Computer Vision
Camo-M3FD: A New Benchmark Dataset for Cross-Spectral Camouflaged Pedestrian Detection
Henry O. Velesaca, Andrea Mero, Guillermo A. Castillo et al. (+1 more)
Pedestrian detection is fundamental to autonomous driving, robotics, and surveillance. Despite progress in deep learning, reliable identification remains challenging due to occlusions, cluttered backgrounds, and degraded visibility. While multispectral detection-combining visible and thermal sensors-mitigates poor visibility, the challenge of camouflaged pedestrians remains largely unexplored. Existing Camouflaged Object Detection (COD)
benchmarks focus on biological species, leaving a gap in safety-critical human detection where targets blend into their surroundings. To address this, we introduce Camo-M3FD (derived from the M3FD
dataset), a novel
benchmark for cross-spectral camouflaged pedestrian detection, consisting of registered visible-thermal image pairs. The
dataset is curated using quantitative metrics to ensure high foreground-background similarity. We provide high-quality pixel-level masks and establish a standardized evaluation framework using state-of-the-art COD models. Our results demonstrate that while thermal signals provide indispensable localization cues, multispectral fusion is essential for refining structural details. Camo-M3FD serves as a foundational resource for developing robust and safety-critical detection systems. The
dataset is available on GitHub: https://cod-espol.github.io/Camo-M3FD/
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All authors Henry O. Velesaca, Andrea Mero, Guillermo A. Castillo, Angel D. Sappa
Computer Vision
Multilevel neural networks with dual-stage feature fusion for human activity recognition
Abeer FathAllah Brery, Ascensión Gallardo-Antolín, Israel Gonzalez-Carrasco et al. (+1 more)
Human activity recognition (HAR) refers to the process of identifying human actions and activities using data collected from sensors. Neural networks, such as convolutional neural networks (CNNs), long short-term
memory (LSTM) networks, convolutional LSTM, and their hybrid combinations, have demonstrated exceptional performance in various research domains. Developing a multilevel individual or hybrid model for HAR involves strategically integrating multiple networks to capitalize on their complementary strengths. The structural arrangement of these components is a critical factor influencing the overall performance. This study explores a novel framework of a two-level network architecture with dual-stage feature fusion: late fusion, which combines the outputs from the first network level, and intermediate fusion, which integrates the features from both the first and second levels. We evaluated $15$ different network architectures of CNNs, LSTMs, and convolutional LSTMs, incorporating late fusion with and without intermediate fusion, to identify the optimal configuration. Experimental evaluation on two public
benchmark datasets demonstrates that architectures incorporating both late and intermediate fusion achieve higher accuracy than those relying on late fusion alone. Moreover, the optimal configuration outperforms baseline models, thereby validating its effectiveness for HAR.
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All authors Abeer FathAllah Brery, Ascensión Gallardo-Antolín, Israel Gonzalez-Carrasco, Mahmoud Fakhry
Computer Vision
Classification of systolic murmurs in heart sounds using multiresolution complex Gabor dictionary and vision transformer
Mahmoud Fakhry and Abeer FathAllah Brery
Systolic murmurs are extra heart sounds that occur during the contraction phase of the cardiac cycle, often indicating heart abnormalities caused by turbulent blood flow. Their intensity, pitch, and quality vary, requiring precise identification for the accurate diagnosis of cardiac disorders. This study presents an automatic classification system for systolic murmurs using a feature extraction module, followed by a classification model. The feature extraction module employs complex orthogonal matching pursuit to project single or multiple murmur segments onto a redundant dictionary composed of multiresolution complex Gabor basis functions (GBFs). The resulting projection weights are split and reshaped into variable-resolution time–frequency feature
matrices. Processing multiple segments of a single recording using a shared dictionary mitigates murmur variability. This is achieved by learning the weights for each segment while enforcing that they correspond to the same set of basis functions in the dictionary, promoting consistent time–frequency feature
matrices. The classification model is built based on a
vision transformer to process multiple input
matrices of different resolutions by passing each through a convolutional neural network for patch tokenization. All
embedding tokens are then concatenated to form a
matrix and forwarded to an encoder layer that includes multihead
attention, residual connections, and a convolutional network with a kernel size of one. This integration of multiresolution feature extraction with
transformer-based feature classification enhances the accuracy and reliability of heart murmur identification. An experimental analysis of four types of systolic murmurs from the CirCor DigiScope
dataset demonstrates the effectiveness of the system, achieving a classification accuracy of $95.96\%$.
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Computer Vision
See Through the Noise: Improving Domain Generalization in Gaze Estimation
Yanming Peng, Shijing Wang, Yaping Huang et al. (+1 more)
Generalizable gaze estimation methods have garnered increasing
attention due to their critical importance in real-world applications and have achieved significant progress. However, they often overlook the effect of label noise, arising from the inherent difficulty of acquiring precise gaze annotations, on model generalization performance. In this paper, we are the first to comprehensively investigate the negative effects of label noise on generalization in gaze estimation. Further, we propose a novel solution, called See-Through-Noise (SeeTN) framework, which improves generalization from a novel perspective of mitigating label noise. Specifically, we propose to construct a semantic
embedding space via a prototype-based transformation to preserve a consistent topological structure between gaze features and continuous labels. We then measure feature-label affinity consistency to distinguish noisy from clean samples, and introduce a novel affinity regularization in the semantic
manifold to transfer gaze-related information from clean to noisy samples. Our proposed SeeTN promotes semantic structure
alignment and enforces domain-invariant gaze relationships, thereby enhancing robustness against label noise. Extensive experiments demonstrate that our SeeTN effectively mitigates the adverse impact of source-domain noise, leading to superior cross-domain generalization without compromising the source-domain accuracy, and highlight the importance of explicitly handling noise in generalized gaze estimation.
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All authors Yanming Peng, Shijing Wang, Yaping Huang, Yi Tian
Computer Vision · Cross-listed from cs.LG
S-GRPO: Unified Post-Training for Large Vision-Language Models
Yuming Yan, Kai Tang, Sihong Chen et al. (+4 more)
Current post-training methodologies for adapting Large
Vision-
Language Models (LVLMs) generally fall into two paradigms: Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL). Despite their prevalence, both approaches suffer from inefficiencies when applied in isolation. SFT forces the model's generation along a single expert trajectory, often inducing catastrophic forgetting of general
multimodal capabilities due to distributional shifts. Conversely, RL explores multiple generated trajectories but frequently encounters optimization collapse - a cold-start problem where an unaligned model fails to spontaneously sample any domain-valid trajectories in sparse-reward visual tasks. In this paper, we propose Supervised Group Relative Policy Optimization (S-GRPO), a unified post-training framework that integrates the guidance of imitation learning into the multi-trajectory exploration of preference optimization. Tailored for direct-generation visual tasks, S-GRPO introduces Conditional Ground-Truth Trajectory Injection (CGI). When a binary verifier detects a complete exploratory failure within a sampled group of trajectories, CGI injects the verified ground-truth trajectory into the candidate pool. By assigning a deterministic maximal reward to this injected anchor, S-GRPO enforces a positive signal within the group-relative advantage estimation. This mechanism reformulates the supervised learning objective as a high-advantage component of the policy gradient, compelling the model to dynamically balance between exploiting the expert trajectory and exploring novel visual concepts. Theoretical analysis and empirical results demonstrate that S-GRPO gracefully bridges the gap between SFT and RL, drastically accelerates convergence, and achieves superior domain adaptation while preserving the base model's general-purpose capabilities.
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All authors Yuming Yan, Kai Tang, Sihong Chen, Ke Xu, Dan Hu, Qun Yu, Pengfei Hu
Computer Vision · Cross-listed from cs.LG
LLM as a Tool, Not an Agent: Code-Mined Tree Transformations for Neural Architecture Search
Masakazu Yoshimura, Zitang Sun, Yuiko Sakuma et al. (+3 more)
Neural Architecture Search (NAS) aims to automatically discover high-performing deep neural network (DNN) architectures. However, conventional
algorithm-driven NAS relies on carefully hand-crafted search spaces to ensure executability, which restricts open-ended exploration. Recent coding-based agentic approaches using large
language models (LLMs) reduce manual design, but current LLMs struggle to reliably generate complex, valid architectures, and their proposals are often biased toward a narrow set of patterns observed in their training data. To bridge reliable algorithmic search with powerful LLM assistance, we propose LLMasTool, a hierarchical tree-based NAS framework for stable and open-ended model
evolution. Our method automatically extracts reusable modules from arbitrary source code and represents full architectures as hierarchical trees, enabling
evolution through reliable tree transformations rather than code generation. At each
evolution step, coarse-level planning is governed by a diversity-guided
algorithm that leverages
Bayesian modeling to improve exploration efficiency, while the LLM resolves the remaining degrees of freedom to ensure a meaningful evolutionary trajectory and an executable generated architecture. With this formulation, instead of fully agentic LLM approaches, our method explores diverse directions beyond the inherent biases in the LLM. Our method improves over existing NAS methods by 0.69, 1.83, and 2.68 points on CIFAR-10, CIFAR-100, and ImageNet16-120, demonstrating its effectiveness.
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All authors Masakazu Yoshimura, Zitang Sun, Yuiko Sakuma, Junji Otsuka, Atsushi Irie, Takeshi Ohashi
Computer Vision
PA-TCNet: Pathology-Aware Temporal Calibration with Physiology-Guided Target Refinement for Cross-Subject Motor Imagery EEG Decoding in Stroke Patients
Xiangkai Wang, Yun Zhao, Dongyi He et al. (+5 more)
Stroke patient cross-subject electroencephalography (
EEG) decoding of motor imagery (MI)
brain-computer interface (BCI) is essential for motor rehabilitation, yet lesion-related abnormal temporal dynamics and pronounced inter-patient heterogeneity often undermine generalization. Existing adaptation methods are easily misled by pathological slow-wave activity and unstable target-domain pseudo-labels. To address this challenge, we propose PA-TCNet, a pathology-aware temporal calibration framework with physiology-guided target refinement for stroke motor imagery decoding. PA-TCNet integrates two coordinated components. The Pathology-aware Rhythmic State Mamba (PRSM) module decomposes
EEG spatiotemporal features into slowly varying rhythmic context and fast transient perturbations, injecting the fused pathological context into selective state propagation to more effectively capture abnormal temporal dynamics. The Physiology-Guided Target Calibration (PGTC) module constructs source-domain sensorimotor region-of-interest templates, imposing physiological consistency constraints and dynamically refining target-domain pseudo-labels, thereby improving adaptation reliability. Leave-one-subject-out experiments on two independent stroke
EEG datasets, XW-Stroke and 2019-Stroke, yielded mean accuracies of 66.56% and 72.75%, respectively, outperforming state-of-the-art baselines. These results indicate that jointly modeling pathological temporal dynamics and physiology-constrained pseudo-supervision can provide more robust cross-subject initialization for personalized post-stroke MI-BCI rehabilitation. The implemented code is available at https://github.com/wxk1224/PA-TCNet.
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All authors Xiangkai Wang, Yun Zhao, Dongyi He, Qingling Xia, Gen Li, Nizhuan Wang, Ningxiao Peng, Bin Jiang
Computer Vision
Autoregressive Diffusion for Compositional Text-to-Scene Generation
Zhenggang Tang, Yuehao Wang, Yuchen Fan et al. (+9 more)
Recent text-to-scene generation approaches largely reduced the manual efforts required to create 3D scenes. However, their focus is either to generate a scene layout or to generate objects, and few generate both. The generated scene layout is often simple even with LLM's help. Moreover, the generated scene is often inconsistent with the text input that contains non-trivial descriptions of the shape, appearance, and spatial arrangement of the objects. We present a new paradigm of sequential text-to-scene generation and propose a novel generative model for interactive scene creation. At the core is a 3D Autoregressive
Diffusion model 3D-ARD+, which unifies the autoregressive generation over a
multimodal token sequence and
diffusion generation of next-object 3D latents. To generate the next object, the model uses one autoregressive step to generate the coarse-grained 3D latents in the scene space, conditioned on both the current seen text instructions and already synthesized 3D scene. It then uses a second step to generate the 3D latents in the smaller object space, which can be decoded into fine-grained object geometry and appearance. We curate a large
dataset of 230K indoor scenes with paired text instructions for training. We evaluate 7B 3D-ARD+, on challenging scenes, and showcase the model can generate and place objects following non-trivial spatial layout and semantics prescribed by the text instructions.
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All authors Zhenggang Tang, Yuehao Wang, Yuchen Fan, Jun-Kun Chen, Yu-Ying Yeh, Kihyuk Sohn, Zhangyang Wang, Qixing Huang, Alexander Schwing, Rakesh Ranjan, Dilin Wang, Zhicheng Yan
Computer Vision
A B-Spline Function Based 3D Point Cloud Unwrapping Scheme for 3D Fingerprint Recognition and Identification
Mohammad Mogharen Askarin, Jiankun Hu, Min Wang et al. (+2 more)
Three-dimensional (3D) fingerprint recognition and identification offer several advantages over traditional two-dimensional (2D) recognition systems. The contactless nature of 3D fingerprints enhances hygiene and security, reducing the risk of contamination and spoofing. In addition to surface ridge and valley patterns, 3D fingerprints capture depth,
curvature, and shape information, enabling the development of more precise and robust authentication systems. Despite recent advancements, significant challenges remain. The topological height of fingerprint pixels complicates the extraction of ridge and valley patterns. Furthermore, registration issues limit the acquisition process, requiring consistent direction and orientation across all samples. To address these challenges, this paper introduces a method that unwraps 3D fingerprints, represented as 3D point clouds, using B-spline curve fitting to mitigate height variation and reduce registration limitations. The unwrapped point cloud is then converted into a grayscale image by mapping the relative heights of the points. This grayscale image is subsequently used for recognition through conventional 2D fingerprint identification methods. The proposed approach demonstrated superior performance in 3D fingerprint recognition, achieving Equal Error Rates (EERs) of 0.2072%, 0.26%, and 0.22% across three experiments, outperforming existing methods. Additionally, the method surpassed 3D fingerprint flattening technique in both recognition and identification during cross-session experiments, achieving an EER of 1.50% when fingerprints with varying registrations were included.
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All authors Mohammad Mogharen Askarin, Jiankun Hu, Min Wang, Xuefei Yin, Xiuping Jia
Computer Vision
BOOKAGENT: Orchestrating Safety-Aware Visual Narratives via Multi-Agent Cognitive Calibration
Bo Gao, Chang Liu, Yuyang Miao et al. (+2 more)
Recent advancements in Large Generative Models (LGMs) have revolutionized multi-modal generation. However, generating illustrated storybooks remains an open challenge, where prior works mainly decompose this task into separate stages, and thus, holistic multi-modal grounding remains limited. Besides, while safety
alignment is studied for text- or image-only generation, existing works rarely integrate child-specific safety constraints into narrative planning and sequence-level multi-modal verification. To address these limitations, we propose BookAgent, a safety-aware multi-
agent collaboration framework designed for high-quality, safety-aware visual narratives. Different from prior story visualization models that assume a fixed storyline sequence, BookAgent targets end-to-end storybook synthesis from a user draft by jointly planning, scripting, illustrating, and globally repairing inconsistencies. To ensure precise multi-modal grounding, BookAgent dynamically calibrates page-level
alignment between textual scripts and visual layouts. Furthermore, BookAgent calibrates holistic consistency from the temporal dimension, by verifying-then-rectifying global inconsistencies in character identity and storytelling logic. Extensive experiments demonstrate that BookAgent significantly outperforms current methods in narrative coherence, visual consistency, and safety compliance, offering a robust paradigm for reliable
agents in complex multi-modal creation. The implementation will be publicly released at https://github.com/bogao-code/BookAgent/tree/main.
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All authors Bo Gao, Chang Liu, Yuyang Miao, Siyuan Ma, Ser-Nam Lim
Computer Vision
PoInit-of-View: Poisoning Initialization of Views Transfers Across Multiple 3D Reconstruction Systems
Weijie Wang, Songlong Xing, Zhengyu Zhao et al. (+2 more)
Poisoning input views of 3D reconstruction systems has been recently studied. However, we identify that existing studies simply backpropagate
adversarial gradients through the 3D reconstruction pipeline as a whole, without uncovering the new vulnerability rooted in specific modules of the 3D reconstruction pipeline. In this paper, we argue that the structure-from-motion (SfM) initialization, as the
geometric core of many widely used reconstruction systems, can be targeted to achieve transferable poisoning effects across diverse 3D reconstruction systems. To this end, we propose PoInit-of-View, which optimizes
adversarial perturbations to intentionally introduce cross-view gradient inconsistencies at projections of corresponding 3D points. These inconsistencies disrupt keypoint detection and feature matching, thereby corrupting pose estimation and triangulation within SfM, eventually resulting in low-quality rendered views. We also provide a theoretical analysis that connects cross-view inconsistency to correspondence collapse. Experimental results demonstrate the effectiveness of our PoInit-of-View on diverse 3D reconstruction systems and
datasets, surpassing the single-view baseline by 25.1% in PSNR and 16.5% in SSIM in black-box transfer settings, such as 3DGS to NeRF.
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All authors Weijie Wang, Songlong Xing, Zhengyu Zhao, Nicu Sebe, Bruno Lepri
Computer Vision
Repurposing 3D Generative Model for Autoregressive Layout Generation
Haoran Feng, Yifan Niu, Zehuan Huang et al. (+3 more)
We introduce LaviGen, a framework that repurposes 3D generative models for 3D layout generation. Unlike previous methods that infer object layouts from textual descriptions, LaviGen operates directly in the native 3D space, formulating layout generation as an autoregressive process that explicitly models
geometric relations and physical constraints among objects, producing coherent and physically plausible 3D scenes. To further enhance this process, we propose an adapted 3D
diffusion model that integrates scene, object, and instruction information and employs a dual-guidance self-rollout
distillation mechanism to improve efficiency and spatial accuracy. Extensive experiments on the LayoutVLM
benchmark show LaviGen achieves superior 3D layout generation performance, with 19% higher physical plausibility than the state of the art and 65% faster computation. Our code is publicly available at https://github.com/fenghora/LaviGen.
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All authors Haoran Feng, Yifan Niu, Zehuan Huang, Yang-Tian Sun, Yuxin Peng, Lu Sheng
Computer Vision
FineCog-Nav: Integrating Fine-grained Cognitive Modules for Zero-shot Multimodal UAV Navigation
Dian Shao, Zhengzheng Xu, Peiyang Wang et al. (+4 more)
UAV vision-
language navigation (VLN) requires an
agent to navigate complex 3D environments from an
egocentric perspective while following ambiguous multi-step instructions over long horizons. Existing zero-shot methods remain limited, as they often rely on large base models, generic
prompts, and loosely coordinated modules. In this work, we propose FineCog-Nav, a top-down framework inspired by human cognition that organizes
navigation into fine-grained modules for
language processing, perception,
attention,
memory, imagination,
reasoning, and decision-making. Each module is driven by a moderate-sized foundation model with role-specific
prompts and structured input-output protocols, enabling effective collaboration and improved interpretability. To support fine-grained evaluation, we construct AerialVLN-Fine, a curated
benchmark of 300 trajectories derived from AerialVLN, with sentence-level instruction-trajectory
alignment and refined instructions containing explicit visual endpoints and landmark references. Experiments show that FineCog-Nav consistently outperforms zero-shot baselines in instruction adherence, long-horizon planning, and generalization to unseen environments. These results suggest the effectiveness of fine-grained cognitive modularization for zero-shot aerial
navigation. Project page: https://smartdianlab.github.io/projects-FineCogNav.
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All authors Dian Shao, Zhengzheng Xu, Peiyang Wang, Like Liu, Yule Wang, Jieqi Shi, Jing Huo
Computer Vision
Enhancing Hazy Wildlife Imagery: AnimalHaze3k and IncepDehazeGan
Shivarth Rai and Tejeswar Pokuri
Atmospheric haze significantly degrades wildlife imagery, impeding computer
vision applications critical for conservation, such as animal detection, tracking, and behavior analysis. To address this challenge, we introduce AnimalHaze3k a synthetic
dataset comprising of 3,477 hazy images generated from 1,159 clear wildlife photographs through a physics-based pipeline. Our novel IncepDehazeGan architecture combines inception blocks with residual skip connections in a GAN framework, achieving state-of-the-art performance (SSIM: 0.8914, PSNR: 20.54, and LPIPS: 0.1104), delivering 6.27% higher SSIM and 10.2% better PSNR than competing approaches. When applied to downstream detection tasks, dehazed images improved YOLOv11 detection mAP by 112% and IoU by 67%. These advances can provide ecologists with reliable tools for population monitoring and surveillance in challenging environmental conditions, demonstrating significant potential for enhancing wildlife conservation efforts through robust visual analytics.
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Computer Vision
VEFX-Bench: A Holistic Benchmark for Generic Video Editing and Visual Effects
Xiangbo Gao, Sicong Jiang, Bangya Liu et al. (+12 more)
As AI-assisted
video creation becomes increasingly practical, instruction-guided
video editing has become essential for refining generated or captured footage to meet professional requirements. Yet the field still lacks both a large-scale human-annotated
dataset with complete editing examples and a standardized evaluator for comparing editing systems. Existing resources are limited by small scale, missing edited outputs, or the absence of human quality labels, while current evaluation often relies on expensive manual inspection or generic
vision-
language model judges that are not specialized for editing quality. We introduce VEFX-
Dataset, a human-annotated
dataset containing 5,049
video editing examples across 9 major editing categories and 32 subcategories, each labeled along three decoupled dimensions: Instruction Following, Rendering Quality, and Edit Exclusivity. Building on VEFX-
Dataset, we propose VEFX-Reward, a reward model designed specifically for
video editing quality assessment. VEFX-Reward jointly processes the source
video, the editing instruction, and the edited
video, and predicts per-dimension quality scores via ordinal regression. We further release VEFX-Bench, a
benchmark of 300 curated
video-
prompt pairs for standardized comparison of editing systems. Experiments show that VEFX-Reward aligns more strongly with human judgments than generic VLM judges and prior reward models on both standard IQA/VQA metrics and group-wise preference evaluation. Using VEFX-Reward as an evaluator, we
benchmark representative commercial and open-source
video editing systems, revealing a persistent gap between visual plausibility, instruction following, and edit locality in current models. Our project page is https://xiangbogaobarry.github.io/VEFX-Bench/.
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All authors Xiangbo Gao, Sicong Jiang, Bangya Liu, Xinghao Chen, Minglai Yang, Siyuan Yang, Mingyang Wu, Jiongze Yu, Qi Zheng, Haozhi Wang, Jiayi Zhang, Jie Yang, Zihan Wang, Qing Yin, Zhengzhong Tu
Computer Vision
Hero-Mamba: Mamba-based Dual Domain Learning for Underwater Image Enhancement
Tejeswar Pokuri and Shivarth Rai
Underwater images often suffer from severe degradation, such as color distortion, low contrast, and blurred details, due to light absorption and scattering in water. While learning-based methods like CNNs and
Transformers have shown promise, they face critical limitations: CNNs struggle to model the long-range dependencies needed for non-uniform degradation, and
Transformers incur quadratic computational complexity, making them inefficient for high-resolution images. To address these challenges, we propose Hero-Mamba, a novel Mamba-based network that achieves efficient dual-domain learning for
underwater image enhancement. Our approach uniquely processes information from both the spatial domain (RGB image) and the spectral domain (FFT components) in parallel. This dual-domain input allows the network to decouple degradation factors, separating color/brightness information from texture/noise. The core of our network utilizes Mamba-based SS2D blocks to capture global receptive fields and long-range dependencies with linear complexity, overcoming the limitations of both CNNs and
Transformers. Furthermore, we introduce a ColorFusion block, guided by a background light prior, to restore color information with high fidelity. Extensive experiments on the LSUI and UIEB
benchmark datasets demonstrate that Hero-Mamba outperforms state-of-the-art methods. Notably, our model achieves a PSNR of 25.802 and an SSIM of 0.913 on LSUI, validating its superior performance and generalization capabilities.
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Computer Vision
Information Router for Mitigating Modality Dominance in Vision-Language Models
Seulgi Kim, Mohit Prabhushankar and Ghassan AlRegib
Vision Language models (VLMs) have demonstrated strong performance across a wide range of
benchmarks, yet they often suffer from modality dominance, where predictions rely disproportionately on a single modality. Prior approaches primarily address this issue by steering model's
attention allocation, implicitly assuming that all modalities provide sufficient information. However,
attention only determines where the model focuses, and cannot enrich information that is missing or ambiguous. In the real world, input modalities often differ in information density and their signal-to-noise ratios. In such cases, simply adjusting model's
attention does not resolve the underlying lack of information. In this paper, we propose MoIR: Multi-modal Information Router, an information-level fusion method that explicitly reduces information disparity prior to fusion. MoIR identifies less informative
tokens and routes complementary information from a stronger modality, constructing information-dense
token representations before they are processed by a large
language model. By modifying information availability, MoIR enables reliable shifts in modality dominance, even when one modality is degraded. We evaluate MoIR on three widely used multi-modal
benchmarks across multiple model backbones. Experimental results show that MoIR consistently demonstrates more balanced modality contribution, and improves robustness and downstream performance, particularly even under modality degradation. These findings demonstrate that explicitly modifying cross-modal information is an effective and complementary strategy for mitigating modality dominance in multi-modal
reasoning models.
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Computer Vision
Do Vision-Language Models Truly Perform Vision Reasoning? A Rigorous Study of the Modality Gap
Yige Xu, Yongjie Wang, Zizhuo Wu et al. (+3 more)
Reasoning in
vision-
language models (VLMs) has recently attracted significant
attention due to its broad applicability across diverse downstream tasks. However, it remains unclear whether the superior performance of VLMs stems from genuine
vision-grounded
reasoning or relies predominantly on the
reasoning capabilities of their textual backbones. To systematically measure this, we introduce CrossMath, a novel
multimodal reasoning benchmark designed for controlled cross-modal comparisons. Specifically, we construct each problem in text-only, image-only, and image+text formats guaranteeing identical task-relevant information, verified by human annotators. This rigorous
alignment effectively isolates modality-specific
reasoning differences while eliminating confounding factors such as information mismatch. Extensive evaluation of state-of-the-art VLMs reveals a consistent phenomenon: a substantial performance gap between textual and visual
reasoning. Notably, VLMs excel with text-only inputs, whereas incorporating visual data (image+text) frequently degrades performance compared to the text-only baseline. These findings indicate that current VLMs conduct
reasoning primarily in the textual space, with limited genuine reliance on visual evidence. To mitigate this limitation, we curate a CrossMath training set for VLM fine-tuning. Empirical evaluations demonstrate that fine-tuning on this training set significantly boosts
reasoning performance across all individual and joint modalities, while yielding robust gains on two general visual
reasoning tasks. Source code is available at https://github.com/xuyige/CrossMath.
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All authors Yige Xu, Yongjie Wang, Zizhuo Wu, Kaisong Song, Jun Lin, Zhiqi Shen
Computer Vision
Where Do Vision-Language Models Fail? World Scale Analysis for Image Geolocalization
Siddhant Bharadwaj, Ashish Vashist, Fahimul Aleem et al. (+1 more)
Image geolocalization has traditionally been addressed through
retrieval-based place recognition or geometry-based visual localization pipelines. Recent advances in
Vision-
Language Models (VLMs) have demonstrated strong zero-shot
reasoning capabilities across
multimodal tasks, yet their performance in geographic inference remains underexplored. In this work, we present a systematic evaluation of multiple state-of-the-art VLMs for country-level image geolocalization using ground-view imagery only. Instead of relying on image matching, GPS metadata, or task-specific training, we evaluate
prompt-based country prediction in a zero-shot setting. The selected models are tested on three geographically diverse
datasets to assess their robustness and generalization ability. Our results reveal substantial variation across models, highlighting the potential of semantic
reasoning for coarse geolocalization and the limitations of current VLMs in capturing fine-grained geographic cues. This study provides the first focused comparison of modern VLMs for country-level geolocalization and establishes a foundation for future research at the intersection of
multimodal reasoning and geographic understanding.
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All authors Siddhant Bharadwaj, Ashish Vashist, Fahimul Aleem, Shruti Vyas
Computer Vision
Find, Fix, Reason: Context Repair for Video Reasoning
Haojian Huang, Chuanyu Qin, Yinchuan Li et al. (+1 more)
Reinforcement learning has advanced
video reasoning in large multi-modal models, yet dominant pipelines either rely on on-policy self-exploration, which plateaus at the model's knowledge boundary, or hybrid replay that mixes policies and demands careful regularization. Dynamic context methods zoom into focused evidence but often require curated pretraining and two-stage tuning, and their context remains bounded by a small model's capability. In contrast, larger models excel at instruction following and multi-modal understanding, can supply richer context to smaller models, and rapidly zoom in on target regions via simple tools. Building on this capability, we introduce an observation-level intervention: a frozen, tool-integrated teacher identifies the missing spatiotemporal dependency and provides a minimal evidence patch (e.g., timestamps, regions etc.) from the original
video while the question remains unchanged. The student answers again with the added context, and training updates with a chosen-rollout scheme integrated into Group Relative Policy Optimization (GRPO). We further propose a Robust Improvement Reward (RIR) that aligns optimization with two goals: outcome validity through correct answers and dependency
alignment through rationales that reflect the cited evidence. Advantages are group-normalized across the batch, preserving on-policy exploration while directing it along causally meaningful directions with minimal changes to the training stack. Experiments on various related
benchmarks show consistent accuracy gains and strong generalization. Code will be available at https://jethrojames.github.io/FFR/.
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All authors Haojian Huang, Chuanyu Qin, Yinchuan Li, Yingcong Chen
Computer Vision
CollideNet: Hierarchical Multi-scale Video Representation Learning with Disentanglement for Time-To-Collision Forecasting
Nishq Poorav Desai, Ali Etemad and Michael Greenspan
Time-to-Collision (TTC)
forecasting is a critical task in collision prevention, requiring precise temporal prediction and comprehending both local and global patterns encapsulated in a
video, both spatially and temporally. To address the multi-scale nature of
video, we introduce a novel spatiotemporal hierarchical
transformer-based architecture called CollideNet, specifically catered for effective TTC
forecasting. In the spatial stream, CollideNet aggregates information for each
video frame simultaneously at multiple resolutions. In the temporal stream, along with multi-scale feature encoding, CollideNet also disentangles the non-stationarity, trend, and seasonality components. Our method achieves state-of-the-art performance in comparison to prior works on three commonly used public
datasets, setting a new state-of-the-art by a considerable margin. We conduct cross-
dataset evaluations to analyze the generalization capabilities of our method, and visualize the effects of disentanglement of the trend and seasonality components of the
video data. We release our code at https://github.com/DeSinister/CollideNet/.
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Computer Vision
A Two-Stage, Object-Centric Deep Learning Framework for Robust Exam Cheating Detection
Van-Truong Le, Le-Khanh Nguyen and Trong-Doanh Nguyen
Academic integrity continues to face the persistent challenge of examination cheating. Traditional invigilation relies on human observation, which is inefficient, costly, and prone to errors at scale. Although some existing AI-powered monitoring systems have been deployed and trusted, many lack transparency or require multi-layered architectures to achieve the desired performance. To overcome these challenges, we propose an improvement over a simple two-stage framework for exam cheating detection that integrates object detection and behavioral analysis using well-known technologies. First, the state-of-the-art YOLOv8n model is used to localize students in exam-room images. Each detected region is cropped and preprocessed, then classified by a fine-tuned RexNet-150 model as either normal or cheating behavior. The system is trained on a
dataset compiled from 10 independent sources with a total of 273,897 samples, achieving 0.95 accuracy, 0.94 recall, 0.96 precision, and 0.95 F1-score - a 13% increase over a baseline accuracy of 0.82 in
video-based cheating detection. In addition, with an average inference time of 13.9 ms per sample, the proposed approach demonstrates robustness and scalability for deployment in large-scale environments. Beyond the technical contribution, the AI-assisted monitoring system also addresses ethical concerns by ensuring that final outcomes are delivered privately to individual students after the examination, for example, via personal email. This prevents public exposure or shaming and offers students an opportunity to reflect on their behavior. For further improvement, it is possible to incorporate additional factors, such as audio data and consecutive frames, to achieve greater accuracy. This study provides a foundation for developing real-time, scalable, ethical, and open-source solutions.
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Computer Vision
Dental Panoramic Radiograph Analysis Using YOLO26 From Tooth Detection to Disease Diagnosis
Khawaja Azfar Asif and Rafaqat Alam Khan
Panoramic radiography is a fundamental diagnostic tool in dentistry, offering a comprehensive view of the entire dentition with minimal radiation exposure. However, manual interpretation is time-consuming and prone to errors, especially in high-volume clinical settings. This creates a pressing need for efficient automated solutions. This study presents the first application of YOLOv26 for automated tooth detection, FDI-based numbering, and dental disease
segmentation in panoramic radiographs. The DENTEX
dataset was preprocessed using Roboflow for format conversion and augmentation, yielding 1,082 images for tooth enumeration and 1,040 images for disease
segmentation across four pathology classes. Five YOLOv26-seg variants were trained on Google Colab using transfer learning at a resolution of 800x800. Results demonstrate that the YOLOv26m-seg model achieved the best performance for tooth enumeration, with a precision of 0.976, recall of 0.970, and box mAP50 of 0.976. It outperformed the YOLOv8x baseline by 4.9% in precision and 3.3% in mAP50, while also enabling high-quality mask-level
segmentation (mask mAP50 = 0.970). For disease
segmentation, the YOLOv26l-seg model attained a box mAP50 of 0.591 and a mask mAP50 of 0.547. Impacted teeth showed the highest per-class average precision (0.943), indicating that visual distinctiveness influences detection performance more than annotation quantity. Overall, these findings demonstrate that YOLOv26-based models offer a robust and accurate framework for automated dental image analysis, with strong potential to enhance diagnostic efficiency and consistency in clinical practice.
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Computer Vision
GAViD: A Large-Scale Multimodal Dataset for Context-Aware Group Affect Recognition from Videos
Deepak Kumar, Abhishek Pratap Singh, Puneet Kumar et al. (+2 more)
Understanding affective dynamics in real-world social systems is fundamental to modeling and analyzing human-human interactions in complex environments. Group affect emerges from intertwined human-human interactions, contextual influences, and behavioral cues, making its quantitative modeling a challenging computational social systems problem. However, computational modeling of group affect in in-the-wild scenarios remains challenging due to limited large-scale annotated
datasets and the inherent complexity of
multimodal social interactions shaped by contextual and behavioral variability. The lack of comprehensive
datasets annotated with
multimodal and contextual information further limits advances in the field. To address this, we introduce the Group Affect from
ViDeos (GAViD)
dataset, comprising 5091
video clips with
multimodal data (
video, audio and context), annotated with ternary valence and discrete
emotion labels and enriched with VideoGPT-generated contextual metadata and human-annotated action cues. We also present Context-Aware Group Affect Recognition Network (CAGNet) for
multimodal context-aware group affect recognition. CAGNet achieves 63.20% test accuracy on GAViD, comparable to state-of-the-art performance. The
dataset and code are available at github.com/deepakkumar-iitr/GAViD.
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All authors Deepak Kumar, Abhishek Pratap Singh, Puneet Kumar, Xiaobai Li, Balasubramanian Raman
Computer Vision
AIFIND: Artifact-Aware Interpreting Fine-Grained Alignment for Incremental Face Forgery Detection
Hao Wang, Beichen Zhang, Yanpei Gong et al. (+5 more)
As forgery types continue to emerge consistently, Incremental Face Forgery Detection (IFFD) has become a crucial paradigm. However, existing methods typically rely on data replay or coarse binary supervision, which fails to explicitly constrain the feature space, leading to severe feature drift and catastrophic forgetting. To address this, we propose AIFIND, Artifact-Aware Interpreting Fine-Grained
Alignment for Incremental Face Forgery Detection, which leverages semantic anchors to stabilize incremental learning. We design the Artifact-Driven Semantic Prior Generator to instantiate invariant semantic anchors, establishing a fixed coordinate system from low-level artifact cues. These anchors are injected into the image encoder via Artifact-Probe
Attention, which explicitly constrains volatile visual features to align with stable semantic anchors. Adaptive Decision Harmonizer harmonizes the classifiers by preserving angular relationships of semantic anchors, maintaining
geometric consistency across tasks. Extensive experiments on multiple incremental protocols validate the superiority of AIFIND.
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All authors Hao Wang, Beichen Zhang, Yanpei Gong, Shaoyi Fang, Zhaobo Qi, Yuanrong Xu, Xinyan Liu, Weigang Zhang
Computer Vision · Cross-listed from cs.RO
DENALI: A Dataset Enabling Non-Line-of-Sight Spatial Reasoning with Low-Cost LiDARs
Nikhil Behari, Diego Rivero, Luke Apostolides et al. (+3 more)
Consumer
LiDARs in mobile devices and
robots typically output a single depth value per pixel. Yet internally, they record full time-resolved histograms containing direct and multi-bounce light returns; these multi-bounce returns encode rich non-line-of-sight (NLOS) cues that can enable perception of hidden objects in a scene. However, severe hardware limitations of consumer
LiDARs make NLOS reconstruction with conventional methods difficult. In this work, we motivate a complementary direction: enabling NLOS perception with low-cost
LiDARs through data-driven inference. We present DENALI, the first large-scale real-world
dataset of space-time histograms from low-cost
LiDARs capturing hidden objects. We capture time-resolved
LiDAR histograms for 72,000 hidden-object scenes across diverse object shapes, positions, lighting conditions, and spatial resolutions. Using our
dataset, we show that consumer
LiDARs can enable accurate, data-driven NLOS perception. We further identify key scene and modeling factors that limit performance, as well as simulation-fidelity gaps that hinder current sim-to-real transfer, motivating future work toward scalable NLOS
vision with consumer
LiDARs.
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All authors Nikhil Behari, Diego Rivero, Luke Apostolides, Suman Ghosh, Paul Pu Liang, Ramesh Raskar
Computer Vision
Saturation-Aware Space-Variant Blind Image Deblurring
Muhammad Z. Alam, Larry Stetsiuk and Arooba Zeshan
This paper presents a novel saturation aware space variant blind image deblurring framework designed to address challenges posed by saturated pixels in deblurring under high dynamic range and low light conditions. The proposed approach effectively segments the image based on blur intensity and proximity to saturation, leveraging a pre estimated Light Spread Function to mitigate stray light effects. By accurately estimating the true radiance of saturated regions using the dark channel prior, our method enhances the deblurring process without introducing artifacts like ringing. Experimental evaluations on both synthetic and real world
datasets demonstrate that the framework improves deblurring outcomes across various scenarios showcasing superior performance compared to state of the art saturation-aware and general purpose methods. This adaptability highlights the framework potential integration with existing and emerging blind image deblurring techniques.
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Computer Vision
Winner of CVPR2026 NTIRE Challenge on Image Shadow Removal: Semantic and Geometric Guidance for Shadow Removal via Cascaded Refinement
Lorenzo Beltrame, Jules Salzinger, Filip Svoboda et al. (+4 more)
We present a three-stage progressive shadow-removal pipeline for the CVPR2026 NTIRE WSRD+ challenge. Built on OmniSR, our method treats deshadowing as iterative direct refinement, where later stages correct residual artefacts left by earlier predictions. The model combines RGB appearance with frozen DINOv2 semantic guidance and
geometric cues from
monocular depth and surface normals, reused across all stages. To stabilise multi-stage optimisation, we introduce a contraction-constrained objective that encourages non-increasing reconstruction error across the cascade. A staged training pipeline transfers from earlier WSRD pretraining to WSRD+ supervision and final WSRD+ 2026 adaptation with cosine-annealed checkpoint ensembling. On the official WSRD+ 2026 hidden test set, our final ensemble achieved 26.680 PSNR, 0.8740 SSIM, 0.0578 LPIPS, and 26.135 FID, ranked first overall, and won the NTIRE 2026 Image Shadow Removal Challenge. The strong performance of the proposed model is further validated on the ISTD+ and
UAV-SC+
datasets.
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All authors Lorenzo Beltrame, Jules Salzinger, Filip Svoboda, Jasmin Lampert, Phillipp Fanta-Jende, Radu Timofte, Marco Körner
Computer Vision · Cross-listed from cs.AI
MARCH: Multi-Agent Radiology Clinical Hierarchy for CT Report Generation
Yi Lin, Yihao Ding, Yonghui Wu et al. (+1 more)
Automated 3D radiology report generation often suffers from clinical
hallucinations and a lack of the iterative verification found in human practice. While recent
Vision-
Language Models (VLMs) have advanced the field, they typically operate as monolithic "black-box" systems without the collaborative oversight characteristic of clinical workflows. To address these challenges, we propose MARCH (Multi-
Agent Radiology Clinical Hierarchy), a multi-
agent framework that emulates the professional hierarchy of radiology departments and assigns specialized roles to distinct
agents. MARCH utilizes a Resident
Agent for initial drafting with multi-scale CT feature extraction, multiple Fellow
Agents for
retrieval-augmented revision, and an Attending
Agent that orchestrates an iterative, stance-based consensus discourse to resolve diagnostic discrepancies. On the RadGenome-ChestCT
dataset, MARCH significantly outperforms state-of-the-art baselines in both clinical fidelity and linguistic accuracy. Our work demonstrates that modeling human-like organizational structures enhances the reliability of AI in high-stakes medical domains.
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All authors Yi Lin, Yihao Ding, Yonghui Wu, Yifan Peng
Computer Vision
neuralCAD-Edit: An Expert Benchmark for Multimodal-Instructed 3D CAD Model Editing
Toby Perrett, Matthew Bouchard and William McCarthy
We introduce neuralCAD-Edit, the first
benchmark for editing 3D CAD models collected from expert CAD engineers. Instead of text conditioning as in prior works, we collect realistic CAD editing requests by capturing
videos of professional designers, interacting directly with CAD models in CAD software, while talking, pointing and drawing. We recruited ten consenting designers to contribute to this contained study. We
benchmark leading foundation models against human CAD experts carrying out edits, and find a large performance gap in both automatic metrics and human evaluations. Even the best foundation model (GPT 5.2) scores 53% lower (absolute) than CAD experts in human acceptance trials, demonstrating the challenge of neuralCAD-Edit. We hope neuralCAD-Edit will provide a solid foundation against which 3D CAD editing approaches and foundation models can be developed. Code/data: https://autodeskailab.github.io/neuralCAD-Edit
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