Computer Vision
MegaStyle++: Scaling Image Style Space through Hierarchical Style Definition
Junyao Gao, Sibo Liu, Jiaxing Li et al. (+4 more)
Image style is a highly abstract, human-constructed concept shaped by a range of visual factors and intrinsically entangled with content, yet a unified and explicit definition of image style remains lacking. In this work, we first discuss the fundamental question of what is style and then propose a hierarchical style definition that describes image style from an overall style identity to fine-grained visual attributes, providing a more structured, transferable, and interpretable style representation. Based on this definition, we refine the style annotation pipeline of MegaStyle and construct MegaStyle++-8M, a large-scale style
dataset containing 150K overall style identities, 1M fine-grained style
prompts, and 8M stylized images. Extensive analyses demonstrate that our hierarchical definition substantially expands the style space in both diversity and semantic breadth, while precisely capturing intrinsic visual style of reference images. The
dataset and code will be updated at https://github.com/Tencent/MegaStyle, we hope MegaStyle++ provides a scalable foundation for studying and modeling diverse image styles.
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All authors Junyao Gao, Sibo Liu, Jiaxing Li, Yanan Sun, Weidong Zhang, Jun Zhang, Cairong Zhao
Computer Vision · Cross-listed from cs.AI
EdiTikZ: Scientific Figure Editing from Revision Trajectories
Christian Greisinger, Zhixue Zhao and Steffen Eger
Vision-
language models (VLMs) have shown strong performance in generating scientific figures from text or images. However, publication-ready figures often require iterative refinement, making scientific figure editing an important yet largely unexplored step toward interactive figure creation. Existing approaches rely on costly proprietary agentic systems, focus primarily on evaluation, or construct training supervision from synthetically generated edits. Instead, we leverage naturally occurring scientific revision and development trajectories as a scalable source of supervision. To this end, we introduce DaEdiTikZ, the first large-scale
dataset of revision-derived scientific figure edits, constructed by mining 391K plausible TikZ edit pairs from arXiv, GitHub, and TeX SE and inferring 781K directed edit instructions with a VLM conditioned on rendered figures and TikZ code. We further introduce DaEdiTikZ-Bench, a human-refined
benchmark with 690 instances, and train two compact Qwen3.5-based EdiTikZ models (4B and 9B) by jointly learning image-to-TikZ reconstruction and instruction-conditioned editing, followed by reinforcement learning (RL) with complementary rewards for rendered fidelity and edit application. Automatic evaluation places our 9B model above all tested baselines, while human evaluation with 9 annotators and 4,320 ratings places it above GPT-5.6-Sol and on par with Gemini-3.1-Pro. Under severe out-of-distribution shifts, it remains competitive with GPT-5.6-Sol near its 2K training sequence-length regime.
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Computer Vision · Cross-listed from cs.AI
Neuro-Symbolic Geometric Abstraction (NeuSOGA): From Observations to Symbolic Mathematical Representations
Qingde Li, Qingqi Hong, Zihan Li et al. (+1 more)
A fundamental challenge in artificial intelligence is the transformation of observations into explicit symbolic representations suitable for abstraction, interpretation, and
reasoning. While modern AI systems achieve remarkable perceptual capabilities through large-scale statistical learning, the resulting knowledge is typically encoded within latent parameters that are difficult to inspect or manipulate analytically. Inspired by Neuro-Symbolic AI and theories of human abstraction, this paper investigates the formation of symbolic mathematical representations from
geometric observations. We propose NeuSOGA (Neuro-Symbolic
Geometric Abstraction), a framework that progressively transforms observations into topological abstractions,
geometric abstractions, and ultimately symbolic mathematical representations. The architecture combines
topology-guided structural discovery using Euclidean Distance Transforms, foundation-model perception using Segment Anything, adaptive multi-scale
geometric abstraction, and symbolic synthesis through Implicit Area Splines. The resulting representation is an analytical implicit model supporting arbitrary-order smoothness, additive composition, and closed-form evaluation. Unlike neural latent encodings, the generated representation remains interpretable, editable, and mathematically explicit. Experiments on ModelNet40 point clouds, arbitrary-view projections, and segmented optical observations demonstrate that NeuSOGA transforms diverse observations into compact symbolic representations while preserving essential
geometric and topological structure across sensing modalities and viewing directions. NeuSOGA provides an interpretable and explainable pathway from observation to symbol and establishes
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All authors Qingde Li, Qingqi Hong, Zihan Li, Jie Tian
Computer Vision
Scale-based Approach for Active Wildfire Segmentation on Satellite Imagery
Matheus F. Kovaleski, Cristiano Premebida and João Ruivo Paulo
Active wildfire mapping from satellite imagery is challenging due to the sparse and highly imbalanced nature of fire pixels, especially in early-stage or low-density fire observations. This work investigates the use of multispectral Landsat-8 imagery for active-fire
segmentation under multi-scale wildfire size conditions. We propose a data-driven protocol to characterize fire-region size distributions through connected-component analysis and an interquartile range criterion, enabling the evaluation of model robustness across different local fire-region densities. Three
segmentation architectures, U-Net, DeepLabV3+, and SegFormer, are evaluated under different SWIR-based spectral configurations. Results show that U-Net achieves the strongest robustness across the evaluated conditions, SegFormer provides competitive performance, and DeepLabV3+ tends to produce conservative predictions with reduced recall. Across architectures, SWIR2 consistently achieves the strongest or near-best results, highlighting its importance for active-fire
segmentation in Landsat-8 imagery. These findings suggest that both spectral band selection and architectural design are critical for robust satellite-based active wildfire mapping trained on low active fire-pixel density images.
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Computer Vision
Multimodal RGB-Infrared Combination for UAV-Based Wildfire Segmentation: A Comparative Study on FLAME3
Matheus F. Kovaleski, Luís Garrote, Cristiano Premebida et al. (+2 more)
Unmanned Aerial Vehicles (
UAVs) have emerged as a promising platform for firefighting operations due to their flexibility, low operational cost, and ability to acquire high-resolution imagery in locations that may be difficult or dangerous to access using conventional methods. Recent advances in deep learning have significantly improved the capabilities of
UAV-based wildfire monitoring systems. The present work investigates RGB-infrared fusion for binary wildfire
segmentation on the FLAME3
dataset. In this Study, RGB and Infrared baselines are compared with three representative fusion strategies across three
segmentation architectures, including U-Net, DeepLabV3+, and SegFormer. The key motivation of this work is to analyze the contribution of each modality, evaluate the impact of fusion timing, and examine how different network architectures exploit
multimodal information for
UAV wildfire delineation. The findings indicate that thermal information plays a dominant role in
UAV segmentation and that feature-level
multimodal fusion combined with
transformer-based architectures offers the most promising direction for future research.
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All authors Matheus F. Kovaleski, Luís Garrote, Cristiano Premebida, Jérôme Mendes, João Ruivo Paulo
Computer Vision · Cross-listed from cs.CL
InSight: A Benchmark for Agentic Claim Verification in Interactive Visualizations
Maeve Hutchinson, Syed Mahbubul Huq, Mohammad Albinhassan et al. (+3 more)
Vision Language Models have demonstrated remarkable proficiency in interpreting static visual artifacts, but modern data analysis is inherently dynamic, requiring the active interrogation of interactive environments. Existing
benchmarks are predominantly constrained to static imagery and one-shot question answering and fail to capture the epistemic demands of this domain, where evidence is frequently occluded, distributed across linked views, or conditionally revealed through user agency. In this paper, we introduce InSight, a
benchmark for agentic claim verification over interactive visualizations. The
dataset consists of 21,349 claims derived from human-authored analytical narratives and grounded in fully interactive web-based environments.
Agents must navigate these environments to determine whether a natural
language claim is supported, refuted or not verifiable given the available evidence. Unlike traditional evaluations, InSight treats interaction traces as intrinsic proxies for
reasoning, enabling a rigorous audit of how models seek and synthesize visual evidence. We evaluate state-of-the-art models, revealing that interactive verification remains a non-trivial challenge. We release InSight at https://github.com/maevehutch/insight.
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All authors Maeve Hutchinson, Syed Mahbubul Huq, Mohammad Albinhassan, Radu Jianu, Aidan Slingsby, Pranava Madhyastha
Computer Vision
IntroConformal: Conformal Factuality Guarantees for Large Vision-Language Models via Introspective Signals
Md. Atabuzzaman, Christian Alexander and Chris Thomas
Large
Vision-
Language Models (LVLMs) have achieved strong
multimodal performance, yet ensuring the factual correctness of generated content remains challenging. Existing methods that provide statistical guarantees on factuality typically rely on external verifiers or generation-time confidence signals, which introduce auxiliary dependencies or often fail for confident but incorrect outputs. We argue that reliable factuality control can instead be achieved through introspective signals derived from the model itself. We introduce IntroConformal, a training-free Conformal Risk Control (CRC) framework that provides finite-sample, distribution-free factuality guarantees. We first instantiate it with layer-wise semantic stability, a conformity score derived from hidden-state representations, and then propose verification probability, a stronger score capturing the model's self-administered judgment on claim factuality. Across multiple LVLM architectures, IntroConformal satisfies the conformal risk guarantee while substantially reducing abstention and achieving competitive or superior claim-level discrimination relative to external verifier-based baselines.
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Computer Vision
Diffusion Based Unpaired Data Learning for Inverse Problems
Chenglong Bao, Yiming Dang, Chenguang Duan et al. (+2 more)
Data is important in many deep learning-based inverse problem solvers. However, obtaining sufficient paired data in many scenarios remains highly challenging, while unpaired data is cheap. To maximize data utilization, this paper proposes LUD-DIF, a
diffusion-based approach for solving inverse problems with unpaired data. Starting from the evidence lower bound (ELBO) of the joint distribution, we decouple it into two independent
diffusion processes under the weak-coupling assumption. The method provides theoretical support from a variational inference perspective, derives the loss function, quantitatively analyzes the error bound introduced by the assumption, and offers a
theorem-motivated heuristic for hyperparameter selection. Experimental results demonstrate that LUD-DIF achieves outstanding performance on multiple image inverse problems, validating its effectiveness and generalization capability in unpaired inverse problem settings.
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All authors Chenglong Bao, Yiming Dang, Chenguang Duan, Yuling Jiao, Defeng Sun
Computer Vision
Accurate Reconstruction of Gas Turbine Blade Geometry Using 3D/2D Rigid Registration and CT View Optimization
Hristo Valtchanov, Nicolas Piché, Vladimir Brailovski et al. (+3 more)
Non-destructive X-ray and computed
tomography (CT) testing are essential for ensuring the dimensional accuracy of manufactured components with complex internal structures, such as the cooling channels in gas turbine blades, which directly affect thermal performance and service life. This study presents a multipart 3D-2D rigid registration approach for aligning CAD models with X-ray projections as an alternative to CT reconstruction for part inspection and measurement. A greedy registration
algorithm sequentially aligns the blade's exterior before registering its internal components by maximizing the mutual information between simulated and acquired X-ray images. This stepwise approach reduces problem complexity and improves
alignment accuracy. View angles are optimized using a greedy method that iteratively selects angles to minimize dimensional measurement errors. The results indicate that a small number of oblique views provides the best accuracy, although a broad range of angles yields acceptable results. The method achieves subpixel registration accuracy, with errors below one-fifth of the magnified detector-pixel pitch. Image noise and defects reduce registration precision, but direct registration in projection space mitigates these effects compared with CT reconstruction. Appropriate view selection can therefore preserve acceptable subpixel accuracy in the presence of image noise and defects.
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All authors Hristo Valtchanov, Nicolas Piché, Vladimir Brailovski, Justin Byers, Catherine Désrosiers, François Guibault
Computer Vision
ExBind: A Controlled Diagnostic Benchmark for Visual-to-Executable Correspondence
Ziqian Wang, Yuxiao Cheng, Tingxiong Xiao et al. (+1 more)
Multimodal coding and editing systems must map a visible or semantic referent to the exact executable object that can be edited. A wrong reference may select a valid but incorrect DOM node, SVG element, graph endpoint, hierarchy member, or table
cell, while final execution success alone does not reveal the source of the failure. ExBind isolates this visual-to-executable correspondence layer as a controlled diagnostic
benchmark between semantic localization and action execution. It samples representation-independent latent binding instances and compiles them into SVG, DOM, canvas, tree, graph, and table cases with deterministic mappings to executable references. Models output only a strict reference; the evaluator maps predictions back to latent structure and scores structural constraints without requiring
reasoning traces. The release contains a 250-case broad suite, a disjoint 240-case targeted suite, and 50 paired latent groups. Qwen2.5-VL-3B achieves 98.4% candidate validity but 76.4% exact accuracy, while Qwen3-VL-4B achieves 100.0% validity and 98.8% exact accuracy. In the targeted table suite, all Qwen2.5-VL-3B residual errors are valid correct-row/wrong-column selections. Candidate-order perturbations change case-level outcomes while preserving this error pattern. ExBind is designed for controlled diagnosis rather than population-scale ranking or end-to-end editing evaluation. Code and
benchmark records are available at https://github.com/Daerwang2020/Exbind and https://huggingface.co/
datasets/Ziqianwwww/ExBind.
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All authors Ziqian Wang, Yuxiao Cheng, Tingxiong Xiao, Jinli Suo
Computer Vision
Md. Atabuzzaman and Chris Thomas
Diffusion-based Large
Vision-
Language Models (dLVLMs) have recently emerged as a compelling alternative to autoregressive (AR) LVLMs, offering advantages in parallel decoding, bidirectional context, and controllable generation. Despite rapid progress, their reliability properties remain largely uncharacterized. We present the first systematic reliability evaluation of
hallucination and bias in dLVLMs, benchmarking six
diffusion models against competitive AR baselines across four dimensions. Our key findings are: (1) dLVLMs reverse the yes-bias of AR models in binary visual queries; (2) they achieve competitive
hallucination rates yet exhibit degraded linguistic quality; (3) they collapse to near-zero accuracy on underrepresented racial groups with opposite-polarity gender bias; and (4) they exhibit accuracy collapse in multiple-choice settings when the correct option is shorter than its distractors, associated with a length prior that emerges at the first denoising step.
Tokens committed at late denoising steps with low confidence further correlate with hallucinated content, pointing to a mechanistic signal unique to
diffusion generation. These patterns vary across model families, suggesting reliability is shaped by the generative paradigm together with training data.
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Computer Vision · Cross-listed from cs.IR
MIDR: Enrichment-Augmented Indexing for Multimodal Document Retrieval
Debanjan Mahata, Atharva Tendle, Daniel Preotiuc-Pietro et al. (+2 more)
Retrieval over visually rich documents has a representation problem: important content often lives in tables, charts, figures, and layout relations that plain OCR linearizes, corrupts, or omits. ColPali-family visual retrievers address this with patch-level multi-vector indexes and late-interaction scoring, keeping image-derived
retrieval on the query-time serving path. We introduce MIDR (
Multimodal Indexing for Document
Retrieval), a training-free framework for enrichment-augmented indexing that shifts
multimodal reasoning to index time. During ingestion, a
multimodal LLM converts rendered pages into verified textual fields that are indexed with BM25F and optionally fused with dense
retrieval, enabling text-centric serving over multimodally grounded evidence. On ViDoRe V3, MIDR Hybrid achieves 0.6219 average nDCG across five English domains, a 23.0% relative gain over BM25, remaining competitive with ColQwen2.5. On two French-document domains, enrichment bridges English queries and French page text, lifting BM25 from 0.1532 to 0.5448 nDCG and outperforming ColQwen2.5. Across all seven domains, MIDR leads ColQwen2.5 on four while using approximately 9x smaller index
memory and approximately 2x lower query latency. These results establish index-time
multimodal reasoning as a compelling accuracy-deployment alternative to serving-time visual late interaction.
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All authors Debanjan Mahata, Atharva Tendle, Daniel Preotiuc-Pietro, Yong Zhuang, Ozan Irsoy
Computer Vision · Cross-listed from eess.IV
GazeRefine: Expert Gaze as a Test-Time Prompt for Training-Free Medical Image Segmentation
Mohammed Oussama Benyahia, Marouane Tliba, Mohamed Amine Kerkouri et al. (+10 more)
Medical image
segmentation remains difficult to scale because high-performing methods typically rely on dense expert annotations and task-specific training. We introduce GazeRefine, a training-free framework that uses gaze as an inference-time
prompt for zero-shot medical image
segmentation. Sparse, duration-weighted fixations are converted into foreground and background priors that initialize semantic prototypes in frozen DINOv3 feature space. These prototypes are iteratively refined through foreground-background discrimination, feature-space affinity propagation, and anchoring to the initial gaze guidance, allowing
segmentation to extend beyond directly fixated regions while limiting semantic drift. GazeRefine requires no
segmentation masks, fine-tuning, adapters,
prompt encoders, or gradient updates. We evaluate the method on gaze-annotated polyp
segmentation and prostate MRI
segmentation. The results show strong performance on colonoscopy images and competitive performance on prostate MRI, supporting gaze-guided prototype refinement as a promising approach for
segmentation-label-efficient, human-in-the-loop medical image
segmentation. Our tools and code can be found in the following repository: https://github.com/MohammedOussamaBEN/GazeRefine.git
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All authors Mohammed Oussama Benyahia, Marouane Tliba, Mohamed Amine Kerkouri, Taifour Yousra, Bin Wang, Max Bengtsson, Gorkem Durak, Elif Keles, Zuheng Ming, Marek Penhaker, Azeddine Beghdadi, Ulas Bagci, Aladine Chetouani
Computer Vision
CMRVision: A Foundation Model for Cardiac MR Image Analysis
Athira J. Jacob, Puneet Sharma and Daniel Rueckert
Cardiac
magnetic resonance (CMR) imaging provides complementary information on cardiac anatomy, function, and tissue characterization across multiple sequences and views. In this work, we investigate foundation model pretraining for 2D CMR and introduce CMRVision, a CMR-specific foundation model trained using DINOv3-style self-supervised learning on a multi-center, multi-sequence cohort of 36 million CMR images. We systematically evaluate architectural and training design choices for domain-specific pretraining. CMRVision is evaluated on two downstream tasks: multi-task
segmentation across cine, late gadolinium enhancement (LGE), and mapping sequences, and cine view classification. Our experiments show that CMR-specific pretraining, smaller patch sizes, and patch-level objectives consistently improve downstream performance. Across a multi-task
segmentation benchmark, CMRVision achieved the strongest overall performance, outperforming prior natural-image (NI), medical-image, supervised, and CMR foundation model baselines. Improvements were modest but consistent across structures and sequences, with Dice scores ranging from 0.940-0.967 for LV and 0.855-0.905 for myocardium, and reaching 0.929 for RV, 0.920 for LA, and 0.931 for RA. The largest gains were observed for myocardium
segmentation in LGE and mapping images. In a zero-shot
segmentation task on unseen LGE long-axis views, the model achieved an average Dice score of 0.692, demonstrating cross-view generalization. For cine view classification, CMRVision achieved the highest average accuracy (0.906), compared to prior methods reported in the literature. These results highlight the potential of CMRVision to support robust and generalizable cardiac MRI analysis across multiple sequences and views.
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Computer Vision · Cross-listed from cs.GR
MeshSuite: A Unified Benchmark for Triangle- and Mesh-Based Neural Rendering
Kaixuan Zhang, Minxian Li, Mingwu Ren et al. (+1 more)
Triangle- and mesh-based neural rendering seeks to bridge neural representations and graphics engines through triangle primitives compatible with standard rasterization. Yet existing methods were developed and evaluated under inconsistent settings, with few direct comparisons and little evaluation in graphics engines. We introduce MeshSuite, the first
benchmark for systematic evaluation of engine deployment and Non-Engine Render in triangle- and mesh-based neural rendering. MeshSuite evaluates two deployment settings: Standard Deployment, using an opaque mesh pipeline with vertex colors and hardware Z-buffering; and Dedicated Deployment, using method-specific engine implementations to preserve appearance and compositing features. For mesh splatting, we also audit exported surfaces for topological and
geometric integrity as downstream graphics assets. Our results show that engine deployment reduces image quality in both settings; among evaluated methods, MeshSplatting degrades least under Standard Deployment. Dedicated Deployment preserves most fidelity at roughly 5-33x the runtime of Standard Deployment across two deployment
datasets. For mesh splatting, explicit connectivity and shared indexing remain limited: shared vertex indexing alone guarantees neither manifoldness nor global connectivity. These findings show that rasterizability is only part of graphics readiness and underscore the need to assess engine deployment. Project page: https://surrey-up-lab.github.io/MeshSuite.
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All authors Kaixuan Zhang, Minxian Li, Mingwu Ren, Xiatian Zhu
Computer Vision
Agentic Multimodal Models for Environmental Hyperspectral Unmixing
Michał Cholewa, Luca Ciampi, Nicola Messina et al. (+2 more)
Hyperspectral unmixing is a key task in remote sensing that aims to decompose mixed pixels in hyperspectral images into their constituent material signatures, or endmembers, and their fractional abundances. Conventional modular approaches estimate the scene composition through successive model-order estimation, endmember extraction, and abundance estimation stages, whose errors can lead to redundant or ambiguous candidate components and ultimately affect the recovered decomposition. We introduce an
algorithm-agnostic, large
vision-
language model (LVLM)-driven agentic framework that refines the outputs of such pipelines rather than replacing their underlying numerical
algorithms. Starting from an initial decomposition, the
agent iteratively gathers complementary spectral and spatial evidence through dedicated tools, including spectral-library
retrieval and abundance-map visualization, and modifies the active endmember set through merge and discard operations followed by abundance re-estimation. We apply the same refinement procedure to several modular pipelines combining different model-order, extraction, and abundance-estimation methods, and evaluate it on HYDICE Urban, Jasper Ridge, and Stonewall Playa. Experiments show that the proposed
agent consistently improves endmember cardinality and generally improves the recovered spectral signatures and abundance maps across heterogeneous modular pipelines, while remaining competitive with integrated end-to-end unmixing methods, including CNN-AE, uDAS, and R-CoNMF. These results highlight the potential of tool-using LVLM
agents to combine spectral and spatial evidence for
algorithm-agnostic refinement of physically grounded hyperspectral unmixing decompositions. Code is publicly available at https://anonymous.4open.science/r/agentic-hu.
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All authors Michał Cholewa, Luca Ciampi, Nicola Messina, Przemysław Głomb, Giuseppe Amato
Computer Vision
HiLRP: Toward One Trustworthy Explanation for Vision Transformer: Conservation-Valid Attribution via Attention Primitives
Sathiyamohan Nishankar, Pubudu Sanjeewani, Asanka Perera et al. (+1 more)
Vision Transformer (ViT) design has become increasingly diverse, with backbones combining convolutional stems, windowed, linear, or multi-axis
attention, patch merging, and spatial reduction in various configurations. This diversity poses challenges for existing attribution methods, whose assumptions often do not hold across ViT variants: Grad-CAM requires a terminal spatial feature map,
attention rollout assumes global softmax
attention, and layer-wise relevance propagation (LRP) requires module-specific rules. To the best of our knowledge, no existing method provides a unified attribution framework across this architectural space. We show that this architectural diversity can be captured by a simpler underlying structure. The
attention and resolution-reduction operators in current ViTs can be decomposed into four operation types: linear maps, bilinear mixing, normalization or gating, and reindexing. Each operation admits a relevance rule that satisfies conservation. Based on these rules, HiLRP supports new backbones by construction rather than by architecture-specific derivation, and its attribution maps decompose the prediction rather than relying on heuristic assumptions. We prove conservation and conditional equivariance and verify both to machine precision. Across 14 attribution methods and 10 architectures, we find that no prior method remains reliable across ViT families, while Faithfulness Correlation becomes uninformative for backbones robust to spatial masking. HiLRP alone preserves conservation across windowed, spatial-reduction, multi-axis, and linear-
attention models, where naive extensions can produce zero or inflated relevance. It also localizes attribution failures in class activation mapping, achieving 0.97 Pointing compared with 0.55 for competing methods on EfficientViT.
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All authors Sathiyamohan Nishankar, Pubudu Sanjeewani, Asanka Perera, Selvarajah Thuseethan
Computer Vision
TimeSteer: Inference-Time Speech Scheduling in Joint Audio-Visual Diffusion Models
Chao Zhou, Yiling Chen, Qi Chu et al. (+3 more)
Although pretrained joint audio-visual
diffusion models offer rich control over what to generate, they provide no explicit control over when an utterance should occur. To address this, we study inference-time
speech scheduling, a novel task that places coupled
speech and visual articulation within user-specified begin–end intervals without finetuning the backbone model. We uncover two intrinsic properties of the denoising process that enable this task. First, a timing-sensitive text-to-audio cross-
attention head exposes each utterance's model-implied source span along the latent timeline. Second, the predicted clean latent already organizes coupled
speech and visual articulation, allowing their temporal placement to be edited without regenerating the content. Building on these discoveries, we propose TimeSteer, a training-free framework that localizes each utterance's source span through Source Span Localization and transfers the associated audio-visual latent content from the source interval to the specified target interval through Region-Aware Latent Remapping. We further introduce SpeechShift, the first
benchmark for interval-level
speech scheduling in joint audio-visual generation. Experiments across two representative backbones show that TimeSteer substantially improves interval controllability over training-free baselines while maintaining competitive overall generation quality.
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All authors Chao Zhou, Yiling Chen, Qi Chu, Tao Gong, Nenghai Yu, Tianyi We
Computer Vision
Seeing the World and the Self from Egocentric Video
Kai Guan, Minchao Jiang, Ruichen WangLi et al. (+2 more)
Complete 3D perception from
egocentric video requires recovering the surrounding scene and the wearer's full-body motion in a shared metric frame. Existing methods typically address scene reconstruction and motion estimation separately: scene reconstruction methods ignore the wearer, whereas motion estimation methods lack explicit scene geometry and often depend on external trajectories. Joint recovery is challenging because the two tasks exhibit asymmetric visibility and require different prediction paradigms. The largely visible scene supports deterministic
geometric regression, whereas the severely occluded body requires generative motion inference. We therefore propose RESELF (REconstructing the Scene and the sELF), a unified framework that couples deterministic metric geometry reconstruction with geometry-conditioned motion generation. RESELF adapts a geometry foundation model pre-trained on large-scale exocentric data to
egocentric video using frame-wise scale and relative-pose consistency objectives. The resulting camera trajectory and latent
geometric features condition a
diffusion model that recovers the wearer's motion. A subsequent closed-loop kinematic feedback stage further refines the camera head while preserving the reconstructed scene geometry. To support training and evaluation, we curate EE4D-JSM from EgoExo4D by aligning
egocentric video, sparse metric scene geometry, camera trajectories, and full-body motion annotations. Experiments show that RESELF outperforms state-of-the-art methods designed for the individual tasks across depth estimation, camera tracking, and full-body motion estimation. Code, models, and
datasets will be available at https://ka1guan.github.io/RESELF/.
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All authors Kai Guan, Minchao Jiang, Ruichen WangLi, Wentao Zhu, Lei Zhang
Computer Vision
MeRoPE: Metric Rotary Position Embedding for Camera-Controlled Video Generation
Zhijian Qiao, Xinjiang Wang, Jiajie Chen et al. (+5 more)
In camera-controlled
video generation, geometry-aware positional encodings condition
tokens on camera extrinsics and per-
token viewing rays. Existing schemes, however, have a scale-dependent failure mode on real-world metric camera trajectories: homogeneous projective encodings cause
attention logits and feature norms to grow unbounded with physical translation baselines. We propose MeRoPE (Metric Rotary Position
Embedding), a norm-preserving relative camera encoding for
attention. MeRoPE encodes relative orientations between calibrated viewing rays with orthogonal rotation blocks, maps raw metric displacements into multi-frequency rotary phases, and adds a disparity-anchored correspondence prior along the epipolar arc. This design strictly preserves feature norms, bounds pre-softmax
attention logits regardless of the physical translation scale, and maintains exact invariance to global rigid coordinate changes. Across nuScenes and PanShot, which cover large-baseline trajectories and diverse camera optics, respectively, MeRoPE achieves stronger camera control than prior encodings, with the best consistency between generated camera motion and conditioning poses in both rotation and translation. Code will be made publicly available.
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All authors Zhijian Qiao, Xinjiang Wang, Jiajie Chen, Haoming Huang, Meng Li, Chih-Chung Chou, Jing Wang, Shaojie Shen
Computer Vision
One Prompt Is Enough: Watermark Laundering Through Foundation Image Models
Jidong Yang, Qi Li, Wei Zong et al. (+5 more)
Invisible watermarks are typically evaluated against predefined perturbations such as compression, blur, noise, cropping, and denoising. Public foundation image models expose a distinct threat: an attacker can submit a watermarked image with a single reconstruction
prompt and obtain a visually faithful output from which the invisible watermark can no longer be decoded reliably. We formalize this failure mode as watermark laundering and evaluate it using a joint payload-fidelity profile that combines bit error rate (BER) with visual and semantic preservation. Across six OpenAI and Google image editing models, three representative watermarking schemes, and 1,800 reconstructed outputs, we identify two complementary laundering regimes: OpenAI models produce the strongest payload disruption across the evaluated schemes, whereas Nano Banana 2 shows that DwtDct remains vulnerable under high-fidelity reconstruction.
Prompt ablations show that no single removal-oriented instruction is necessary for payload disruption, indicating that the effect is primarily induced by the reconstruction pathway rather than by explicit attack wording. Comparisons with conventional attacks further show that
prompt-conditioned reconstruction constitutes a distinct operational attack interface. These findings motivate foundation-model reconstruction as a missing robustness condition in invisible watermark evaluation.
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All authors Jidong Yang, Qi Li, Wei Zong, Yang-Wai Chow, Willy Susilo, Huaike Yu, Chunpeng Wang, Suo Gao
Computer Vision
Yuanyuan Jia, Shunpu Tang and Qianqian Yang
Visual
token pruning reduces the inference overhead of
multimodal large
language models (MLLMs) by retaining only a subset of visual
tokens. Existing methods usually select
tokens based on importance or redundancy. However, we observe that these criteria produce stable spatial biases across inputs and do not always outperform simple Uniform Grid sampling, highlighting the value of broad spatial coverage. Motivated by this, we propose S$^2$Prune, a training-free
pruning method that preserves spatial coverage while adapting
token density to local image structure. We first divide the image into regions and assign at least one
token to each region to preserve coverage. The remaining
token budget is then distributed according to
Laplacian variation, giving more
tokens to regions with richer structure. We then use Early Representation Change (ERC), computed from the first decoder block, to select representative
tokens within each region. We evaluate S$^2$Prune across diverse settings and two MLLM architectures. On Qwen2.5-VL-7B-Instruct, it achieves the highest average accuracy among the evaluated training-free
pruning methods. With only 32 of the original 576 visual
tokens, it still retains 79.3% of the full-model performance. Code is available at https://github.com/yuanyuanjia71-spec/S2Prune.
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Computer Vision
Compressing AI Traffic: Standardized Neural Network Coding of Visual-Token Representations in Split Vision-Language Inference
Reza Heidari, Hamed R. Tavakoli and Juho Kannala
When the visual encoder and the
language decoder of a
vision-
language model (VLM) run on different compute nodes, the intermediate visual-
token embeddings become a communicated payload rather than an internal activation. We call such machine-consumed intermediate tensors AI traffic and ask how far they can be compressed with a standardized, training-free codec. We insert ISO/IEC 15938-17 Neural Network Coding (NNC) round trips on the complete visual interface of a Qwen3-VL-8B-Instruct
video question answering pipeline, comprising the main visual-
token representation and the DeepStack feature streams, while leaving weights,
prompts, and generation untouched, and sweep the quantization parameter (QP) over a wide rate range. Closed-ended
Video-MME accuracy remains close to the uncompressed reference up to a 98% reduction of the transmitted BF16 tensor and only then collapses; open-ended MLVU generation shows the same plateau-and-collapse profile under an LLM judge. This robustness is not due to near-lossless reconstruction: the decoded tensor is heavily discretized, carries substantial row-wise relative L2 error, and has a visibly steeper singular-value decay than its source. Downstream
reasoning therefore depends on coarse structure and relative geometry rather than exact floating-point values, which argues for rate-task rather than rate-distortion optimization of AI traffic codecs.
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Computer Vision
Monocular Depth Estimation from a Single Image: Progress and Opportunities
Muxin Liu, Xiaoyang Lyu, Yang-Tian Sun et al. (+4 more)
Monocular depth estimation has long stood as a fundamental challenge in computer
vision, enabling a wide range of applications including 3D reconstruction, robotics, autonomous driving, and augmented reality. This survey traces the field's
evolution from early learning-based methods to the emergence of transformative foundation models. We begin by framing the problem, distinguishing between relative and metric depth estimation, and highlighting the key challenges that have shaped a decade of research. We then present common problem formulations and introduce the most widely used
datasets, covering indoor, outdoor, and synthetic data. Following this, we review major advances prior to the foundation model era, distilling core insights from influential methods that contributed to improvements in accuracy, efficiency, and robustness. The survey then turns to the recent surge of foundation-model-based approaches, categorizing them into discriminative and generative paradigms and emphasizing the critical roles of large-scale pretraining (e.g., DINOv3) and synthetic data. We compare representative models using both quantitative
benchmarks and qualitative examples, and discuss natural extensions to
video-based depth estimation. Further, to illustrate real-world impact, we highlight the integration of depth estimation into applications such as visual SLAM, content generation, and
robot perception. Finally, we outline open challenges and promising research directions as the field advances further into the era of foundation models.
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All authors Muxin Liu, Xiaoyang Lyu, Yang-Tian Sun, Yi-Hua Huang, Ziyi Yang, Peng Dai, Xiaojuan Qi
Computer Vision
Dotting the Eye: An Intent-Driven Image Retouching Agent for Visual Focus Enhancement
Chujie Qin, Zilong Zhang, Zewei Chang et al. (+5 more)
Image retouching is commonly formulated as enhancing overall visual quality through color adjustment, but in practice, it also serves to emphasize visual focus by guiding viewers'
attention toward a specific subject or region. Achieving such focus-oriented retouching is inherently challenging, as it requires well-coordinated global and local adjustments to manipulate perceptual saliency while maintaining visual naturalness. This intricate process typically demands substantial professional expertise. In this study, we propose EyeControl, a MLLM-driven
agent with a
diffusion-based retouching executor that enables visual focus enhancement under weak user intent. With only a few clicks or coarse strokes, EyeControl directs visual
attention to the intended region, effectively "dotting the eye" of the image. The core idea is to explicitly link the weak user intention with the target editing region and the corresponding tonal adjustment operations during retouching. To achieve this, the system first interprets the intent and image content to infer the visual focus and generate structured intent guidance for the retouching executor. Second, the retouching executor is encouraged to respond more strongly to the target region, explicitly aligning its
attention map with a designed pseudo-intent map. We also introduce an operation-consistency constraint to improve coordination between global and local adjustments, achieving more natural and coherent retouching. Additionally, we contribute ControlArt-Bench, a high-quality evaluation
dataset for visual focus enhancement. Extensive evaluations demonstrate that EyeControl yields perceptually appealing results with stronger intent
alignment. Code can be found in https://github.com/DragonisCV/EyeControl.
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All authors Chujie Qin, Zilong Zhang, Zewei Chang, Chunle Guo, Ruixing Wang, Tao Hu, Ming-Ming Cheng, Chongyi Li
Computer Vision
On the Design Fundamentals of Pixel Text Representation Learning
Chaohao Yuan, Ruifeng Yuan, Zhuoxu Huang et al. (+4 more)
Text-rich visual inputs require models that can read, retrieve, and compress
language directly in pixel space, yet existing pixel-text encoders struggle with fixed resolution pretraining, visual shortcut learning, weak visual grounding, and
multilingual visual text understanding. In this work, we investigate the fundamental design principles required for robust visual text representation learning. Through systematic controlled ablations, we identify four critical components: variable image resolutions and rendered font sizes provide spatial proxies for high-resolution document generalization; natural image-text pairs are indispensable for grounding and prevent text-only collapse; layout-aware rendering helps prevent pixel-level shortcuts; and a two-stage
multilingual curriculum enables effective cross-lingual
alignment. By integrating these principles into a scalable training recipe, we train Pixel Linguist II, a native-resolution
vision encoder trained with on-the-fly rendering, unified contrastive grounding, and a
multilingual curriculum over 280M training examples. Pixel Linguist II sets new state-of-the-art results on English, cross-lingual, and
multilingual Visual STS and ViDoRe, while also enabling better MLLM downstream evaluation. Notably, Pixel Linguist II remains robust under 80% visual
token compression, showing great promise for optical context compression. Our code and resources are available at https://github.com/Pixel-Linguist/Pixel-Linguist-II.
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All authors Chaohao Yuan, Ruifeng Yuan, Zhuoxu Huang, Yu Rong, Hong Cheng, Hou Pong Chan, Chenghao Xiao
Computer Vision
StainPresetNet: Stain Preset Network for Fast Multi-to-Multi Stain Normalization
Hongtao Kang, Die Luo, Li Chen et al. (+4 more)
Stain normalization reduces color variations caused by variations in staining protocols and imaging conditions, thereby enhancing computer-aided diagnostic system performance. Traditional methods derive mapping relationships from individual or limited reference images through pixel-wise transformation, offering style flexibility but suffering from inaccurate color mapping extraction. While existing deep-learning-based approaches achieve accurate
dataset-wide color mapping through complex neural networks, they face challenges including computational inefficiency, artifact generation, and fixed normalization directions requiring model retraining for directional changes. To address these limitations, we propose StainPresetNet - a novel framework that combines structural preservation with
dataset-level color mapping while maintaining computational efficiency. Our method implements pixel-wise normalization guided by preset reference images, enabling multi-directional adaptability without retraining. Evaluations on cytopathology and histopathology
datasets demonstrate that StainPresetNet achieves superior color mapping accuracy compared to conventional methods, effectively improves classifier generalization in diagnostic tasks, and reduces computational overhead by 90% versus existing deep learning approaches. The proposed preset-guided mechanism facilitates flexible adjustment of normalization directions through simple reference image replacement, overcoming the directional
rigidity of current deep-learning-based solutions.
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All authors Hongtao Kang, Die Luo, Li Chen, Jing Cai, Junbo Hu, Xiuli Liu, Shenghua Cheng
Computer Vision
Revisiting Face Recognition for Monozygotic Twins: The Celeb Twins Test Set
Michael Zang, Haiyu Wu, Mrinal Sharma et al. (+1 more)
Past literature on face recognition for monozygotic (("identical") twins points to facial marks and mirror asymmetry as possible directions for improved accuracy of twins recognition. The Celeb Twins Test Set (CTTS) contains web-scraped image pairs for 80 sets of celebrity twins. It is the only twins test set with meta-data for twins with distinguishing skin marks and possible mirror asymmetry. CTTS is organized in the manner of face verification test sets such as LFW, CALFW, CPLFW, CFP-FP, and AgeDB-30. Current deep CNN matchers can achieve over 76% accuracy in classifying CTTS same-person / different-person image pairs. We show that current matchers do not make use of skin marks, or asymmetry, and discuss reasons for this. Finally, we discuss the feasibility of using generative AI tools such as Grok, ChatGPT and Gemini to create images of imagined monozygotic twins as a means to increase representation of twins in face recognition training sets.
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All authors Michael Zang, Haiyu Wu, Mrinal Sharma, Kevin W. Bowyer
Computer Vision
Different Changes Require Different Reasoning: Change-Type-Specialized Experts for Robust Change Captioning
Jiyoung Park, InJae Oh and Jung Uk Kim
Change captioning is the task of generating natural
language descriptions that explain the changes between a pair of images. Although different change types (e.g., color shifts, object additions) exhibit distinct visual cues and require specialized
reasoning processes, existing methods often overlook these distinctions. To address this limitation, we propose Multi-Expert Diagnosis for Image Change (MEDIC), a novel framework that introduces change-type awareness by explicitly modeling change categories. MEDIC employs type-specialized
memory experts that dynamically retrieve type-relevant visual patterns conditioned on the input. This design enables each expert to capture diverse variations within its change type while focusing on the most informative visual cues. By softly routing inputs across type-specialized experts and learning dedicated representations for each change category, MEDIC generates more precise and type-aware change descriptions. Extensive experiments demonstrate that the proposed MEDIC consistently outperforms existing methods across diverse and challenging
datasets. The code is available at \href{https://github.com/VisualAIKHU/MEDIC}{GitHub}.
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Computer Vision
P-PatchDiff: Progressive Patch Diffusion Models for Low-light Image Enhancement
Ruoyu Guo, Haonan Zhong, Maurice Pagnucco et al. (+1 more)
Recent advancements in low-light image enhancement have leveraged
diffusion models for their strong ability to generate perceptually realistic, detailed images. Patch
diffusion models further offer a promising solution to size-agnostic image restoration while improving efficiency. However, existing methods typically rely on small, fixed patches (e.g., 64$\times$64) that cannot capture image-level brightness context, whereas enlarging the receptive field improves brightness and colour estimation but substantially increases computational cost. Moreover, low-light images often exhibit uneven brightness across regions, making it necessary to ensure that locally enhanced patches remain visually coherent when combined into the full image. To address these limitations, we propose P-PatchDiff, a scalable progressive patch
diffusion framework for low-light image enhancement that dynamically adjusts patch size throughout the denoising process, enabling a gradual shift from local to global views. A Multi-Patch
Alignment strategy is also introduced to normalise features across varying patch scales using an estimated global brightness proxy. Rather than pursuing pixel-level reconstruction accuracy, P-PatchDiff focuses on scalability and coherent brightness across the whole image, allowing the model to perceive multi-scale information and better enhance regions with varying brightness. We empirically demonstrate that P-PatchDiff effectively enhances images ranging from 400 $\times$ 600 to 4K and is 80$\times$ faster than existing patch
diffusion models while using less than 9GB of
memory. The code is available at https://github.com/RuoyuGuo/P-PatchDiff.
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All authors Ruoyu Guo, Haonan Zhong, Maurice Pagnucco, Yang Song