Computer Vision
Reward Lightning: Fast Video Generation via Homologous Preference Distillation
Jiaxiang Cheng, Bing Ma, Xuhua Ren et al. (+4 more)
Achieving simultaneous preference
alignment and
distillation acceleration in
video diffusion models remains an open challenge. Existing methods optimize the two objectives over mismatched representation spaces, where improving one objective often compromises the other. To overcome this, we propose Reward Lightning, a unified framework that aligns and accelerates a
video diffusion model within a single shared representation. Its central principle is homology: both objectives are evaluated on identical latent features, which mitigates the gradient conflicts that arise when they are optimized over disjoint representations. As a foundational component, we first introduce a latent reward model (LRM) that scores
videos directly in the latent space, without decoding back to the pixel space. Building on the LRM, homologous preference
distillation (HPD) reuses this shared backbone to perform
adversarial distillation and preference
alignment jointly, yielding few-step generators that remain faithful and well aligned. Extensive experiments demonstrate that the LRM surpasses pixel-level and latent-level reward baselines by $11.0\%$ and $14.7\%$ in preference accuracy, and that Reward Lightning generates high-fidelity
videos in merely $1$ to $4$ steps, improving the average VBench score by $2.1\%$ while leading in text
alignment, motion quality, and visual quality. Project page: https://reward-lightning.github.io.
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All authors Jiaxiang Cheng, Bing Ma, Xuhua Ren, Kai Yu, Peng Zhang, Tianxiang Zheng, Qinglin Lu
Computer Vision · Cross-listed from eess.IV
Cross-Modal Fusion of OCT and OCT angiography enface for Improved Diagnostics of Diabetic Retinopathy
Rashadul Hasan Badhon, Atalie Carina Thompson, Jennifer I. Lim et al. (+2 more)
Diabetic retinopathy (DR) is a leading cause of
vision impairment worldwide, highlighting the need for accurate and accessible screening tools. Optical Coherence
Tomography (OCT) provides high-resolution structural information of the retina, whereas OCT angiography (OCTA) offers complementary vascular information that is highly relevant for DR diagnosis. In this study, we propose a cross-modal fusion of OCT B-scans with single-channel en face OCTA using a bidirectional cross-modal
attention network for automated DR classification. Two independent
datasets, OCT500 and UIC, comprising 730 subjects in total, were utilized to evaluate performance under within-
dataset, combined-
dataset, and cross-
dataset generalization settings. A ConvNeXt V2 model trained solely on OCT images served as the unimodal baseline. In addition to ground-truth (GT) OCTA, we explored the use of translated (TR) OCTA generated from OCT scans, eliminating the requirement for dedicated OCTA hardware. Experimental results demonstrate that cross-modal fusion consistently outperforms unimodal OCT classification across all evaluation scenarios. Fusion with GT OCTA improved classification accuracy and discriminative performance, while TR OCTA achieved comparable or superior results in most settings. Furthermore, TR OCTA improved sensitivity and cross-
dataset generalization, indicating enhanced robustness to domain shifts. These findings demonstrate that
attention-based OCT-OCTA en face fusion provides clinically meaningful improvements for DR detection and suggest that computationally generated OCTA can serve as a practical, low-cost alternative to hardware-acquired OCTA, enabling broader deployment of high-performance retinal screening systems in resource-limited clinical environments.
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All authors Rashadul Hasan Badhon, Atalie Carina Thompson, Jennifer I. Lim, Theodore Leng, Minhaj Nur Alam
Computer Vision
TESSERA v2: Scaling Pixel-wise Earth Foundation Models
Zhengpeng Feng, Sadiq Jaffer, Ira Shokar et al. (+12 more)
Pixel-wise Earth-observation (EO) foundation models are now achieving state-of-the-art performance via generated spatial
embeddings. However, how these models scale and how best to spend a pretraining budget remain poorly understood. We present the largest controlled scaling study for EO to date: 395 training runs within a fixed pixel-wise Barlow Twins family, each evaluated on 15 diverse downstream tasks. We find that pretraining loss barely predicts downstream performance (|Pearson r| < 0.2), so selecting models by loss wastes a large share of the compute. We also find that, as the training budget grows, the encoder and the data should grow together while the projector stays fixed, which gives a simple rule for allocating compute. Using this rule, we train a family of pixel-wise teachers (0.5B, 1B, and 2B) and distil the largest into compact students for
embeddings-as-data deployment. In aggregate, our 44-million-parameter distilled student outperforms every open and proprietary
embedding product we test, several of them an order of magnitude larger. These students produce Matryoshka representations that are inexpensive to serve: a 16-dimensional prefix keeps 92% of the full 128-dimensional performance at 1/8 of the storage. Together, these results give a concrete, empirically grounded recipe for scaling pixel-wise EO foundation models: train large encoders, select by downstream performance, and distil into flexible student models. We plan to release global 10 m annual
embeddings covering 2017-2025 as version 2 of the TESSERA foundation-model
embeddings product. All code is available at: https://github.com/ucam-eo/tessera
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All authors Zhengpeng Feng, Sadiq Jaffer, Ira Shokar, Jovana Knezevic, James Ball, Pedro Sousa, Mark Elvers, Madeline Lisaius, Clement Atzberger, Robin Young, Aneesh Naik, Niall Robinson, David Coomes, Anil Madhavapeddy, Srinivasan Keshav
Computer Vision
A Large-Scale Dataset and a New Method for RemoteSensing Traffic Object Segmentation
Zhigang Yang, Huiguang Yao, Linmao Tian et al. (+2 more)
Remote sensing imagery plays a crucial role in evaluating regional transportation capacity. However, existing
segmentation datasets often lack diversity in object categories and scenes, limiting the ability of models to comprehensively evaluate trans portation capacity in real-world scenes. To alleviate this gap, we construct a large-scale and diverse
dataset for transportation object
segmentation, named as NWPU-Traffic. This
dataset encompass four traffic object categories (car, airplane, ship, and train) and a wide range of scenes from 49 cities across 7 countries, with instance-level annotations to ensure precise
segmentation of individual objects, which bridges critical shortcomings in resolution and scene diversity in existing
datasets. Leveraging this
dataset, we establish a
benchmark with several popular
segmentation networks. Furthermore, we propose a novel
segmentation method that leverages spatial-channel preserving feature interaction and an adaptive feature decoder, enabling robust
segmentation across varying scales and complex environments. Extensive experiments and ablation studies validate the effectiveness of our approach. The
dataset and code are publicly available at https://github.com/CVer-Yang/NWPU-Traffic.
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All authors Zhigang Yang, Huiguang Yao, Linmao Tian, Qiang Li, Qi Wang
Computer Vision
Fatemah Alhamdoosh, Pietro Pala, Abduallah Mohamed et al. (+1 more)
Motor impairments, including tremor, bradykinesia, gait abnormalities, and postural instability, are common across many neurological and movement-related conditions. Conventional clinical assessments are often intermittent and may fail to capture subtle temporal variations in motor behavior. While wearable IMUs and third-person
video have shown promise for objective motor assessment, third-person recordings raise
privacy concerns and require constrained acquisition setups. In contrast,
egocentric vision provides a more naturalistic and privacyaware alternative. In this work, we introduce EgoInertia-MI, a
multimodal benchmark dataset combining synchronized
egocentric video and wearable IMU signals for motor impairment analysis. The
dataset contains 19 upper- and lower-body activities performed by healthy volunteers simulating varying levels of motor impairment severity levels: no impairment, mild impairment, and severe impairment. We establish two
benchmark tasks: action recognition and motor impairment severity estimation, and evaluate multiple unimodal and
multimodal baselines. Experimental results show that
egocentric video provides strong cues for motor impairment assessment, while
multimodal fusion achieves the best overall performance, reaching 0.78 Macro-F1 for severity estimation and 0.93 Macro-F1 for action recognition. These findings highlight the potential of combining
egocentric vision and wearable sensing for ecologically valid and
privacy-aware motor assessment. Code and data are available at:https://fatemah-alh.github.io/EgoInertia-MI-Page/.
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All authors Fatemah Alhamdoosh, Pietro Pala, Abduallah Mohamed, DK Arvind
Computer Vision · Cross-listed from eess.IV
GLOW-FDG: Generalized cancer LesiOn Whole-body segmentation model for $^{18}$F-FDG-PET/CT
Maksym Fritsak, Maximilian Rokuss, Hubert S. Gabryś et al. (+10 more)
Whole-body fluorodeoxyglucose positron emission
tomography combined with computed
tomography is widely used in cancer care, but manual lesion delineation is slow, subjective, and difficult to scale. We present GLOW-FDG, an open-source artificial intelligence model for whole-body cancer lesion
segmentation in fluorodeoxyglucose positron emission
tomography and computed
tomography. The model was trained on 1,563 scans spanning multiple cancer types and evaluated on 185 external scans from independent institutions. Across breast cancer, nonmetastatic and oligometastatic lung cancer, head and neck cancer, and metastatic melanoma, GLOW-FDG consistently outperformed publicly available
benchmark models in lesion detection, while reducing false positives and maintaining strong
segmentation accuracy. Quantification of total tumor burden and total lesion glycolysis was robust across cohorts, and performance approached the variability observed between expert radiation oncologists. These results support GLOW-FDG as a generalizable tool for automated cancer
segmentation and quantitative imaging biomarker extraction in whole-body imaging.
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All authors Maksym Fritsak, Maximilian Rokuss, Hubert S. Gabryś, Yannick Kirchhoff, Benjamin Hamm, Sebastian M. Christ, Nicolas Martz, Isabelle Opitz, Rolf Stahel, Martin Hüllner, Matthias Guckenberger, Klaus Maier-Hein, Stephanie Tanadini-Lang
Computer Vision
NavEYE: Vision-Centered Multi-Sensor Fusion-Based Situational Awareness System for Intelligent Surface Vehicles
Ryan Wen Liu, Junxiong Liang, Haoyu Wang et al. (+1 more)
With the rapid development of sensor and artificial intelligence (AI) technologies, intelligent surface vehicles (ISVs) have gained increasing
attention from academia and industry. Their intelligence, reliability, and safety depend heavily on situational awareness in complex navigational environments. To achieve high-quality perception, we develop a
vision-centered multi-sensor fusion system, named NavEYE, by exploiting complementary sensors, including the automatic identification system (AIS), radar, and RGB camera. Specifically, we first propose a multi-constrained gated data association method (MCGA) to accurately match low-temporal-resolution AIS data with high-temporal-resolution radar data. Their fusion result is then obtained by selectively implementing distance-aware adaptively weighted fusion (DAWF) and timeliness decay-based stitching fusion (TDSF), which reduce the uncertainty caused by AIS or radar data loss in real-world sensing scenarios. Based on accurate and robust visual object detection, we further associate and fuse AIS, radar, and visual data through joint constraints of normalized bearing and distance features. According to the fusion results, comprehensive information related to ships of interest can be automatically obtained, helping enhance situational awareness and reduce collision risk for ISVs. The feasibility, robustness, usability, and effectiveness of the proposed multi-sensor fusion method and situational awareness system are demonstrated through extensive experiments on a real-world sensing
dataset collected from AIS, radar, and camera. The experimental results show the superior performance of our fusion method in both quantitative and qualitative evaluations. In addition, the shipboard NavEYE system can promote navigational safety for ISVs in complex and dynamic environments.
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All authors Ryan Wen Liu, Junxiong Liang, Haoyu Wang, Mengwei Bao
Computer Vision
USE: A Unified Self-Ensembling Framework for Test-Time Prompt Tuning
Siru Jiang, Jian Liang, Ran He et al. (+1 more)
Test-time adaptation (TTA) has emerged as a popular paradigm for improving the performance of
vision-
language models (e.g., CLIP) on downstream tasks. Among existing CLIP-based TTA methods, Test-Time
Prompt Tuning (TPT) is a pioneering work that optimizes textual
prompts using multiple test-time augmentations and remains a strong baseline to date. In this work, we revisit TPT and reveal that its optimization can be interpreted as implicitly learning from self-generated pseudo labels. Building on this perspective, we propose a unified self-ensembling framework (USE) that ensures consistency between the optimization and inference stages. During optimization, we introduce a simple yet effective self-ensembling (SE) strategy that emphasizes the test image itself over its augmented views adaptively to obtain more reliable pseudo labels. To fully exploit the potential of augmentations, we further apply the same strategy at inference time, unifying the objectives of both stages. Notably, SE can also act as a lightweight optimization-free TTA method. Extensive experiments across multiple
datasets demonstrate that SE and USE outperform their counterparts, respectively. Furthermore, SE yields consistent performance gains when integrated with existing TTA methods. The code is available at https://github.com/sirujiang/USE.
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All authors Siru Jiang, Jian Liang, Ran He, Tieniu Tan
Computer Vision
DICT: Data Injection and Contrastive Trajectory Refinement for Conditional Image Generation with Diffusion Models
Chunnan Shang, Xin Zhang, Zhizhong Wang et al. (+1 more)
Diffusion models have become a dominant paradigm for conditional image generation, yet existing approaches generally follow two directions: task-specific designs that can improve performance but limit generalization, and training-free loss guidance that compresses rich conditions into scalar objectives and applies stepwise guidance, leading to information bottlenecks and error accumulation along the sampling trajectory. Given the urgent need for an effective unified framework across diverse conditional image generation tasks, we propose Data Injection and Contrastive Trajectory Refinement (DICT), a training-free inference method that enhances conditional image generation without introducing task-dependent architectures. DICT introduces Data Injection, where noise-perturbed conditional signals are integrated into early denoising stages; by performing guided denoising on these injected signals, DICT adaptively selects and distills task-salient information from the raw condition, effectively preserving spatial richness and ensuring precise condition-to-generation
alignment. Furthermore, DICT applies Contrastive Trajectory Refinement across adjacent denoising states, enabling pairwise comparisons that progressively improve sample quality. These designs keep inference simple while improving cross-task transfer under a unified
diffusion formulation. Extensive experiments on conditional image generation tasks (e.g., style transfer, image super-resolution, and image deblurring) show consistent gains in fidelity and perceptual quality over representative task-specific and loss-guided baselines.
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All authors Chunnan Shang, Xin Zhang, Zhizhong Wang, Hongwei Wang
Computer Vision
BAT3R: Bootstrapping Articulated 3D Reconstruction from 2D Image Collections
Jakub Zadrozny, Oisin Mac Aodha and Hakan Bilen
3D reconstruction of articulated objects from a single image is challenging because large training
datasets with paired image and 3D supervision are difficult to obtain. Recent point map-based methods achieve strong performance but rely on synthetic
datasets rendered from manually created articulated 3D assets with carefully curated pose distributions. While camera viewpoints can be easily sampled, generating realistic object articulations remains costly and labor-intensive. We propose a training framework that reduces this requirement by leveraging unannotated 2D images collections with only a single rigged canonical mesh per category. Starting from a weak 3D shape predictor trained on canonical-pose renders, we iteratively estimate object articulation and camera pose by fitting the mesh to predicted point maps. The recovered articulations and viewpoints are then used to render updated synthetic training data, progressively improving the predictor. Despite using substantially weaker 3D supervision, our models achieve performance comparable with DualPM, which requires manually curated articulated training
datasets.
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Computer Vision
SGF-CDNet: A Consistency-Discrepancy Graph Network over Semantic-Geometric Fused Nodes for Face Forgery Detection
Jiayao Jiang, Bin Liu and Nenghai Yu
The rapid advancement of deepfakes necessitates robust face forgery detection. Although forged faces may lack obvious artifacts, they often contain subtle disharmony among different facial regions. We propose SGF-CDNet, a Consistency-Discrepancy Graph Network (CD-GNN) over Semantic-
Geometric Fused (SGF) nodes. First, SGF-CDNet constructs SGF nodes by deeply fusing semantic regions from face parsing with
geometric information from facial landmarks, allowing nodes to capture both high-level concepts and precise
geometric constraints. Next, a dual-path CD-GNN performs parallel relational
reasoning on these nodes across two dimensions: consistency and discrepancy. The consistency path evaluates if facial components follow natural biological patterns, while the discrepancy path mines for structural tensions and feature conflicts introduced by forgeries. By integrating these processes, our model effectively identifies disharmonious relationships between facial components. Extensive experiments on public
datasets demonstrate that SGF-CDNet achieves superior performance, establishing it as a reliable solution for face forgery detection.
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Computer Vision
MACRO: Training-free Multi-plane Attention for Closeup Render Optimization
Nitzan Hodos, Roy Amoyal, Lior Fritz et al. (+3 more)
Close-up rendering, zooming into a scene well beyond any training camera, is important for virtual production and interactive 3D content, yet remains an open challenge. 3D Gaussian splatting (3DGS) enables high-fidelity, real-time novel view synthesis, but its rendering quality degrades at close range. Recent
diffusion-based methods that enhance the rendering by conditioning on reference images from the training set produce significant artifacts in this setting. We analyze this failure and identify its root cause: the scale gap between the close-up and reference views. We show that the features in reference-conditioned enhancement models are not scale-invariant, causing cross-view
attention to retrieve incorrect correspondences when the same content appears at different scales, and that this mismatch cannot be corrected in latent space because the VAE encoder is not scale-equivariant. Building on this analysis we introduce MACRO, Multi-plane
Attention for Closeup Render Optimization, a training-free method for high-quality close-up novel view synthesis from 3DGS. MACRO resolves the scale gap by leveraging the scene's known 3D structure: it decomposes the close-up into depth planes, crops and resizes references in image space to match the scale of each plane before encoding, and applies a depth-aware
attention mask so each
token attends only to scale-matched references. The method requires no architectural changes or additional training. We further contribute two new close-up novel view synthesis
benchmarks, the first standardized evaluation protocol for this setting, and demonstrate state-of-the-art results on both, outperforming existing 3DGS and
diffusion-based methods on both reconstruction and perceptual metrics. Project page: https://nitzanhod.github.io/MACRO
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All authors Nitzan Hodos, Roy Amoyal, Lior Fritz, Ianir Ideses, Sagie Benaim, Netalee Efrat
Computer Vision
SharpSplat: Edge-Regularized 3D Gaussian Splatting for High Fidelity Urban Building Reconstruction from UAV images
Porus Vaid, Shivam Chopra and Vaibhav Kumar
Reconstructing high-fidelity 3D building models from
UAV imagery is essential for large-scale digital twin development. However, existing 3D Gaussian Splatting (3DGS) techniques often struggle with building facades, failing to capture sharp
geometric transitions. To address this, we propose a semantic edge regularization framework that supervises 3DGS to produce crisp architectural boundaries. Our method leverages SAM 3 to generate precise building masks, from which we extract architecturally significant edges. During training, we align rendered image gradients with these extracted edges, forcing the Gaussians to converge into sharp structural geometries. Evaluations across campus environments, dense urban centers, and custom residential
datasets demonstrate significant improvements in edge fidelity without requiring architectural modifications to the 3DGS pipeline. Our approach proves robust across diverse building types, roof geometries, and urban densities.
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Computer Vision
GeoSelect: Spatial-Program Execution for Training-Free Referring Remote Sensing Image Segmentation
Yuhang Jiang, Guohui Deng, Miaozhong Xu et al. (+3 more)
Referring remote sensing image
segmentation segments the object named by a natural-
language expression in an aerial image. Existing training-free methods resolve the expression through implicit
vision-
language activations or region-text similarity, which gives weak control over the spatial, superlative, and ordinal relations that dominate aerial referring, such as the rightmost ship or the second court from the left. We propose GeoSelect, a training-free pipeline that reframes referring as the execution of a typed spatial program. A frozen, text-only
language model synthesises the expression into a small domain-specific
language, a well-formedness checker accepts the program, and a deterministic executor runs it. The central abstraction is a single scored candidate set type under which every operator composes: continuous
geometric fields realise position and proximity, while discrete set and order operators add the extremum, ordinal, top-k, and relational constructions that fields alone cannot express. Execution is explicit, so every intermediate is inspectable, and a reliability ladder degrades any failing program to the field-only special case. GeoSelect achieves 58.86 mIoU on RRSIS-D test and 55.27 mIoU on RISBench test, more than twice the best prior training-free method on RRSIS-D, with no referring supervision and on a single GPU. Under a fixed detector and segmenter, explicit execution improves over implicit selectors under the same backbone; the best-box-IoU and outcome-partition diagnostics motivate complementary tests of proposal recall and program-path behaviour, with the program path the clearer priority on RISBench, and an exposure audit shows comparable accuracy on the audited unseen subset. Code and configurations are available at https://github.com/Avalon-S/GeoSelect.
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All authors Yuhang Jiang, Guohui Deng, Miaozhong Xu, Chao Ruan, Jinling Zhao, Linsheng Huang
Computer Vision
Ghosts Beneath Textures: Texture-Relation Cues for Cross-Paradigm AI-Generated Image Detection
Haoyu Wang, Yiming Qin, Zhongjie Ba et al. (+4 more)
AI-generated images have proliferated rapidly, motivating extensive research. Most existing AI-generated image detectors are developed and evaluated under image-free generation paradigms, such as noise-based or text-guided generation. However, image-conditioned generation has become increasingly important in practical applications, as it enables more fine-grained control over generated content. Detecting AI-generated images across these two paradigms creates a critical cross-paradigm detection problem that has long been overlooked. To study this problem, we construct ConImageGen, a
benchmark for cross-paradigm AI-generated image detection. Evaluations on ConImageGen show that existing detectors fail to generalize reliably across image-free and image-conditioned generation. To address this failure, this paper identifies a cross-paradigm forensic cue and provides a new perspective for generalized AI-generated image detection. Specifically, by suppressing semantic interference, we visualize, for the first time, semantics-irrelevant texture patterns across generation paradigms. These patterns exhibit structured local-global texture relations, indicating a generalizable form of forensic evidence. Motivated by this finding, we shift the focus from directly exploiting explicit artifacts to modeling texture relations and propose DTS-Det, a detection framework that captures and leverages such relations for generalized AI-generated image detection. Extensive experiments validate the effectiveness of our method. DTS-Det achieves state-of-the-art performance across diverse evaluation settings, reaching 99.6% ACC on ConImageGen with a 10.5% gain over the best baseline. It also achieves 93.2%/94.1% ACC in cross-
dataset evaluation on PicoBanana/RAID and maintains detection rates of 95.2%/88.1% under reconstruction attacks and black-box
adversarial attacks, respectively.
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All authors Haoyu Wang, Yiming Qin, Zhongjie Ba, Ziping Dong, Jishen Zeng, Peng Cheng, Kui Ren
Computer Vision
PRISM3D: Probabilistic Refinement and Robust Initialization for Physically Consistent Scene Modeling under Extreme Motion Blur
Gopi Raju Matta, Reddypalli Trisha, Vemunuri Divya Madhuri et al. (+1 more)
We address the inverse problem of blind 3D scene reconstruction from extremely motion-blurred images, a scenario where traditional Structure-from-Motion (SfM) pipelines fail. Existing approaches typically circumvent this bottleneck by relying on impractical sharp-image supervision. In this work, we introduce PRISM3D, a unified framework enabling robust reconstruction directly from severely degraded inputs. To overcome the lack of a reliable starting point, we propose a Robust Initialization strategy utilizing deep dense tracking method (VGGSfM) to recover global
topology where feature matching fails. To the best of our knowledge, we are the first to effectively leverage this paradigm to bootstrap 3D Gaussian Splatting from extreme motion blur. However, while robust, this initialization yields sparse and noisy geometry that causes deterministic optimization to diverge. To resolve this, we propose a coupled solution driven by probability and physics: we adopt a probabilistic formulation for
geometric densification via Markov Chain Monte Carlo (MCMC) to robustly populate the sparse priors, while simultaneously modeling physical image formation via continuous Bezier Trajectories. Furthermore, while PRISM3D establishes a highly robust standalone pipeline, the availability of complementary event streams offers an opportunity to push the reconstruction fidelity further. To exploit this, we introduce PRISM3D-E, a multi-modal (RGB + Events) extension that seamlessly integrates high-temporal-resolution events as structural priors to maximize
geometric recovery. Because existing
datasets lack paired event streams under such severe degradation, we concurrently contribute the PRISM3D-E
Benchmark to facilitate rigorous evaluation. Extensive experiments demonstrate that both our standalone RGB framework and its multi-modal extension establish new state-of-the-art performance.
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All authors Gopi Raju Matta, Reddypalli Trisha, Vemunuri Divya Madhuri, Kaushik Mitra
Computer Vision
CogRad: A Cognitively-Inspired Multi-Agent Framework for Radiology Report Generation
Saif Ur Rehman Khan, Hasaan Maqsood, Sebastian Vollmer et al. (+2 more)
Automated radiology report generation (RRG) can ease radiologist workload, yet most existing systems produce a report in a single forward pass, with no mechanism to check a claim against the image or revisit a finding once stated. We present CogRad, a cognitively inspired multi-
agent framework that structures generation around four stages of a radiologist's reading process. A Scout
agent discovers anatomical regions directly from image patches via slot
attention and assigns region and disease-level triage scores; an Investigator
agent concentrates representational capacity on the regions Scout flags as suspicious; a Writer
agent compiles these signals into a disease gated visual prefix for a large
language model; and a Verifier
agent supervises training with a visual entailment loss and, at inference, re-examines its own draft sentence by sentence, regenerating any report it judges insufficiently grounded. On CheXpert Plus, CogRad attains a BLEU-4 of 0.316 and a CIDEr of 0.322, the best scores among the methods we compare against. On IU X-Ray, it attains a BLEU-4 of 0.201 and a CIDEr of 0.724, leading every baseline on every standard NLG metric. We further evaluate CogRad with RadGraph F1, CheXbert F1, and a
hallucination analysis to assess clinical accuracy beyond standard text-overlap metrics, complemented by ablation studies and Grad-CAM-based visualizations that characterize each
agent's contribution and the model's visual grounding.
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All authors Saif Ur Rehman Khan, Hasaan Maqsood, Sebastian Vollmer, Andreas Dengel, Muhammad Nabeel Asim
Computer Vision
ContiStain: Cross-Domain Relation-Preserving Distillation for Continual Multi-Domain Virtual IHC Staining
Fuqiang Chen, Yifeng Wang, Hongpeng Wang et al. (+1 more)
A unified multiplex virtual staining model enables scalable and non-destructive multiplex analysis from H&E slides while promoting parameter efficiency, shared pathological knowledge, and consistent cross-biomarker representations. However, in clinical practice, data for new biomarkers are typically acquired sequentially over time. Fine-tuning on such temporally arriving data leads to severe performance degradation on previously learned biomarkers, as sequential optimization disrupts the structured relationships among biomarker representations in the latent space. To address this issue, we propose ContiStain, an IHC multi-domain relational
distillation framework for continual virtual staining. We first (i) construct a domain-aware structured feature space using a mixture-of-experts (MoE) feature extractor to reduce representation interference across biomarker domains. Based on this stabilized feature space, we then (ii) propose a relation-preserving
distillation strategy that explicitly enforces the consistency of cross-domain
token-level cosine similarity
matrices between learned biomarker domains during continual adaptation. By maintaining cross-domain structural coherence, ContiStain mitigates forgetting while retaining adaptability to new domains. Experiments on the MIST
dataset under a four-domain sequential virtual IHC staining setting show improved stability, reducing FID and ConchFID by 11.1 and 60.9 compared to sequential fine-tuning, enabling scalable and robust multi-domain virtual staining. Code is released at https://github.com/ccitachi/ContiStain.
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All authors Fuqiang Chen, Yifeng Wang, Hongpeng Wang, Yongbing Zhang
Computer Vision
When Simpler Is Better: Evaluating Translation Pipelines for Medieval Latin Manuscripts
Nguyen Kim Hai Bui, Md. Easin Arafat, Tamás Gábor Orosz et al. (+1 more)
Despite remarkable progress in machine translation,
Vision Language Models (VLMs) struggle on historical manuscripts, a domain that stresses core Natural
Language Processing (NLP) capabilities: low-resource transliteration, archaic vocabulary, and noisy input signals. We present a systematic framework for evaluating the full image-to-translation pipeline on medieval Latin manuscripts, a setting in which scribal shorthand, ligatures, and parchment degradation expose failure modes that are invisible in clean-text
benchmarks. Benchmarking on the CATMuS Latin
dataset reveals a specialization gap: domain-specific Optical Character Recognition (OCR) models reduce character error rate by up to 4.3$\times$ compared to general-purpose VLMs, despite operating at orders of magnitude fewer parameters. We introduce the Interpres-Parallel-Corpus (IPC), a novel
dataset comprising 1,383 aligned manuscript image lines, transcriptions, and expert translations, the first of its kind for medieval Latin. Our experiments uncover a complexity paradox: the simplest pipeline, a specialized OCR model feeding directly into a VLM, outperforms all multi-component variants. Adding
retrieval-augmented generation (RAG) or post-OCR correction introduces
prompt saturation and error propagation that degrade aggregate translation quality. These findings offer both a new
benchmark and practical guidance for deploying translation systems in low-resource historical settings.
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All authors Nguyen Kim Hai Bui, Md. Easin Arafat, Tamás Gábor Orosz, Mufti Mahmud
Computer Vision
How Do Diffusion Classifiers Decide? A Bias-Centric Evaluation
Saba Fathi, Fardin Ayar, Maryam Abdolali et al. (+3 more)
Diffusion models have recently been repurposed for zero-shot classification, giving rise to
diffusion classifiers that identify the best-matching text
prompt by minimizing the noise-prediction error. Despite their growing adoption, how these models make classification decisions remains poorly understood. We introduce ASOB-Bench, a bias evaluation for
diffusion classifiers along three dimensions: Attribute binding, Size-Order bias, and Background dependency. These dimensions serve not as an exhaustive taxonomy but as targeted probes of how the text-conditioned reconstruction-error score reaches a decision. Such a perspective is well studied for discriminative
vision-
language models, yet remains overlooked for
diffusion classifiers. Extending an existing framework with five new attribute categories on newly constructed
datasets, we find
diffusion classifiers are less prone to attribute misbinding than an OpenCLIP baseline; on the established ComCo
benchmark they are substantially more susceptible to size-order shortcuts; and on ImageNet-B they suffer far larger accuracy drops, revealing heavy reliance on background over foreground cues. Reconstruction-error heatmaps and U-Net cross-
attention visualizations expose the mechanism behind each bias. Because
diffusion classifiers share the same denoiser as text-to-image models, these single-pass diagnostics also point toward analogous failure modes in generation. Overall,
diffusion classifiers exhibit a distinct bias profile from
vision-
language models, offering guidance for building more robust
diffusion-based models.
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All authors Saba Fathi, Fardin Ayar, Maryam Abdolali, Ehsan Javanmardi, Manabu Tsukada, Mahdi Javanmardi
Computer Vision
Q-TriM: Question-Guided Tri-Modal Attention for Audio-Visual Question Answering
SungHun Kim and SeungJun Baek
Audio-Visual Question Answering (AVQA) extends classical VQA by requiring joint
reasoning over
video and synchronized audio. However, many AVQA systems rely on deeply stacked layers of self- and cross
attention across text,
video, and audio. Such sequential stacking may incur loss of information such as subtle inter-modal cues over the layers, causing errors to accumulate across sequential
attention layers during the fusion. We introduce Q-TriM which performs multi-modal fusion in a shallow and parallel manner instead of a deep and sequential manner. For Q-TriM, we propose a novel framework for
attention operation incorporating
video and audio conditioned on text. As a result, we obtain not only standard cross
attention outputs but also Tri-Modal
Attention representations in which Query, Key, and Value come from distinct modalities. These
attention representations are combined in parallel at a single stage, thus avoiding the multi-modal fusion with deep stacks in order to mitigate error accumulation and depth-induced issues. Q-TriM achieves state-of-the-art performance on three AVQA
benchmarks, including substantial gains on MUSIC-AVQA-R, which demonstrates its robustness and out-of-distribution generalization. Code is available at https://github.com/Sunghun95/Q-TriM
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Computer Vision
FDR-Occ: Factorized Dense Routing for Full-Spectrum 3D Occupancy Prediction
Dubing Chen, Huan Zheng, Tianyi Yan et al. (+5 more)
Vision-based 3D occupancy prediction fundamentally relies on the 2D-to-3D view transformation. Current paradigms predominantly utilize explicit physical projection, which artificially restricts the routing
matrix to strict, sparse camera rays. While computationally efficient, this imposes a severe Locality Bottleneck, preventing the network from constructing holistic contextual understanding and degrading sharply when camera extrinsics are unreliable or absent. To break this bottleneck, we abstract view transformation as unconstrained bipartite routing and propose Factorized Dense Routing (FDR). By approximating dense 2D-to-3D mixing through hierarchical tensor contractions, FDR guarantees a fully-global receptive field with tractable, sub-quadratic complexity. Crucially, the mandatory spatial contraction in dense routing exposes a fundamental Resolution-Context Trade-off. To address this, we introduce a Resolution-Context Decoupled Architecture. We factorize the 3D space into a global macroscopic topological anchor (via FDR) and precise local
geometric planes (via explicit projection). This decoupling enables global semantic inference and exact surface localization to complement each other without mutual compromise. Extensive experiments demonstrate that our framework achieves state-of-the-art performance on the Occ3D-nuScenes and Occ3D-Waymo
benchmarks. More notably, in an uncalibrated setting where physical extrinsics are withheld, our global routing internalizes the implicit multi-camera rig
topology and exhibits substantially stronger structural robustness than physical-projection baselines under the same protocol.
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All authors Dubing Chen, Huan Zheng, Tianyi Yan, Yucheng Zhou, Runzhou Tao, Zhongying Qiu, Jianfei Yang, Jianbing Shen
Computer Vision
CGGS: Consistency-Augmented Geometric Gaussian Splatting for Ego-Centric 3D Scene Generation
Zhenyu Sun, Xiaohan Zhang, Qi Liu et al. (+1 more)
Challenges remain in ego-centric 3D scene generation due to limited view overlap and the dominant influence of individual perspectives on scene interpretation. These factors hinder the creation of viewpoint-consistent and semantically aligned visual content, as well as the construction of accurate
geometric structures. In this paper, we propose CGGS, a text-to-3D framework aiming to enhance 3D-content-awareness and address
geometric distortions in ego-centric scene generation. Firstly, the Ego-centric Generator is proposed by fine-tuning a Multi-View Latent
Diffusion Model with consistency-augmented loss to generate consistent, high-fidelity 2D content aligned with textual descriptions. Then, Layout Decorator leverages optical flow and point-track correspondence to estimate depth, therefore producing dense point clouds as coarse layouts from the ego-centric 2D priors. Building on this initialization,
Geometric Refiner is proposed to enhance 3D Gaussian reconstruction via an
entropy-based Mutual Information Depth Loss (MID) combined with a hierarchical optimization scheme for improving visual quality and
geometric structure. Comprehensive experiments demonstrate that CGGS outperforms previous methods in generating coherent and accurate text-driven 3D scenes.
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All authors Zhenyu Sun, Xiaohan Zhang, Qi Liu, Huan Wang
Computer Vision
TRISTAR: Triple-Signal Stair Recognition and Vision-Only Indoor Navigation for Search-and-Rescue Micro-UAVs
Octavian Gîngu and Stelian Spînu
Indoor search-and-rescue (SAR) operations often require rapid situational awareness where GNSS signals are unavailable and human access is difficult or hazardous. While most autonomous aerial systems rely on
LiDAR, stereo
vision, or specialized depth cameras, such solutions increase both hardware complexity and deployment costs. This paper presents a complete autonomous indoor
navigation framework for low-cost unmanned aerial vehicles based exclusively on
monocular vision. Implemented on a DJI Tello platform, the system combines
monocular depth estimation using Depth Anything V2 with classical computer
vision and lightweight deep learning models for scene understanding, victim detection, and hazard recognition. The framework consists of two independent behaviors: (i) corridor exploration with automatic door detection, room entry, OCR-based room identification, and victim inspection; and (ii) autonomous stair ascent based on TRISTAR (TRI-Signal STair Ascent Recognition), a novel triple-sensor fusion method that integrates structural cues (Sobel filtering), texture analysis (multi-scale Gabor filtering), and
geometric depth from
monocular depth estimation. Evaluation used real indoor flights in a university building. Depth calibration reduced relative depth error from 27.4% to below 10%, while the door detection
algorithm reached a precision of 0.93 and an F1-score of 0.91. A dedicated ablation study shows that multi-sensor fusion significantly improves stair-recognition robustness compared to individual sensing modalities, and a failure-case analysis delineates the limits of
monocular perception under challenging lighting and reflective surfaces. The results demonstrate that reliable indoor exploration and stair traversal are achievable on resource-constrained platforms without specialized ranging hardware, a practical, cost-effective solution for rapid SAR deployment.
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Computer Vision
Global Logic and Local Search: Dual-Stream Multimodal In-Context Learning for Verifiable Industrial Anomaly Detection
Runzhi Deng, Yundi Hu, Yiming Zhong et al. (+5 more)
Large
Multimodal Models (LMMs) show strong few-shot generalization, but industrial anomaly detection remains difficult because defects are small, input resolution is limited, and textual standards are not always grounded in visual evidence. Recent optimization-based methods improve
alignment through fine-tuning, but they often require many defective samples, which are unavailable in early deployment. We present Global Logic and Local Search (GLLS), a training-free framework for reference-guided
multimodal in-context verification. GLLS uses a Part-Aware Visual-Logical Atlas to organize normal references and structured specifications in the inference context. It combines a Global & Logic Stream, where SAM 3 extracts partially checkable visual facts, with a Fine-Grained & Actions Stream, where MCTS selects local evidence crops under a fixed budget. Experiments on MMAD-QA and additional anomaly detection
datasets show consistent gains over matched and general-purpose baselines, while keeping the final diagnostic decision traceable to explicit visual evidence throughout the inspection trace.
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All authors Runzhi Deng, Yundi Hu, Yiming Zhong, Zhao Wang, Xixi Liu, Hongsong Wang, Caifeng Shan, Fang Zhao
Computer Vision
TestMate: Test-Time Domain Adaptation Aided by Lightweight Vision Foundation Model
Dimitrios Fotiou, Vasileios Mygdalis and Ioannis Pitas
Test-Time Domain Adaptation (TTDA) aims to adapt Deep Neural Networks to distribution shifts using only streaming, unlabeled test data in real time. Current methods for semantic
segmentation tasks suffer from critical limitations.
Entropy minimization techniques require costly backpropagation, risking catastrophic forgetting and producing noisy
segmentation boundaries.
Memory-bank methods, while backpropagation-free, exhibit slow adaptation, requiring numerous samples to converge and struggle to handle continuous domain shifts. We introduce TestMate, a novel, real-time, and backpropagation-free TTDA framework that overcomes these issues. TestMate leverages generalization capability of a lightweight Visual Foundation Model to guide the adaptation. We use a zero-shot instance
segmentation YOLOv8-seg based model to generate unlabeled mask proposals for objects and their parts at multiple scales in real time. These proposals are fused with the primary model via a heuristic, size-ordered competitive scheme, where small, high-confidence regions dominate and refine predictions in surrounding larger, less certain areas. This paremeter-free mechanism enables immediate adaptation from the first frame, inherently avoids catastrophic forgetting and effectively preserves fine object details and boundaries, even for small objects. TestMate can be used as a standalone, efficient refinement module or seamlessly integrated into existing TTDA methods to significantly boost their performance. We demonstrate state-of-the-art results across two
benchmark datasets, proving TestMate's effectiveness in three distinct adaptation tasks: TTDA, Source-Free Domain Adaptation (SFDA), and online-TTDA. Code is available.
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Computer Vision
CineMobile: On-Device Image-to-Video Diffusion for Cinematic Camera Motion Generation
Xuyao Huang, Zelai Deng, Xu Wang et al. (+2 more)
The growing demand for image-to-
video creation on mobile devices has increasingly focused on cinematic motion effects like bullet time, dolly zoom, slow motion, etc. While
Diffusion Transformers (DiTs) exhibit strong performance in
video generation, their large parameter sizes and multi-step iterative denoising processes lead to substantial computational overhead, making efficient generation on mobile devices challenging. We propose CineMobile to bridge the gap. In particular, CineMobile adopts a three-fold optimization strategy: (1) leveraging a
distillation-guided
pruning approach to derive a compact yet efficient model that retains the essential
video generation capabilities required for cinematic effects; (2) optimizing the compressed model into a 4-step generator via a combination of
diffusion distillation and reinforcement learning; (3) employing a hybrid post-training quantization strategy to compress the model footprint to under 1 GB. Experimental results show that compared to the teacher model with the Wan 2.1 architecture, CineMobile achieves a 40x speedup in generation while maintaining comparable visual quality. Specifically, CineMobile generates 49-frame 480p
videos with a per-step denoising latency of 0.6s on an NVIDIA H200 GPU and 20s on the MediaTek Dimensity 8400 Ultimate 5G platform, with a peak
memory usage of 1.8 GB, demonstrating its practical applicability for mobile-based image-to-
video creation.
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All authors Xuyao Huang, Zelai Deng, Xu Wang, Xizhong Xiao, Zhijie Deng
Computer Vision · Cross-listed from cs.LG
Foundations of Equivariant Deep Learning: Unifying Graph and Sheaf Neural Networks
Yoshihiro Maruyama
Symmetry is everywhere in nature and society.
Geometric deep learning builds architectures respecting group
symmetries, whereas topological deep learning organizes computation through
cells, incidence relations, and local-to-global structure. In this paper, we extend
geometric deep learning beyond simple group actions and unify it with topological deep learning. Specifically, we develop order-equivariant neural networks (OENN), which generalize standard graph message passing and sheaf neural networks via the theory of equivariant vector bundles over face posets (or face categories). We (i) characterize all linear order-equivariant maps, (ii) build OENN layers, and (iii) prove universal approximation
theorems (UATs) for continuous order-equivariant maps, which are new results even when restricted to sheaf neural networks. We illustrate the framework on graph and sheaf models. Our results can also be seen as extending the known UAT for graph neural networks to a more general setting that subsumes sheaf neural networks as well. In the appendix, we show that OENN can be connected, via the action groupoid Grothendieck construction, to CENN (category-equivariant neural network), which gives the categorical general form of equivariant neural networks, allowing us to leverage categorical
symmetry in data and extending
geometric deep learning from groups of
symmetries to categories of transformations.
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Computer Vision
Probabilistic Robustness in Medical Image Classification
Yi Zhang, Siddartha Khastgir and Xingyu Zhao
Deep learning (DL) has shown strong performance in medical image classification, but its trustworthy deployment remains challenging in safety-critical clinical settings, where prediction errors under perturbations may lead to severe consequences. Existing studies mainly focus on
adversarial robustness (AR) from a worst-case perspective; however, such settings may be less representative of real medical applications. In this work, we investigate probabilistic robustness (PR) as a more practical measure of model trustworthiness. To this end, we construct a set of natural corruption settings for medical image classification and systematically evaluate commonly used DL models on MedMNIST v2
dataset. Our study provides a statistically grounded perspective on assessing the trustworthiness of DL models, thereby supporting their more trustworthy deployment in medical imaging applications.
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Computer Vision
InfraNet: Quality-Aware RGB Guidance for Efficient Infrared Object Detection
Zichao Feng, Haodong Zhu, Jingying Yang et al. (+8 more)
Robust object detection under adverse visual conditions remains a long-standing challenge for multi-modal perception systems. Existing fusion-based methods typically require both RGB and infrared (IR) inputs, and treat them equally during both training and inference, which compromises their robustness when the RGB modality becomes unreliable or unavailable. In this case, we propose InfraNet, an IR-centric quality-aware framework that regulates RGB guidance during training and supports flexible RGB–IR or IR-only deployment. InfraNet employs an asymmetric architecture where the primary IR pathway extracts multi-scale infrared features for predictions, while the auxiliary RGB pathway provides reliability-controlled supervisory signals. The core of InfraNet is QualGate, a quality-aware fusion module that learns a task-oriented control signal to suppress unreliable RGB guidance and compensate IR features during cross-modal training. Built upon InfraNet, we design two architectural variants: a lightweight IR-only architecture InfraNet-IR and an RGB–IR architecture InfraNet-RGB-IR. Our method is evaluated through extensive experiments on four
benchmark datasets (LLVIP, FLIR-Aligned, M$^3$FD, and DroneVehicle), showing strong or competitive accuracy in challenging low-light and adverse weather conditions. Notably, InfraNet maintains high efficiency in IR-only inference, making it both accurate and computationally efficient.
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All authors Zichao Feng, Haodong Zhu, Jingying Yang, Sheng Xu, Yangyang Ren, Yuguang Yang, Xuhui Liu, Juan Zhang, Tian Wang, Linlin Yang, Baochang Zhang