Computer Vision
Homer: Understanding Long-form Videos with Hierarchical Memory and Agentic Reasoning
Yixin Ji, Fanghua Ye, Juntao Li et al. (+5 more)
Multimodal large
language models excel on short clips but struggle on hour-long
videos in an online setting, where frames are processed incrementally under limited
memory. Existing online methods either retain compact visual representations that lack semantic structure, or build higher-level
memory stores organized around temporal proximity rather than explicit causal links, leaving multi-hop narrative
reasoning to be reconstructed by the LLM at every query. We bridge this gap with Homer, a Hierarchical Online
Memory Exploration and
Reasoning framework. Homer's
memory mirrors the multi-scale structure of long
videos, ranging from raw perception, to recurring entities, to events connected by explicit temporal and causal relations. Its agentic reasoner then explores this
memory the way humans do, locating the relevant scene, looking up details, and composing the answer through multi-round
memory retrieval, with a harness that verifies and corrects each step. Homer outperforms the previous best
agent method by $+5.5$, $+10.8$, and $+4.4$ points on M3-Bench-
robot, M3-Bench-web, and
Video-MME-Long, and consistently lifts three various LLM backbones, indicating a model-agnostic structural capability for grounded
retrieval over long
videos.
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All authors Yixin Ji, Fanghua Ye, Juntao Li, Bo Zhao, Zexuan Qiu, Zhaopeng Tu, Liefeng Bo, Min Zhang
Computer Vision
Reliability-Aware CT-MRI Registration: A Quality Engineering Framework with Stability Analysis and Risk Classification
Nisreen Albzour
Multimodal CT-MRI registration is central to image-guided radiotherapy,
surgical navigation, and diagnostic workflows, but most pipelines report only aggregate quality metrics without per-case reliability signals. We propose a reliability-aware framework that converts registration quality into Green/Yellow/Red risk categories using data-learned thresholds. CT images were registered to T1-weighted MRI using rigid and affine transformations on 90 paired slices from 18 patients across
brain, abdominal, and neck anatomies. Reliability was assessed using Delta NMI, Delta SSIM, Dice overlap, registration stability, and inverse consistency error, combined into a single score R. Thresholds learned from training patients were applied unchanged to held-out test patients. Affine registration outperformed rigid registration on NMI and SSIM, yielding 44% Green classifications versus 33% for rigid. Reliability-filtered registrations improved the average
alignment profile compared with unfiltered methods. Per-anatomy analysis showed substantial variation, with stronger reliability for abdominal registrations than
brain registrations. Weight sensitivity analysis identified Dice overlap as the dominant reliability component. The proposed framework provides an interpretable quality-control layer for
multimodal registration, while risk thresholds reflect statistical rather than clinical validation.
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Computer Vision
RotateAttention: RoPE-Aware Rotation and Range Rectification for INT4 Quantized Attention in Video Generation
Yaofu Liu, Wanli Lan, Jinxi Li et al. (+2 more)
In $\textbf{DiT-based video generation models equipped with 3D Rotary Position Embeddings (3D RoPE)}$, the
attention mechanism remains a primary computational bottleneck due to its quadratic complexity with respect to sequence length. While quantized $\textbf{FlashAttention}$ offers a promising path toward hardware acceleration, existing low-bit quantization methods overlook two critical challenges in this setting: $\textbf{1)}$ applying online rotation
matrices – a widely used technique for mitigating outliers in Queries ($Q$) and Keys ($K$) – is difficult to reconcile with $\textbf{RoPE}$; and $\textbf{2)}$ the non-negative
attention matrix $P = \exp(QK - \max(QK))$ makes symmetric quantization waste half of the 4-bit dynamic range. In this work, we observe that the outlier distributions of $Q$ and $K$ are strongly affected by the dimensional partitioning of $\textbf{3D RoPE}$. Based on this finding, we propose $\textbf{RotateAttention}$, an efficient $\textbf{mixed-precision INT4 FlashAttention}$ framework tailored for $\textbf{DiT-based video generation models with 3D RoPE}$, using selective $\textbf{FP16 fallback}$ for accuracy-sensitive
attention blocks and denoising steps. RotateAttention introduces two core techniques: $\textbf{1) RoPE-aware Rotation}$, which employs either mergeable rotation
matrices that can be fused into RoPE or negligible-overhead
matrices to mitigate RoPE-induced outliers in $Q$ and $K$; and $\textbf{2) Range-optimized $P$ Quantization}$, which uses fixed scales and zero-points to fully exploit the $\textbf{INT4 numerical range}$ with minimal computational overhead. Experiments show that $\textbf{RotateAttention}$ preserves
video generation quality nearly identical to full-precision baselines while achieving up to 1.68$\times$ end-to-end speedup and 2.2$\times$ kernel-level acceleration.
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All authors Yaofu Liu, Wanli Lan, Jinxi Li, Binhang Yuan, Harry Yang
Computer Vision
Evaluating Intellectual Property Guardrails of Generative Image Models: A Technical Report
Austin T. Hoag, Apostolos Modas, Yunhao Ba et al. (+9 more)
Generative image models are capable of producing images that bear a strong resemblance to, or replicate, recognizable intellectual property (IP). In this technical report, we present a
benchmark and automated evaluation pipeline to test for evidence of IP guardrails in generative image models along with the propensity for these models to generate images with recognizable IP. The IP categories we tested include fictional characters, celebrity likeness, and commercial logos and do not encompass the full range of IP which may be implicated by image generation models. We evaluated fourteen widely used text-to-image models, including three self-hosted open weights models and eleven private models. While all of the private models were observed to refuse generations at some level due to IP guardrails, the frequency of generation refusals varied substantially among models. The refusal rates also varied considerably across the different IP categories tested. Commercial logos were refused least frequently and were successfully generated at the highest rate, on average. Though the rate varies, all models tested readily generated images containing recognizable IP as of March 2026.
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All authors Austin T. Hoag, Apostolos Modas, Yunhao Ba, Julienne M. LaChance, Jinru Xue, Wiebke Hutiri, Jan Simson, Tiffany Georgievski, Alex Towli, Joseph Smith, Yuki Mitsufuji, Alice Xiang
Computer Vision
ESC: Emotional Self-Correction for Reliable Vision-Language Models
Tien-Huy Nguyen, Minh-Nhat Nguyen, Nguyen Nhat Huy et al. (+9 more)
Vision-
language models (VLMs) have achieved strong performance across diverse
multimodal tasks, yet they remain vulnerable to unreliable
reasoning. Existing self-correction methods mitigate these issues but typically rely on post-training or carefully engineered feedback, incurring high computational cost. In this work, we revisit this challenge through the lens of emotional cues, asking whether they can activate latent self-correction behaviors in VLMs without additional training. We find that emotional signals serve as an effective trigger for self-correction, encouraging more cautious and reflective
reasoning. Motivated by this finding, we propose \escabstract (\textbf{\underline{E}}motional \textbf{\underline{S}}elf-\textbf{\underline{C}}orrection), a training-free self-correction framework. ESC introduces an external verifier that detects potentially incorrect initial responses and injects emotional feedback to encourage model to reflect, and produce a better revised response without additional training. Extensive experiments across safety,
hallucination,
vision-centric perception, and
multimodal reasoning benchmarks show that ESC consistently improves reliability while preserving overall model utility. These results suggest that
emotion can function not only as an ability to be recognized, but also as a practical control signal for scalable self-correction in VLMs. We therefore believe that ESC provides a strong foundation for a new reliable human-like,
emotion-integrated research direction. Our project is publicly available at \textcolor{red}{https://genai4e.github.io/ESC/}.
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All authors Tien-Huy Nguyen, Minh-Nhat Nguyen, Nguyen Nhat Huy, Hung Viet Nguyen, Huy Nguyen Minh Nhat, Thanh-Huy Nguyen, Cuong Tuan Nguyen, Hoang M. Le, Dat Nguyen, Phat Kim Huynh, Min Xu, Ulas Bagci
Computer Vision
Hidden-Shot: Towards One-Shot Task Generalization for Low-Level Vision Generalist Models
Shao-Jun Xia, Xianzheng Ma and Zichong Meng
Despite the intense engagement surrounding low-level
vision generalist models, their effectiveness in zero/few-shot scenarios beyond learned tasks remains unverified. The primary challenge of developing an ideal generalist lies in achieving the ability to generalize from new unseen tasks, which also can be assessed by matched quantitative criteria. Existing methods have made some progress in
prompt engineering but have not systematically explored this gap across a wide range of low-level visual tasks. Stimulated by the problem, we propose Hidden-Shot, an implicit
prompt mechanism aimed at exploring low-level task adaptation in a
vision generalist model. Specifically, the method extracts implicit visual task-based information, utilizes a global task-aware textural
prompt, and selectively merges implicit information with in-task processing information to enhance one-shot capabilities in new tasks. The overall design performs direct injection in a cost-effective manner, while minimally altering the architecture of the original generalist model. Additionally, we introduce a data-driven evaluation framework termed C/U assessment to cover two basic scenarios, 3C4U (3 conventional and 4 unconventional tasks) for retraining existing models and 3C7U (3 conventional and 7 unconventional tasks) for training from scratch, as a comprehensive assessment to systematically test the generalization ability of low-level generalist models. Experiments on seven and ten
datasets outperform the state-of-the-art
vision generalist model, respectively verified by 3C4U and 3C7U framework. Our presented Hidden-Shot approach demonstrates superior performance on one-shot new tasks while maintaining consistent performance on existing tasks.
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Computer Vision
Disentangling Pictorial Cue Understanding from Language Bias in VLMs via Depth Ordering Task
Yiqian Liu, Iuliia Kotseruba and John K. Tsotsos
In this paper, we study depth perception of
vision-
language models (VLMs) to isolate the effects of pictorial depth cues and disentangle
vision and
language influences on model performance. To this end, we combine depth-ordering and odd-one-out psychophysical tasks: the VLMs are presented with images where one object is at different depth relative to other, otherwise identical, objects, and must determine whether the odd-one-out target is closer or farther to the observer. To create stimuli, we generate 2D views from simulated and real 3D scenes while controlling the presence of individual pictorial depth cues, enabling a fine-grained analysis of cue-level contributions.
Language effects are examined by varying referring expression clarity. We also introduce a novel metric to quantify
vision-vs-
language sensitivities. Applying this methodology, we create the Odd-One-Out Depth (O3-D)
dataset with 37K real and synthetic images and 147K image-question pairs. Evaluation of 12 open-source and commercial models on O3-D shows under-utilization of depth cues and depth-ordering accuracies between 47% and 56%, with no model above chance level. At the same time, our metric reveals strong linguistic bias in the answers. Neither chain-of-thought (CoT) nor in-context learning (ICL) significantly improves performance, suggesting that static image data alone may be insufficient for depth understanding. All code, the image generation pipeline, and the O3-D
dataset are publicly released at https://github.com/lyiqian/o3-d.
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Computer Vision
Anti-Prompt: Image Protection against Text-Guided Image-to-Video Generation
Yeonghwan Song, Chanhui Lee, Jinsoo Park et al. (+1 more)
Recent advances in Image-to-
Video generation allow a single image to be animated into a convincing
video under text guidance, raising serious copyright and
privacy risks. We propose Anti-
Prompt, an image protection approach that injects imperceptible perturbations into an image, inducing visible inconsistencies and structural failures in text-guided I2V generation. Our method is motivated by a simple empirical observation. When text guidance is removed from modern I2V models, generation quality degrades markedly, not only in motion realism but also in subject preservation, structural coherence, and temporal consistency. Building on this insight, Anti-
Prompt exploits the model reliance on textual guidance by attenuating text-conditioned interactions during denoising while strengthening visual-only pathways. To further systematically evaluate protection effectiveness, we introduce a
Video-LLM-assisted evaluation protocol that provides interpretable, frame-grounded analyses of generation artifacts and inconsistencies. Experiments on two representative I2V architectures demonstrate that our method achieves strong protection performance while improving efficiency and cross-model transferability.
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All authors Yeonghwan Song, Chanhui Lee, Jinsoo Park, Jeany Son
Computer Vision · Cross-listed from math.OC
Boundary-Aware Quantization: Finite-Scale Decision Geometry of Neural Classifiers
O.M. Kiselev
We measured quantization-induced decision-boundary changes using local logit-margin radii, first-order boundary displacement, normal variation, slice-boundary Jaccard distance, grid prediction changes, multiclass junction counts, and low-margin boundary-band flips. On the digits
benchmark, 8-bit weight quantization preserved all test labels while producing boundary-mask Jaccard \(0.428\) on the PCA slice; at 4 bits, accuracy remained \(0.9733\), while boundary Jaccard rose to \(0.970\) and median local boundary shift reached \(0.0290\). Interpolation between adjacent quantization levels localized the visible reconfigurations at multiclass junctions, with 12, 34, and 17 triple-junction
cells in the selected transitions. Calibration-to-test stopping reduced the digits held-out flip rate from \(0.0094\) to \(0.0022\) and boundary Jaccard from \(0.825\) to \(0.524\); the same stopping rule also reduced flips on MNIST and Fashion-MNIST. On official CIFAR-10 subsets, PTQ-W selected by accuracy gave 6-bit flip \(0.0367\) and boundary Jaccard \(0.184\), whereas boundary-aware stopping selected 8-bit flip \(0.0083\) and boundary Jaccard \(0.048\). On full CIFAR-10 with three seeds, 6-bit PTQ-W lost \(0.0029\) accuracy relative to float, changed \(5.3\%\) of held-out decisions, and changed \(24.5\%\) of low-margin boundary-band decisions. A fixed-bit boundary-gap rounding term changed the trade-off at 4 bits by reducing boundary Jaccard from \(0.457\) to \(0.435\) and boundary-band pair-order flip from \(0.3600\) to \(0.3558\), with an accuracy trade-off; the 3-bit stress test exposed the tuning limit of this surrogate. Calibration boundary Jaccard predicted held-out boundary Jaccard across PTQ-W and optimized rounding variants with \(r=0.947\)–\(0.994\).
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Computer Vision
Accuracy and Cost Claims Do Not Survive Re-Execution in Agentic VideoQA
Rama AlHamidi, Aseel Mohamed, Rasul Khanbayov et al. (+3 more)
Agentic
Video Question Answering (VideoQA) systems produce answers through adaptive
reasoning and tool-use trajectories, yet standard practice evaluates each system once and estimates uncertainty only across questions. This leaves a basic question untested: would the measured method effect survive if the evaluation were run again? We show that it need not. Using Static-SAGE and Dynamic-SAGE as a controlled case study, we repeat the paired comparison twice on identical SAGE-Bench question-
video pairs, holding configuration, tool library, and scoring protocol fixed. In the first execution, Dynamic-SAGE outperforms Static-SAGE by +7.33 accuracy points; in the second, the effect reverses to -4.05. Both are individually significant under paired analysis, supporting opposite conclusions. The change in the paired effect between executions is highly significant and far larger than within-execution uncertainty. The reversal is consistent across question format, modality, difficulty, and
video duration, and both evaluation arms move significantly. Motivated by this failure, we introduce REPAIR (REpeated PAired Inference Reliability), a protocol that repeats the paired comparison and tests whether the method effect changes across executions, separating directional reproducibility from effect-size stability. Applied across accuracy and execution metrics, REPAIR exposes three behaviors-directional reversal, magnitude shift, and effect attenuationand shows that reductions in
reasoning turns and visible tool calls do not imply reproducible reductions in primitive computation or latency. The execution-level movement is comparable to, and here larger than, median gain reported by recent agentic VideoQA systems, contextualizing its magnitude without implying those systems are unstable. Significance within a single agentic execution is insufficient evidence that a reported method effect is reproducible.
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All authors Rama AlHamidi, Aseel Mohamed, Rasul Khanbayov, Mohamed Rayan Barhdadi, Erchin Serpedin, Hasan Kurban
Computer Vision · Cross-listed from cs.CR
From Forgeries to Foundation Models: A Systematic Survey of Identity Document Attack and Detection
Gourab Das, Pavan Kumar C and Raghavendra Ramachandra
Identity document forgery has undergone a fundamental capability shift: generative AI tools now enable high-fidelity document synthesis and field-level manipulation with minimal technical expertise, while detection methods remain constrained by
benchmarks that do not reflect this threat. The resulting attack surface spans physical presentation, digital injection, and fully generative synthesis, introducing distinct forensic failure modes that require a unified threat model and evaluation framework. This survey provides, to our knowledge, the first unified treatment of Presentation Attacks, Digital Injection Attacks, and GenAI-driven synthesis within a single identity verification threat model. We trace detection methodologies from rule-based heuristics through forensic localisation, injection-aware pipelines, foundation models, and few-shot frameworks. A systematic audit of public
datasets from 2019–2025 exposes a persistent Reality Gap between
benchmark conditions and operational deployment. We further analyse large
multimodal models for identity document manipulation, identifying Script-Dependent Generative Instability (SDGI) as a recurring typographic failure mode in non-Latin script inpainting. Finally, zero-shot benchmarking on unseen synthesised ID cards shows that even the strongest publicly available models achieve APCER values above 25% under security-oriented operating conditions, highlighting substantial limits in cross-domain generalisation. We conclude by outlining future directions toward forensically grounded,
privacy-preserving, and legally accountable identity verification systems.
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Computer Vision
How Much Future Helps? A Controlled Study of Future-Privileged Supervision for Causal Egocentric Gaze Estimation
Jia Li, Wenjie Zhao, Fnu Atisri et al. (+5 more)
Egocentric gaze estimation is commonly studied using models that process the full
video with access to future frames, while real-world applications require strictly causal, online prediction. This discrepancy raises key questions: Does future context inherently provide valuable signals for gaze estimation? If so, how much future look-ahead optimally supervises a causal model during training? To investigate, we propose a controlled framework featuring a future-aware branch that accesses a tunable look-ahead horizon during training but is discarded at inference. This design isolates the impact of future context while keeping the inference architecture fixed and strictly causal. Across EGTEA Gaze+ and Ego4D, we find that future-privileged supervision consistently improves causal gaze prediction, confirming its utility. However, performance gains do not increase monotonically with longer look-ahead, but rather peak within a bounded temporal regime. Specifically, optimal performance corresponds to roughly 1.7–3.3 seconds of future context ($H{\in}[5, 10]$) on EGTEA Gaze+ and 2.7 seconds ($H{=}10$) on Ego4D. Our results demonstrate that lightweight causal models can effectively absorb future-aware signals, providing practical guidance for real-time
egocentric gaze modeling.
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All authors Jia Li, Wenjie Zhao, Fnu Atisri, Sanskriti Aripineni, Shijian Deng, Jon E. Froehlich, Yuhang Zhao, Yapeng Tian
Computer Vision
Sign in the Air to Unlock: An Interface for authentication in Virtual and Augmented Reality Powered by Point-Voxel Cross-Attention Network
Neda Abdolrahimi, Thiru Siddharth, Frank Sicong chen et al. (+1 more)
Significant advancement of immersive technologies such as Virtual and Augmented Reality (VR/AR) and their integration into diverse aspects of modern life need authentication interfaces that are secure, intuitive, and compatible with
embodied interaction. Traditional methods such as passwords, PINs, and device-based logins, break immersion and rely on external hardware. Recent 3D-specific behavioral approaches, such as hand-gesture, eye-tracking, and electroencephalography (
EEG)-based methods, offer promising alternatives but often require specialized sensors or constrain natural movement, limiting usability in dynamic environments. We present Sign in the Air to Unlock, an in-air signature interface that enables users to authenticate by signing naturally in 3D space which is a familiar, personal, and reproducible gesture. To realize this interface, we design a point-voxel Cross-
Attention Network (PV-Net) that jointly models local motion dynamics and global spatial structure from 3D trajectories. The model is evaluated on two
datasets: the public DeepAirSig
dataset (1,800 signatures from 40 users) and ImmAirsig, a new
dataset collected using Meta Quest 2 in immersive VR (880 samples from 22 users). PV-Net achieves an Equal Error Rate of 2.5% on DeepAirSig and 76% classification accuracy on ImmAirSig. These findings highlight the potential of 3D behavioral interfaces for seamless, user-centric authentication that merges security with natural interaction in immersive environments.
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All authors Neda Abdolrahimi, Thiru Siddharth, Frank Sicong chen, Vir V Phoha
Computer Vision · Cross-listed from cs.CL
MultAttnAttrib: Training-Free Multimodal Attribution in Long Document Question Answering
Dang Quang Thien Tran, Quang V. Dang, Vinamra Tyagi et al. (+7 more)
As grounded QA systems are increasingly deployed in AI assistants, accurately attributing generated answers to evidence is critical for user trust and model safety. While unimodal attributions have been explored in depth, the
multimodal setting remains relatively under-researched. As a result, we introduce MultAttnAttrib, a training-free attribution-generation method that leverages a model's prefill pass, selected
attention heads, and calibrated thresholds to locate source evidence within a document. To establish baseline results for the method, we introduce MultAttrEval, a complementary
benchmark dataset annotated with fine-grained, ground-truth attributions for answer components grounded in
multimodal source documents. To our knowledge, this is the first evaluation
dataset designed specifically for
multimodal attribution in long-form documents. Experimental results show that MultAttnAttrib consistently outperforms a variety of attribution-generation methods, including several strong prompting-based approaches and matches the latest frontier models such as GPT 5.4. Our method not only substantially improves attribution accuracy for both unimodal and
multimodal attribution types, but also produces attributions at up to one-seventh of the direct inference latency compared to prompting on the same base model.
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All authors Dang Quang Thien Tran, Quang V. Dang, Vinamra Tyagi, Sai Soorya Rao Veeravalli, Trang Nguyen, Ryan A. Rossi, Franck Dernoncourt, Nedim Lipka, Koustava Goswami, Samyadeep Basu
Computer Vision
Beyond Heatmaps: Unsupervised Concept-Graph Reasoning for Interpretable Visual Explanation
Md Mohasin Hossain, Anar Amirli, Robert Leist et al. (+2 more)
Concept Bottleneck Models (CBMs) provide an intrinsically interpretable alternative to post-hoc explanations. However, existing CBMs often rely on predefined concept vocabularies or supervised annotations, lack explicit concept grounding, and summarize each concept with a single image-level score – discarding spatial recurrence and inter-concept dependencies. We propose a Graph-based Concept Bottleneck Model (G-CBM), an intrinsically interpretable framework that performs unsupervised concept discovery via Non-negative
Matrix Factorization (NMF) and represents the discovered concepts as nodes in a per-image concept-graph representation. G-CBM matches region-level features to these concept nodes – providing concept grounding and capturing concept recurrence across the image – and applies a tunable concept filtering threshold $\tau$ to suppress weak region-level features. A Graph
Attention Network (GAT) then performs concept-level
reasoning by modeling nonlinear dependencies across nodes. Across ImageNet, HAM10000, PH2, and Derm7pt, G-CBM achieves an average relative AUC improvement of 3.7% over a ResNet-50 baseline. Concept filtering frequently improves predictive performance while inducing selective concept use, achieving peak AUC of $0.96$ on PH2 with only 2 of 10 concepts and 0.92 on HAM10000 with 3.8 of 9 concepts. On dermoscopy
benchmarks, G-CBM is competitive with supervised approaches requiring external annotations. Deletion/insertion analyses with random ablation controls show that the learned concept ranking faithfully reflects model predictions.
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All authors Md Mohasin Hossain, Anar Amirli, Robert Leist, Md Abdul Kadir, Daniel Sonntag
Computer Vision · Cross-listed from cs.LG
NeuroBridge: Bridging Multi-Task MRI Knowledge for Neurodegenerative Disease Diagnosis
Mengyu Li, Guoyao Shen, Chad W. Farris et al. (+1 more)
Accurate MRI-based identification of Alzheimer's disease (AD), mild cognitive impairment (MCI), and related dementias remains challenging because disease-related structural changes are often subtle and heterogeneous. We developed NeuroBridge, a clinically guided multi-task MRI framework for neurodegenerative disease diagnosis. NeuroBridge integrates large-scale self-supervised MRI pretraining with hippocampal
segmentation, hippocampal atrophy classification, and reconstruction objectives, followed by gated fusion fine-tuning. Performance was evaluated across ADNI and OASIS cohorts, including cross-cohort transfer, probability-based analysis, and opportunistic screening. NeuroBridge achieved the highest performance across evaluated classification tasks, reaching 88.17% accuracy for AD versus cognitively normal controls in ADNI and 82.78% in OASIS. The largest gains occurred in MCI-related and mixed-diagnosis settings. The framework demonstrated strong cross-cohort generalization, systematic associations between predicted-class probability and accuracy, and the feasibility of probability-based opportunistic screening. Clinically guided multi-task representation learning improves neurodegenerative MRI diagnosis beyond conventional single-task approaches. NeuroBridge provides a robust and scalable framework for dementia assessment and MRI-based opportunistic screening.
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All authors Mengyu Li, Guoyao Shen, Chad W. Farris, Xin Zhang
Computer Vision
Computer Vision for Wildlife Monitoring: Detecting Brown Howler Monkeys using YOLO
Gabriel Ferri Schneider, Guido Luis Glufke Mainardi, Paulo Ricardo Knob et al. (+4 more)
Urban expansion threatens global biodiversity, especially affecting arboreal species due to the fragmentation of forest habitats. The movement of arboreal species across disjointed forest patches increases mortality risk and, thus, compromises their conservation. In this context, the installation of canopy bridges can be a viable strategy; yet continuous monitoring of their use by arboreal species is essential for ensuring their effectiveness, typically carried out with the aid of camera traps. However, this method often produces false-positive images that demand time from conservationists for review. In this context, computer
vision algorithms can optimize the task of detecting target species using the canopy bridges. In this study, we explored the automatic detection of brown howler monkeys (Alouatta guariba) in
videos obtained by camera traps. Given the need for a large number of annotated images of the target animals to train the
algorithms, we tested the incorporation of auxiliary data to improve detection models, fine-tuning the YOLOv10 framework using varying proportions of them. The improvement of these automatic detection techniques contributes to conservation efforts, by providing automatic tools to monitor solutions that minimize the impact of human interference in animals habitats.
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All authors Gabriel Ferri Schneider, Guido Luis Glufke Mainardi, Paulo Ricardo Knob, Patrícia Dias, Márcia Jardim, Júlio César Bicca-Marques, Soraia Raupp Musse
Computer Vision
Rethinking Generic Object Tracking Toward Human-Level Perceptual Intelligence
Shih-Fang Chen
At the heart of human visual perception lies the ability to maintain a continuous and coherent understanding of the external world. By integrating observations with accumulated experience, the human visual system can continuously adapt to variations in both the target and its surrounding environment, while preserving robust visual continuity as scene dynamics evolve. Human
vision can therefore integrate prior knowledge, spatial geometry, and semantic context to understand complex scenes and their changes. As a core problem in computer
vision, visual object tracking aims to bring machine perception closer to human visual perception. These capabilities are central to the task of Generic Object Tracking (GOT). In this task, a visual tracker is initialized only with the bounding box of an arbitrarily specified target in the first frame, and must continuously localize the target in subsequent dynamic visual streams. However, future events, observations, and real-world variations are inherently unpredictable; therefore, the model's generalization and online adaptation capabilities remain bottlenecks. Tracking reliability can deteriorate when the target undergoes severe deformation, is affected by complex distractors, encounters significant environmental changes, or belongs to a category unseen during training. This dissertation aims to narrow the gap between machine visual tracking systems and human visual perception by proposing a series of methods that systematically enhance the target discrimination, robust adaptation, and
geometric reasoning capabilities of tracking models.
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Computer Vision
MIBE: Multi-subject Interaction Benchmark and Evaluator for Personalized Image Generation
Zhihan Chen, Yuhuan Zhao, Yijie Zhu et al. (+7 more)
Multi-subject personalized image generation requires the precise rendering of all requested reference identities and their specified interactions based on a guiding
prompt. However, state-of-the-art models still struggle with this process, frequently omitting subjects, failing to preserve reference appearances, or misattributing interactions. Furthermore, existing metrics designed primarily for single-subject fidelity cannot reliably capture these errors, suffering severe degradation in ranking separability and failing to align with human preference as the subject count increases. To address this gap, we introduce Multi-subject Interaction
Benchmark and Evaluator (MIBE), a unified framework comprising a Multi-subject Interaction
Benchmark (MIB) and a Multi-subject Interaction Evaluator (MIE). MIB systematically covers diverse relation types and scene complexities through a decoupled data regime. This consists of a 60K-pair VLM-labeled Silver Set for scalable metric training and a 4K-pair double-blind Human Evaluation Gold Set covering a diverse range of state-of-the-art generators, with the Silver Set reaching 95.1% cross-VLM preference agreement. To demonstrate the utility of this
benchmark, we present MIE, a lightweight, reference-conditioned evaluator trained exclusively on the Silver Set with a dual-head ranking and diagnosis objective. MIE exhibits strong cross-generator generalization on the Gold Set, achieving 0.922 overall pairwise accuracy against human preference, including 0.982 on seen generators and 0.884 on unseen generators. By outperforming a broad spectrum of baseline metrics, including CLIP and DINO variants, MIE demonstrates that diagnostic supervision can preserve ranking separability and human
alignment where traditional evaluators collapse.
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All authors Zhihan Chen, Yuhuan Zhao, Yijie Zhu, Xinyu Yao, Mengcong Ren, Suwen Wang, Qiuyang Yin, Yuchen Sun, Qin Wang, Lu Xin
Computer Vision
MapDreamer: Aerial Imagery Conditioned Latent Diffusion for Lane-Level Map Generation
Julian Brandes, Philipp Crocoll and Wolfram Burgard
High definition map generation is essential for autonomous driving, yet remains a labor-intensive process at scale. We present MapDreamer, a generative
diffusion model that synthesizes lane-level vector maps with explicit
topology directly from a single aerial image. MapDreamer learns a compact latent representation of lane centerlines and their topological relations using a variational autoencoder and predicts graphs with a
transformer-based latent
diffusion model. To align generated maps with the observed scene, we condition each denoising step on dense aerial features injected through cross-
attention. To handle the varying number of lanes across scenes, we propose a lane cardinality module paired with background ghost lane latents, a learned buffer that prevents slot collapse during
diffusion. Furthermore, we introduce a sliding-window global graph aggregation strategy that stitches local tiles into city-scale maps while preserving connectivity through encoded lane boundaries. Experiments on UrbanLaneGraph derived from Argoverse 2 show improved
geometric and topological fidelity over non-generative baselines.
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Computer Vision · Cross-listed from cs.LG
Multi-modal Rail Crossing Safety Analysis
Paimon Goulart, Chansong Lim, Nícolas Roque dos Santos et al. (+4 more)
Given one or more images of a railway crossing, can we leverage visual cues that allow us to robustly estimate how safe it is? Can we improve our ability to do so by introducing structured data (such as official accident reports) about the accident history of that crossing into our models? In this work, we explore how to best answer those questions towards building an AI system that can ingest multi-modal data for railway crossings and provide safety assessment and scores that align with expert opinion and with safety scoring used by the Federal Railroad Administration (FRA). To that end, we propose a proof-of-concept pipeline that delivers on that goal, while at the same time exploring and tackling a number of critical research challenges that pertain to different parts of the pipeline, from data preparation to different learning paradigms that can allow us to realize such a system. Indicatively, our proposed system identifies HIGH-RISK and LOW-RISK crossings with a macro F1 score of 0.757 and estimates FRA-based safety scores with an RMSE of 0.078 and correlation of 0.492 using a routed fine-tuned compact VLM pipeline, while producing qualitative results that align with domain-expert assessment.
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All authors Paimon Goulart, Chansong Lim, Nícolas Roque dos Santos, Yue Dong, Sheldon Peterson, Jia Chen, Evangelos E. Papalexakis
Computer Vision
Spatial-Temporal Expert Learning for Video-based Person Re-identification
Xiaofei Hui, Pengfei Wang, Evan Ling et al. (+4 more)
Video-based person re-identification (Re-ID) aims to retrieve the same identity in the query
video clips from the gallery
video clips. To solve this problem, exploiting fine-grained features is of great importance, especially when discriminating identities that are similar in appearance. In this paper, we propose to enhance the ability to explore fine-grained information with a novel input-aware extendable expert module. Instead of updating the network parameters with every sample in the
dataset, we aim to train the experts within specific subsets that only contain similar samples and promote their ability to exploit fine-grained information within these similar samples. To achieve this goal, we incorporate two mechanisms in this module: input-aware expert selection mechanism and spatial-temporal selection mechanism. The first mechanism dynamically activates a set of experts on subsets of similar samples, pushing the experts to exploit subtle differences between these similar samples, while the second one further increases their sensitivity to the fine-grained differences in spatial and temporal aspects and allows the experts to dynamically utilize them for different input samples. In addition, to facilitate the expert module, we design an extendable scheme that allows the module to flexibly add new experts when necessary. As a result, our method achieves outstanding performance on two large-scale
datasets.
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All authors Xiaofei Hui, Pengfei Wang, Evan Ling, Dezhao Huang, Keng Teck Ma, Minhoe Hur, Jun Liu
Computer Vision
KathaTrace: Diagnosing Semantic Trajectory Collapse in Generated Visual Narratives
Jamuna S. Murthy, Amin Karimi Monsefi and Rajiv Ramnath
Visual narratives are central to storyboards, comics, children's media, and film previsualization, where viewers understand stories from images alone. Recent generators such as StoryDiffusion produce coherent sequences, but visual coherence does not guarantee that source-story transition meaning remains recoverable. Existing
benchmarks assess visual quality, content faithfulness, and scene coherence, but miss a critical failure mode: storyboards where scenes appear visually coherent while the semantic link between scenes disappears. We introduce KathaTrace, a generator-agnostic protocol for diagnosing semantic trajectory collapse, defined as the loss of transition meaning needed to understand how one scene follows another. KathaTrace evaluates transitions under three evidence conditions: text-only, image-only, and text-plus-image, and filters ambiguous items. We contribute KathaBench-25K, with 5,000 narratives from classical collections including Aesop, Panchatantra, and Kathasaritasagara, 20,000 transitions, and 28,712 recoverability questions. We define Semantic Trajectory Gap, or STG, as text-only minus image-only recoverability, measuring transition meaning lost during visualization. Human validation yields Fleiss' kappa = 0.845. Experiments across state-of-the-art generators show substantial STG of 23.5 +/- 1.3. Semantic Compass, an actionability probe, uses KathaTrace signals for post-generation repair and improves storyboard selection.
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Computer Vision
CPG-PAD: Concept-Informed Prompts Guided Presentation Attack Detection
Haoyuan Zhang, Xiangyu Zhu, Li Gao et al. (+3 more)
Presentation Attack Detection (PAD) serves as a crucial safeguard for face recognition systems against presentation attacks such as printed photos, replayed
videos, and 3D masks. Despite significant progress, existing PAD models still struggle to generalize across unseen domains due to variations in sensors, lighting, and attack materials. Recent
Vision-
Language Models (VLMs) have shown strong generalization ability, yet their applications in PAD remain limited because learned
prompts, typically optimized under class-label supervision, fail to explicitly align with fine-grained attack-relevant visual semantics. As a result, the learned representations often overfit domain-specific artifacts instead of capturing transferable attack cues. To address this, we propose Concept-Informed
Prompts Guided Presentation Attack Detection (CPG-PAD), a framework that introduces model-level concept guidance into the
prompt learning process. Specifically, we design a Visual Concept-driven Enhancement (VCE) module that employs eXplainable AI (XAI) techniques to automatically discover PAD-relevant visual concepts and generate concept-associated heatmaps providing localized fine-grained guidance. Guided by these heatmaps, a
Prompt-based Concept Injection (PCI) mechanism integrates these concepts into the
prompt space through a Visual-
Prompt Decoder (VPD) and a concept-mapping loss, enabling
prompts to align with the model's internal concept space. This design enables CPG-PAD to capture generalizable and domain-invariant attack cues while effectively suppressing
dataset-specific biases. Extensive experiments across nine
benchmark datasets demonstrate that CPG-PAD consistently achieves state-of-the-art cross-domain performance under multi-source, limited-source, and single-source settings.
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All authors Haoyuan Zhang, Xiangyu Zhu, Li Gao, Ajian Liu, Siran Peng, Zhen Lei
Computer Vision
AnchorSplat: Fast and Structure Consistent Detail Synthesis for Gaussian Splatting
Dexu Zhu, Jiangnan Shao, Xiaofeng Wang et al. (+4 more)
3D Gaussian Splatting (3DGS) has emerged as a powerful representation for high-fidelity rendering. However, existing assets often suffer from quality bottlenecks such as missing details and texture noise. Prior attempts to enhance these assets via 2D image processing introduce multi-view inconsistencies and high computational costs. In this paper, we propose a novel 3D-native refinement paradigm named AnchorSplat. AnchorSplat is an end-to-end deep network operating directly on 3D structures, avoiding the expensive optimization overhead of traditional 3D-2D-3D pipelines. Crucially, AnchorSplat is a strictly source-free solution requiring no original multi-view images. Central to the proposed method is the Point Anchor Mechanism, which enforces
geometric consistency via local offset constraints, mitigating ill-posed mapping and gradient confounding. Furthermore, AnchorSplat replaces iterative densification with a single-pass multiplication mechanism. To facilitate research, we construct 3DGS-SR, the first large-scale
benchmark for this task. Experiments demonstrate state-of-the-art results on the 3DGS-SR
dataset, with throughput up to $10^5$ times faster than optimization methods. Notably, AnchorSplat exhibits robust zero-shot generalization across diverse data distributions, including generative model outputs and real-world scans.
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All authors Dexu Zhu, Jiangnan Shao, Xiaofeng Wang, Junxian Duan, Jie Cao, Zheng Zhu, Huaibo Huang
Computer Vision
Ink3D: Sculpting 3D Assets with Extremely Complex Textures via Video Generative Models
Yue Han, Chong Li, Zhening Liu et al. (+5 more)
Recent 3D generative models can synthesize high-quality geometry but often struggle to reproduce intricate textures from reference images, largely due to the scarcity of large-scale 3D training data with rich surface appearance. In contrast, visual generative models are trained on
datasets several orders of magnitude larger and excel at modeling complex visual patterns. Motivated by this gap, we introduce Ink3D, a framework that bridges 3D generation with large-scale
video generative models to synthesize extremely complex textures. Ink3D first reconstructs a white-mesh geometry using an off-the-shelf 3D generation model. It then employs OrbitPainter, a conditional
video generative model, to produce dense orbit-scan
videos capturing object appearance across viewpoints. To convert these views into coherent textures, we introduce TextureOptimizer, a neural baking module that integrates dense multi-view observations while mitigating geometry inconsistencies arising from
video generation. By decoupling geometry and texture synthesis and leveraging large-scale pretrained
video priors, Ink3D enables significantly richer and more faithful texture generation than prior approaches.
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All authors Yue Han, Chong Li, Zhening Liu, Cong Huang, Fang Deng, Yong Liu, Fangyun Wei, Yan Lu
Computer Vision
Linkify: Learning from Interface-Augmented Assembly Graphs
Anushrut Jignasu and Daniele Grandi
We present Linkify, a framework for learning from interface-augmented assembly graphs to enable context-aware part
retrieval in mechanical assemblies. While recent generative AI methods for CAD have focused largely on isolated parts or monolithic assemblies, the rich
geometric information at the interfaces between parts, where function is realized, remains underexplored. We address this gap by recomputing high-fidelity interface geometry for the Fusion 360 Gallery Assembly
dataset, correcting missing and erroneous contacts, and generating point-cloud representations of local contact regions. Using this data, we construct assembly graphs whose nodes encode part geometry and whose edges encode interface geometry via a pretrained point-cloud encoder. On top of this representation, we train a Graph
Attention Network based on GATv2 to solve a masked part prediction task: given an assembly with one part held out, the model predicts the class of the missing component from a large vocabulary of geometrically clustered parts, thereby approximating a realistic part-
retrieval scenario. Compared to non-graph baselines such as logistic regression and k-nearest neighbors operating on aggregated node features, Linkify achieves higher Top-K accuracy and F1 scores. Ablation studies on graph connectivity, edge attributes, and
attention mechanisms demonstrate that accurate contact computation and dynamic
attention over interfaces are critical for performance. Our corrected interface
dataset and training pipeline, released publicly, provide a foundation for future interface-aware models for assembly
retrieval, validation, and generative design.
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Computer Vision
World from Motion: Generative Dynamic Gaussian Reconstruction from Monocular Video
Liyuan Zhu, Shengyu Huang, Amrita Mazumdar et al. (+6 more)
We present World from Motion, a method for generating freely renderable dynamic 3D Gaussian representations from
monocular videos. Our approach conditions a
video model on dense, pixel-aligned renderings that encode appearance, geometry, and 3D scene motion along both input and target camera trajectories to correct rendering artifacts and fill in missing regions from an initial reconstruction. To train this model, we construct a
dataset of aligned multiview
video pairs and dynamic 3DGS representations, with simulated artifacts characteristic of
monocular reconstruction. At test time, we distill the model's generations, including newly observed regions and motions, back into a single consistent, high-quality dynamic 3DGS, improving both novel-view synthesis and the underlying 3D motion. Our method sets a new state of the art in 4D reconstruction and seamlessly generalizes to in-the-wild
videos with large viewpoint changes and dynamic motions.
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All authors Liyuan Zhu, Shengyu Huang, Amrita Mazumdar, Tianye Li, Zan Gojcic, Gordon Wetzstein, Iro Armeni, Shalini De Mello, Alex Trevithick
Computer Vision
Perceive-to-Reason: Decoupling Perception and Reasoning for Fine-Grained Visual Reasoning
Hongxing Li, Xiufeng Huang, Dingming Li et al. (+11 more)
Fine-grained visual
reasoning remains challenging for
vision-
language models, especially when small but critical visual cues are buried in high-resolution images. Existing approaches rely on repeated cropping or test-time visual search to introduce local evidence, but they typically do not explicitly distinguish perception from
reasoning. In this paper, we propose Perceive-to-Reason (P2R), a unified framework that formulates fine-grained visual
reasoning as a two-stage process: the model first localizes question-relevant evidence as a Perceiver, and then answers the question as a Reasoner based on the annotated image and cropped regions. To better align training with this decoupled formulation, we further introduce Perception-
Reasoning Alternating GRPO (PRA-GRPO), a role-aware reinforcement learning strategy that alternates between perception-focused and
reasoning-focused updates using only final-answer supervision. Built on top of Qwen3-VL-Instruct-2B/4B/8B, P2R consistently improves performance across model scales. In particular, P2R-4B achieves 93.2% on V-
Star, 81.9% on HR-Bench-4K, and 80.5% on HR-Bench-8K, substantially outperforming its corresponding backbone. Further experiments show that the benefits of P2R extend beyond high-resolution
benchmarks to broader
multimodal reasoning tasks. These results suggest that explicitly decoupling perception from
reasoning provides an effective framework for fine-grained visual
reasoning.
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All authors Hongxing Li, Xiufeng Huang, Dingming Li, Wenjing Jiang, Zixuan Wang, Haolei Xu, Hanrong Zhang, Haiwen Hong, Longtao Huang, Hui Xue, Weiming Lu, Jun Xiao, Yueting Zhuang, Yongliang Shen
Computer Vision
High-dimensional Embedding Prior for Noisy K-space Domain MRIReconstruction
Yu Guan, Tianjia Huang, Qinrong Cai et al. (+3 more)
Magnetic resonance imaging (MRI) reconstruction under realistic acquisition conditions can be fundamentally viewed as estimating the underlying k-space distribution from incomplete and noise-corrupted measurements. While
diffusion models have recently shown strong potential as generative prior for inverse problems,existingapproachesstruggletohandlenoisyreconstruction settings, especially when operating directly in k-space domain. In this work, we propose a unified high-dimensional k-space reconstruction framework tailored for noisy inverse problems, whichenhancesdiffusion-based solversthroughrepresentation lifting.Ratherthanmodifyingthe underlying optimization procedures, the proposed framework augments the data representation space, enabling existing
diffusion-based solvers to operate on enriched k-space
embeddings with improved expressiveness. Extensive experiments on both in-house and public
datasets across varying noise levels and undersampled factors demonstrate that the proposed frame work consistently improves reconstruction quality for multiple
diffusion-based inverse solvers. Notably, the largest gains are observed in high-noise regimes, which is consistent with our theoretical analysis of error propagation under high-dimensional representation. These results suggest that high-dimensional representation provides a general and model-agnostic mechanism for improving
diffusion-based MRI reconstruction in noisy settings, offering a new perspective on robust k-space generative modeling for practical inverse problems. The code will be available at https://github.com/yqx7150/HEP-MRIRec.
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All authors Yu Guan, Tianjia Huang, Qinrong Cai, Qiuyun Fan, Dong Liang, Qiegen Liu