracies up to 0.79, approximating human ranking, but fall short of fine-grained, explainable error attribution. ImagenWorld provides both a rigorous
benchmark and a diagnostic tool to advance robust image generation.
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All authors Samin Mahdizadeh Sani, Max Ku, Nima Jamali, Matina Mahdizadeh Sani, Paria Khoshtab, Wei-Chieh Sun, Parnian Fazel, Zhi Rui Tam, Thomas Chong, Edisy Kin Wai Chan, Donald Wai Tong Tsang, Chiao-Wei Hsu, Ting Wai Lam, Ho Yin Sam Ng, Chiafeng Chu, Chak-Wing Mak, Keming Wu, Hiu Tung Wong, Yik Chun Ho, Chi Ruan, Zhuofeng Li, I-Sheng Fang, Shih-Ying Yeh, Ho Kei Cheng, Ping Nie, Wenhu Chen
Computer Vision
3-D Representations for Hyperspectral Flame Tomography
Nicolas Tricard, Zituo Chen and Sili Deng
Flame
tomography is a compelling approach for extracting large amounts of data from experiments via 3-D thermochemical reconstruction. Recent efforts employing neural-network flame representations have suggested improved reconstruction quality compared with classical
tomography approaches, but a rigorous quantitative comparison with the same
algorithm using a voxel-grid representation has not been conducted. Here, we compare a classical voxel-grid representation with varying regularizers to a continuous neural representation for tomographic reconstruction of a simulated pool fire. The representations are constructed to give temperature and composition as a function of location, and a subsequent ray-tracing step is used to solve the radiative transfer equation to determine the spectral intensity incident on hyperspectral infrared cameras, which is then convolved with an instrument lineshape function. We demonstrate that the voxel-grid approach with a total-variation regularizer reproduces the ground-truth synthetic flame with the highest accuracy for reduced
memory intensity and runtime. Future work will explore more representations and under experimental configurations.
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Computer Vision
Benchmarking Multi-View BEV Object Detection with Mixed Pinhole and Fisheye Cameras
Xiangzhong Liu and Hao Shen
Modern autonomous driving systems increasingly rely on mixed camera configurations with pinhole and fisheye cameras for full view perception. However, Bird's-Eye View (BEV) 3D object detection models are predominantly designed for pinhole cameras, leading to performance degradation under fisheye distortion. To bridge this gap, we introduce a multi-view BEV detection
benchmark with mixed cameras by converting KITTI-360 into nuScenes format. Our study encompasses three adaptations: rectification for zero-shot evaluation and fine-tuning of nuScenes-trained models, distortion-aware view transformation modules (VTMs) via the MEI camera model, and polar coordinate representations to better align with radial distortion. We systematically evaluate three representative BEV architectures, BEVFormer, BEVDet and PETR, across these strategies. We demonstrate that projection-free architectures are inherently more robust and effective against fisheye distortion than other VTMs. This work establishes the first real-data 3D detection
benchmark with fisheye and pinhole images and provides systematic adaptation and practical guidelines for designing robust and cost-effective 3D perception systems. The code is available at https://github.com/CesarLiu/FishBEVOD.git.
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Computer Vision
Towards Context-Aware Image Anonymization with Multi-Agent Reasoning
Robert Aufschläger, Jakob Folz, Gautam Savaliya et al. (+3 more)
Street-level imagery contains personally identifiable information (PII), some of which is context-dependent. Existing anonymization methods either over-process images or miss subtle identifiers, while API-based solutions compromise data sovereignty. We present an agentic framework CAIAMAR (\underline{C}ontext-\underline{A}ware \underline{I}mage \underline{A}nonymization with \underline{M}ulti-\underline{A}gent \underline{R}easoning) for context-aware PII
segmentation with
diffusion-based anonymization, combining pre-defined processing for high-confidence cases with multi-
agent reasoning for indirect identifiers. Three specialized
agents coordinate via round-robin speaker selection in a Plan-Do-Check-Act (PDCA) cycle, enabling large
vision-
language models to classify PII based on spatial context (private vs. public property) rather than rigid category rules. The
agents implement spatially-filtered coarse-to-fine detection where a scout-and-zoom strategy identifies candidates, open-vocabulary
segmentation processes localized crops, and $IoU$-based deduplication ($30\%$ threshold) prevents redundant processing. Modal-specific
diffusion guidance with appearance decorrelation substantially reduces re-identification (Re-ID) risks. On CUHK03-NP, our method reduces person Re-ID risk by $73\%$ ($R1$: $16.9\%$ vs. $62.4\%$ baseline). For image quality preservation on CityScapes, we achieve KID: $0.001$, and FID: $9.1$, significantly outperforming existing anonymization. The agentic workflow detects non-direct PII instances across object categories, and downstream semantic
segmentation is preserved. Operating entirely on-premise with open-source models, the framework generates human-interpretable audit trails supporting EU's GDPR transparency requirements while flagging failed cases for human review.
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All authors Robert Aufschläger, Jakob Folz, Gautam Savaliya, Manjitha D Vidanalage, Michael Heigl, Martin Schramm
Computer Vision
Shijian Wang, Jiarui Jin, Runhao Fu et al. (+11 more)
Research
agents have recently achieved significant progress in information seeking and synthesis across heterogeneous textual and visual sources. In this paper, we introduce MuSEAgent, a
multimodal reasoning agent that enhances decision-making by extending the capabilities of research
agents to discover and leverage stateful experiences. Rather than relying on trajectory-level
retrieval, we propose a stateful experience learning paradigm that abstracts interaction data into atomic decision experiences through hindsight
reasoning. These experiences are organized into a quality-filtered experience bank that supports policy-driven experience
retrieval at inference time. Specifically, MuSEAgent enables adaptive experience exploitation through complementary wide- and deep-search strategies, allowing the
agent to dynamically retrieve
multimodal guidance across diverse compositional semantic viewpoints. Extensive experiments demonstrate that MuSEAgent consistently outperforms strong trajectory-level experience
retrieval baselines on both fine-grained visual perception and complex
multimodal reasoning tasks. These results validate the effectiveness of stateful experience modeling in improving
multimodal agent reasoning.
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All authors Shijian Wang, Jiarui Jin, Runhao Fu, Zexuan Yan, Xingjian Wang, Mengkang Hu, Eric Wang, Xiaoxi Li, Kangning Zhang, Li Yao, Wenxiang Jiao, Xuelian Cheng, Yuan Lu, Zongyuan Ge
Computer Vision
Tracking without Seeing: Geospatial Inference using Encrypted Traffic from Distributed Nodes
Sadik Yagiz Yetim, Gaofeng Dong, Isaac-Neil Zanoria et al. (+5 more)
Accurate observation of dynamic environments traditionally relies on synthesizing raw, signal-level information from multiple distributed sensors. This work investigates an alternative approach: performing geospatial inference using only encrypted packet-level information, without access to the raw sensory data. We further explore how this indirect information can be fused with directly available sensory data to extend overall inference capabilities. We introduce GraySense, a learning-based framework that performs geospatial object tracking by analyzing encrypted wireless
video transmission traffic, such as packet sizes, from cameras with inaccessible streams. GraySense leverages the inherent relationship between scene dynamics and transmitted packet sizes to infer object motion. The framework consists of two stages: (1) a Packet Grouping module that identifies frame boundaries and estimates frame sizes from encrypted network traffic, and (2) a Tracker module, based on a
Transformer encoder with a recurrent state, which fuses indirect packet-based inputs with optional direct camera-based inputs to estimate the object's position. Extensive experiments with realistic
videos from the CARLA simulator and emulated networks under varying conditions show that GraySense achieves 2.33 meters tracking error (Euclidean distance) without raw signal access, within the dimensions of tracked objects (4.61m x 1.93m). To our knowledge, this capability has not been previously demonstrated, expanding the use of latent signals for sensing.
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All authors Sadik Yagiz Yetim, Gaofeng Dong, Isaac-Neil Zanoria, Ronit Barman, Maggie Wigness, Tarek Abdelzaher, Mani Srivastava, Suhas Diggavi
Computer Vision · Cross-listed from cs.GR
Engineering Mythology: A Digital-Physical Framework for Culturally-Inspired Public Art
Jnaneshwar Das, Christopher Filkins, Rajesh Moharana et al. (+13 more)
Navagunjara Reborn: The Phoenix of Odisha was built for Burning Man 2025 as both a sculpture and an experiment-a fusion of myth, craft, and computation. This paper describes the digital-physical workflow developed for the project: a pipeline that linked digital sculpting, distributed fabrication by artisans in Odisha (India), modular structural optimization in the U.S., iterative feedback through photogrammetry and digital twins, and finally, one-shot full assembly at the art site in Black Rock Desert, Nevada. The desert installation tested not just materials, but also systems of collaboration: between artisans and engineers, between myth and technology, between cultural specificity and global experimentation. We share the lessons learned in design, fabrication, and deployment and offer a framework for future interdisciplinary projects at the intersection of cultural heritage, STEAM education, and public art. In retrospect, this workflow can be read as a convergence of many knowledge systems-artisan practice, structural engineering, mythic narrative, and environmental constraint-rather than as execution of a single fixed blueprint.
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All authors Jnaneshwar Das, Christopher Filkins, Rajesh Moharana, Ekadashi Barik, Bishweshwar Das, David Ayers, Christopher Skiba, Rodney Staggers Jr, Mark Dill, Swig Miller, Daniel Tulberg, Patrick Smith, Seth Brink, Kyle Breen, Harish Anand, Ramon Arrowsmith
Computer Vision
Diversity Matters: Dataset Diversification and Dual-Branch Network for Generalized AI-Generated Image Detection
Nusrat Tasnim, Kutub Uddin and Khalid Malik
The rapid proliferation of AI-generated images, powered by generative
adversarial networks (GANs),
diffusion models, and other synthesis techniques, has raised serious concerns about misinformation, copyright violations, and digital security. However, detecting such images in a generalized and robust manner remains a major challenge due to the vast diversity of generative models and data distributions. In this work, we present Diversity Matters, a novel framework that emphasizes data diversity and feature domain complementarity for AI-generated image detection. The proposed method introduces a feature-domain similarity filtering mechanism that discards redundant or highly similar samples across both inter-class and intra-class distributions, ensuring a more diverse and representative training set. Furthermore, we propose a dual-branch network that combines CLIP features from the pixel domain and the frequency domain to jointly capture semantic and structural cues, leading to improved generalization against unseen generative models and
adversarial conditions. Extensive experiments on
benchmark datasets demonstrate that the proposed approach significantly improves cross-model and cross-
dataset performance compared to existing methods. Diversity Matters highlights the critical role of data and feature diversity in building reliable and robust detectors against the rapidly evolving landscape of synthetic content.
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Computer Vision
Towards Emotion Recognition with 3D Pointclouds Obtained from Facial Expression Images
Laura Rayón Ropero, Jasper De Laet, Filip Lemic et al. (+6 more)
Facial
Emotion Recognition is a critical research area within Affective Computing due to its wide-ranging applications in Human Computer Interaction, mental health assessment and fatigue monitoring. Current FER methods predominantly rely on Deep Learning techniques trained on 2D image data, which pose significant
privacy concerns and are unsuitable for continuous, real-time monitoring. As an alternative, we propose High-Frequency Wireless Sensing (HFWS) as an enabler of continuous,
privacy-aware FER, through the generation of detailed 3D facial pointclouds via on-person sensors embedded in wearables. We present arguments supporting the
privacy advantages of HFWS over traditional 2D imaging, particularly under increasingly stringent data protection regulations. A major barrier to adopting HFWS for FER is the scarcity of labeled 3D FER
datasets. Towards addressing this issue, we introduce a FLAME-based method to generate 3D facial pointclouds from existing public 2D
datasets. Using this approach, we create AffectNet3D, a 3D version of the AffectNet database. To evaluate the quality and usability of the generated data, we design a pointcloud refinement pipeline focused on isolating the facial region, and train the popular PointNet++ model on the refined pointclouds. Fine-tuning the model on a small subset of the unseen 3D FER
dataset BU-3DFE yields a classification accuracy exceeding 70%, comparable to oracle-level performance. To further investigate the potential of HFWS-based FER for continuous monitoring, we simulate wearable sensing conditions by masking portions of the generated pointclouds. Experimental results show that models trained on AffectNet3D and fine-tuned with just 25% of BU-3DFE outperform those trained solely on BU-3DFE. These findings highlight the viability of our pipeline and support the feasibility of continuous,
privacy-aware FER via wearable HFWS systems.
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All authors Laura Rayón Ropero, Jasper De Laet, Filip Lemic, Pau Sabater Nácher, Nabeel Nisar Bhat, Sergi Abadal, Jeroen Famaey, Eduard Alarcón, Xavier Costa-Pérez
Computer Vision
Inference-time Trajectory Optimization for Structure-Preserving Manga Image Editing
Ryosuke Furuta
We present a lightweight, training-free trajectory correction method that adapts a pretrained image editing model to each input manga image using only the input itself. Despite recent progress in pretrained image editing, such models often underperform on manga because they are trained predominantly on natural-image data, while re-training or fine-tuning them on manga is costly and raises copyright concerns. Many manga image editing tasks encountered in practice are structure-preserving, requiring local details to be modified while the input's global composition is retained. To support this common editing setting, our method corrects the early editing trajectory by anchoring it to an empty-
prompt reconstruction trajectory. Experiments indicate improved performance in the main text-removal setting, while qualitative examples suggest better composition preservation in screentone synthesis. With FLUX.1 Kontext on an RTX A6000, the method incurs 11% runtime overhead and 0.1% peak-
memory overhead; an additional runtime measurement with Qwen Image Edit 2509 on an NVIDIA H200 shows only a 0.1% increase.
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Computer Vision
GS3LAM: Gaussian Semantic Splatting SLAM
Linfei Li, Lin Zhang, Zhong Wang et al. (+1 more)
Recently, the multi-modal fusion of RGB, depth, and semantics has shown great potential in dense Simultaneous Localization and Mapping (SLAM). However, a prerequisite for generating consistent semantic maps is the availability of dense, efficient, and scalable scene representations. Existing semantic SLAM systems based on explicit representations are often limited by resolution and an inability to predict unknown areas. Conversely, implicit representations typically rely on time-consuming ray tracing, failing to meet real-time requirements. Fortunately, 3D Gaussian Splatting (3DGS) has emerged as a promising representation that combines the efficiency of point-based methods with the continuity of
geometric structures. To this end, we propose GS3LAM, a Gaussian Semantic Splatting SLAM framework that processes
multimodal data to render consistent, dense semantic maps in real-time. GS3LAM models the scene as a Semantic Gaussian Field (SG-Field) and jointly optimizes camera poses and the field via
multimodal error constraints. Furthermore, a Depth-adaptive Scale Regularization (DSR) scheme is introduced to resolve misalignments between scale-invariant Gaussians and
geometric surfaces. To mitigate catastrophic forgetting, we propose a Random Sampling-based Keyframe Mapping (RSKM) strategy, which demonstrates superior performance over common local covisibility optimization methods. Extensive experiments on
benchmark datasets show that GS3LAM achieves increased tracking robustness, superior rendering quality, and enhanced semantic precision compared to state-of-the-art methods. Source code is available at https://github.com/lif314/GS3LAM.
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All authors Linfei Li, Lin Zhang, Zhong Wang, Ying Shen
Computer Vision · Cross-listed from cs.CY
Exploring Student Perception on Gen AI Adoption in Higher Education: A Descriptive Study
Harpreet Singh, Jaspreet Singh, Satwant Singh et al. (+4 more)
The rapid proliferation of Generative Artificial Intelligence (GenAI) is reshaping pedagogical practices and assessment models in higher education. While institutional and educator perspectives on GenAI integration are increasingly documented, the student perspective remains comparatively underexplored. This study examines how students perceive, use, and evaluate GenAI within their academic practices, focusing on usage patterns, perceived benefits, and expectations for institutional support. Data were collected through a questionnaire administered to 436 postgraduate Computer Science students at the University of Hertfordshire and analysed using descriptive methods. The findings reveal a Confidence-Competence Paradox: although more than 60% of students report high familiarity with tools such as ChatGPT, daily academic use remains limited and confidence in effective application is only moderate. Students primarily employ GenAI for cognitive scaffolding tasks, including concept clarification and brainstorming, rather than fully automated content generation. At the same time, respondents express concerns regarding data
privacy, reliability of AI-generated information, and the potential erosion of critical thinking skills. The results also indicate strong student support for integrating AI literacy into curricula and programme Knowledge, Skills, and Behaviours (KSBs). Overall, the study suggests that universities should move beyond a policing approach to GenAI and adopt a pedagogical framework that emphasises AI literacy, ethical guidance, and equitable access to AI tools.
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All authors Harpreet Singh, Jaspreet Singh, Satwant Singh, Rupinder Singh, Shamim Ibne Shahid, Mohammad Hassan, Tayarani Najaran
Computer Vision
RINO: Rotation-Invariant Non-Rigid Correspondences
Maolin Gao, Shao Jie Hu-Chen, Congyue Deng et al. (+3 more)
Dense 3D shape correspondence remains a central challenge in computer
vision and graphics as many deep learning approaches still rely on intermediate
geometric features or handcrafted descriptors, limiting their effectiveness under non-isometric deformations, partial data, and non-
manifold inputs. To overcome these issues, we introduce RINO, an unsupervised, rotation-invariant dense correspondence framework that effectively unifies rigid and non-rigid shape matching. The core of our method is the novel RINONet, a feature extractor that integrates vector-based SO(3)-invariant learning with orientation-aware complex functional maps to extract robust features directly from raw geometry. This allows for a fully end-to-end, data-driven approach that bypasses the need for shape pre-
alignment or handcrafted features. Extensive experiments show unprecedented performance of RINO across challenging non-rigid matching tasks, including arbitrary poses, non-isometry, partiality, non-manifoldness, and noise.
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All authors Maolin Gao, Shao Jie Hu-Chen, Congyue Deng, Riccardo Marin, Leonidas Guibas, Daniel Cremers
Computer Vision
When Surfaces Lie: Exploiting Wrinkle-Induced Attention Shift to Attack Vision-Language Models
Chengyin Hu, Xuemeng Sun, Jiaju Han et al. (+5 more)
Visual-
Language Models (VLMs) have demonstrated exceptional cross-modal understanding across various tasks, including zero-shot classification, image captioning, and visual question answering. However, their robustness to physically plausible non-rigid deformations-such as wrinkles on flexible surfaces-remains poorly understood. In this work, we propose a parametric structural perturbation method inspired by the mechanics of three-dimensional fabric wrinkles. Specifically, our method generates photorealistic non-rigid perturbations by constructing multi-scale wrinkle fields and integrating displacement field distortion with surface-consistent appearance variations. To achieve an optimal balance between visual naturalness and
adversarial effectiveness, we design a hierarchical fitness function in a low-dimensional parameter space and employ an optimization-based search strategy. We evaluate our approach using a two-stage framework: perturbations are first optimized on a zero-shot classification proxy task and subsequently assessed for transferability on generative tasks. Experimental results demonstrate that our method significantly degrades the performance of various state-of-the-art VLMs, consistently outperforming baselines in both image captioning and visual question-answering tasks.
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All authors Chengyin Hu, Xuemeng Sun, Jiaju Han, Qike Zhang, Xiang Chen, Xin Wang, Yiwei Wei, Jiahua Long
Computer Vision
RHO: Robust Holistic OSM-Based Metric Cross-View Geo-Localization
Junwei Zheng, Ruize Dai, Ruiping Liu et al. (+7 more)
Metric Cross-View Geo-Localization (MCVGL) aims to estimate the 3-DoF camera pose (position and heading) by matching ground and satellite images. In this work, instead of pinhole and satellite images, we study robust MCVGL using holistic panoramas and OpenStreetMap (OSM). To this end, we establish a large-scale MCVGL
benchmark dataset, CV-RHO, with over 2.7M images under different weather and lighting conditions, as well as sensor noise. Furthermore, we propose a model termed RHO with a two-branch Pin-Pan architecture for accurate visual localization. A Split-Undistort-Merge (SUM) module is introduced to address the panoramic distortion, and a Position-Orientation Fusion (POF) mechanism is designed to enhance the localization accuracy. Extensive experiments prove the value of our CV-RHO
dataset and the effectiveness of the RHO model, with a significant performance gain up to 20% compared with the state-of-the-art baselines. Project page: https://github.com/InSAI-Lab/RHO.
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All authors Junwei Zheng, Ruize Dai, Ruiping Liu, Zichao Zeng, Yufan Chen, Fangjinhua Wang, Kunyu Peng, Kailun Yang, Jiaming Zhang, Rainer Stiefelhagen
Computer Vision
E-TIDE: Fast, Structure-Preserving Motion Forecasting from Event Sequences
Biswadeep Sen, Benoit R. Cottereau, Nicolas Cuperlier et al. (+1 more)
Event-based cameras capture visual information as asynchronous streams of per-pixel brightness changes, generating sparse, temporally precise data. Compared to conventional frame-based sensors, they offer significant advantages in capturing high-speed dynamics while consuming substantially less power. Predicting future event representations from past observations is an important problem, enabling downstream tasks such as future semantic
segmentation or object tracking without requiring access to future sensor measurements. While recent state-of-the-art approaches achieve strong performance, they often rely on computationally heavy backbones and, in some cases, large-scale pretraining, limiting their applicability in resource-constrained scenarios. In this work, we introduce E-TIDE, a lightweight, end-to-end trainable architecture for event-tensor prediction that is designed to operate efficiently without large-scale pretraining. Our approach employs the TIDE module (Temporal Interaction for Dynamic Events), motivated by efficient spatiotemporal interaction design for sparse event tensors, to capture temporal dependencies via large-kernel mixing and activity-aware gating while maintaining low computational complexity. Experiments on standard event-based
datasets demonstrate that our method achieves competitive performance with significantly reduced model size and training requirements, making it well-suited for real-time deployment under tight latency and
memory budgets.
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All authors Biswadeep Sen, Benoit R. Cottereau, Nicolas Cuperlier, Terence Sim
Computer Vision
AI-Powered Facial Mask Removal Is Not Suitable For Identification
Emily A Cooper and Hany Farid
Recently, crowd-sourced online criminal investigations have used generative-AI to enhance low-quality visual evidence. In one high-profile case, social-media users circulated an "AI-unmasked" image of a federal
agent involved in a fatal shooting, fueling a wide-spread misidentification. In response to this and similar incidents, we conducted a large-scale analysis evaluating the efficacy and risks of commercial AI-powered facial unmasking, specifically assessing whether the resulting faces can be reliably matched to true identities.
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Computer Vision
Data Organization Matters in Multimodal Instruction Tuning: A Controlled Study of Capability Trade-offs
Guowei Tang
Recent
multimodal large
language models (MLLMs) perform strongly on general visual understanding, diagram and chart
reasoning, and document-centric perception. However, these abilities are learned from heterogeneous supervision sources with very different task structures and learning demands, and the effect of their temporal organization during training remains underexplored. We study whether data organization affects the trade-off among general understanding, structured
reasoning, and fine-grained OCR/document understanding in
multimodal instruction tuning. To isolate this factor, we use a controlled three-stage training framework in which the backbone, trainable modules, and optimization pipeline are fixed across all runs, and only the temporal arrangement of post-
alignment supervision is changed. We compare four strategies: direct mixture, curriculum training, balanced sampling, and reverse curriculum. Experiments on general visual instruction following, diagram
reasoning, chart
reasoning, scene-text question answering, and document question answering show that data organization is a first-order design variable in
multimodal adaptation. Curriculum training gives the best overall trade-off and the strongest structured
reasoning performance. Balanced sampling is better for OCR-oriented capability but weakens the broader capability balance. Reverse curriculum performs worst in both final performance and optimization stability. Training-dynamics analysis further suggests that building general understanding and
reasoning before introducing OCR-intensive supervision leads to smoother optimization and faster convergence. These findings highlight data scheduling as an explicit design dimension for
multimodal model adaptation.
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Computer Vision
TIR-Agent: Training an Explorative and Efficient Agent for Image Restoration
Guoli Jia, Yisheng Zhang, Haote Hu et al. (+11 more)
Vision-
language agents that orchestrate specialized tools for image restoration (IR) have emerged as a promising method, yet most existing frameworks operate in a training-free manner. They rely on heuristic task scheduling and exhaustive tool traversal, resulting in sub-optimal restoration paths and prohibitive computational cost. We argue that the core bottleneck lies in the absence of a learned policy to make decision, as a
vision-
language model cannot efficiently handle degradation-aware task ordering and tool composition. To this end, we propose TIR-
Agent, a trainable image restoration
agent that performs a direct tool-calling policy through a two-stage training pipeline of supervised fine-tuning (SFT) followed by reinforcement learning (RL). Two key designs underpin effective RL training: (i) a random perturbation strategy applied to the SFT data, which broadens the policy's exploration over task schedules and tool compositions, and (ii) a multi-dimensional adaptive reward mechanism that dynamically re-weights heterogeneous image quality metrics to mitigate reward hacking. To support high-throughput, asynchronous GPU-based tool invocation during training, we further develop a globally shared model-call pool. Experiments on both in-domain and out-of-domain degradations show that TIR-
Agent outperforms 12 baselines, including 6 all-in-one models, 3 training-free
agents, and 3 proprietary models, and achieves over 2.5$\times$ inference speedup by eliminating redundant tool executions.
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All authors Guoli Jia, Yisheng Zhang, Haote Hu, Shanxu Zhao, Kaikai Zhao, Long Sun, Xinwei Long, Kai Tian, Che Jiang, Zhaoxiang Liu, Kai Wang, Shiguo Lian, Kaiyan Zhang, Bowen Zhou
Computer Vision · Cross-listed from astro-ph.GA
Segmenting Superbubbles in a Simulated Multiphase Interstellar Medium using Computer Vision
Jing-Wen Chen, Alex S. Hill, Anna Ordog et al. (+2 more)
We developed a computer
vision-based methodology to achieve precise 3D
segmentation and tracking of superbubbles within magnetohydrodynamic simulations of the
supernova-driven interstellar medium. Leveraging advanced 3D
transformer models, our approach effectively captures the complex morphology and dynamic
evolution of these astrophysical structures. To demonstrate the technique, we specifically focused on a superbubble exhibiting interesting interactions with its surrounding medium, driven by a series of successive
supernova explosions. Our model successfully generated detailed 3D
segmentation masks, enabling us to visualize and analyze the bubble's structural
evolution over time. The results reveal insights into the superbubble's growth patterns, energy retention, and interactions with surrounding interstellar matter. This interdisciplinary approach not only enhances our understanding of superbubble dynamics but also offers a robust framework for investigating other complex phenomena in the cosmos.
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All authors Jing-Wen Chen, Alex S. Hill, Anna Ordog, Rebecca A. Booth, Mohamed S. Shehata
Computer Vision
Synergizing Discriminative Exemplars and Self-Refined Experience for MLLM-based In-Context Learning in Medical Diagnosis
Wenkai Zhao, Zipei Wang, Mengjie Fang et al. (+3 more)
General
Multimodal Large
Language Models (MLLMs) often underperform in capturing domain-specific nuances in medical diagnosis, trailing behind fully supervised baselines. Although fine-tuning provides a remedy, the high costs of expert annotation and massive computational overhead limit its scalability. To bridge this gap without updating the weights of the pre-trained backbone of the MLLM, we propose a Clinician Mimetic Workflow. This is a novel In-Context Learning (ICL) framework designed to synergize Discriminative Exemplar Coreset Selection (DECS) and Self-Refined Experience Summarization (SRES). Specifically, DECS simulates a clinician's ability to reference "anchor cases" by selecting discriminative visual coresets from noisy data at the computational level; meanwhile, SRES mimics the cognition and reflection in clinical diagnosis by distilling diverse rollouts into a dynamic textual Experience Bank. Extensive evaluation across all 12
datasets of the MedMNIST 2D
benchmark demonstrates that our method outperforms zero-shot general and medical MLLMs. Simultaneously, it achieves performance levels comparable to fully supervised
vision models and domain-specific fine-tuned MLLMs, setting a new
benchmark for parameter-efficient medical in-context learning. Our code is available at an anonymous repository: https://anonymous.4open.science/r/Synergizing-Discriminative-Exemplars-and-Self-Refined-Experience-ED74.
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All authors Wenkai Zhao, Zipei Wang, Mengjie Fang, Di Dong, Jie Tian, Lingwei Zhang
Computer Vision
Look, Compare and Draw: Differential Query Transformer for Automatic Oil Painting
Lingyu Liu, Yaxiong Wang, Li Zhu et al. (+2 more)
This work introduces a new approach to automatic oil painting that emphasizes the creation of dynamic and expressive brushstrokes. A pivotal challenge lies in mitigating the duplicate and common-place strokes, which often lead to less aesthetic outcomes. Inspired by the human painting process, \ie, observing, comparing, and drawing, we incorporate differential image analysis into a neural oil painting model, allowing the model to effectively concentrate on the incremental impact of successive brushstrokes. To operationalize this concept, we propose the Differential Query
Transformer (DQ-
Transformer), a new architecture that leverages differentially derived image representations enriched with positional encoding to guide the stroke prediction process. This integration enables the model to maintain heightened sensitivity to local details, resulting in more refined and nuanced stroke generation. Furthermore, we incorporate
adversarial training into our framework, enhancing the accuracy of stroke prediction and thereby improving the overall realism and fidelity of the synthesized paintings. Extensive qualitative evaluations, complemented by a controlled user study, validate that our DQ-
Transformer surpasses existing methods in both visual realism and artistic authenticity, typically achieving these results with fewer strokes. The stroke-by-stroke painting animations are available on our project website.
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All authors Lingyu Liu, Yaxiong Wang, Li Zhu, Lizi Liao, Zhedong Zheng