Computer Vision · Cross-listed from eess.IV
Local-sensitive connectivity filter (ls-cf): A post-processing unsupervised improvement of the frangi, hessian and vesselness filters for multimodal vessel segmentation
Erick O Rodrigues, Lucas O Rodrigues, João HP Machado et al. (+5 more)
A retinal vessel analysis is a procedure that can be used as an assessment of risks to the eye. This work proposes an unsupervised
multimodal approach that improves the response of the Frangi filter, enabling automatic vessel
segmentation. We propose a filter that computes pixel-level vessel continuity while introducing a local tolerance heuristic to fill in vessel discontinuities produced by the Frangi response. This proposal, called the local-sensitive connectivity filter (LS-CF), is compared against a naive connectivity filter to the baseline thresholded Frangi filter response and to the naive connectivity filter response in combination with the morphological closing and to the current approaches in the literature. The proposal was able to achieve competitive results in a variety of
multimodal datasets. It was robust enough to outperform all the state-of-the-art approaches in the literature for the OSIRIX angiographic
dataset in terms of accuracy and 4 out of 5 works in the case of the IOSTAR
dataset while also outperforming several works in the case of the DRIVE and STARE
datasets and 6 out of 10 in the CHASE-DB
dataset. For the CHASE-DB, it also outperformed all the state-of-the-art unsupervised methods.
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All authors Erick O Rodrigues, Lucas O Rodrigues, João HP Machado, Dalcimar Casanova, Marcelo Teixeira, Jeferson T Oliva, Giovani Bernardes, Panos Liatsis
Computer Vision
SR-Ground: Image Quality Grounding for Super-Resolved Content
Artem Borisov, Evgeney Bogatyrev, Khaled Abud et al. (+1 more)
Super-Resolution (SR) has advanced rapidly in recent years, with
diffusion-based models achieving unprecedented fidelity at the cost of introducing new types of visual artifacts. While existing Image Quality Assessment (IQA) methods provide holistic quality scores, they lack interpretability and fail to distinguish between different artifact types arising from modern SR approaches. To address this gap, we introduce SR-Ground, a large-scale
dataset specifically designed for fine-grained artifact
segmentation in super-resolved images. The
dataset comprises images processed by a diverse set of state-of-the-art SR models, with pixel-level annotations for multiple artifact categories. We conduct a large-scale crowdsourcing study involving 1,062 participants to validate and refine automatically generated
segmentations, resulting in a highquality
dataset of 63,000 images spanning 6 distinct artifact types. We demonstrate that training IQA models with grounding capabilities on SR-Ground significantly improves performance on downstream tasks. Furthermore, we introduce a fine-tuning pipeline that leverages our grounding model to reduce perceptible artifacts in SR outputs, showcasing the practical utility of our
dataset. On a separate
benchmark of 1,000 outputs from five unseen SR methods, an Low-Resolution-referenced grounding model significantly improves prominence
alignment over its no-reference counterpart and performs best on native real-world
datasets of Low-Resolution images without High-Resolution Ground-Truth.
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All authors Artem Borisov, Evgeney Bogatyrev, Khaled Abud, Dmitriy Vatolin
Computer Vision
RePCM: Region-Specific and Phenotype-Adaptive Bi-Ventricular Cardiac Motion Synthesis
Xuan Yang, Xiaohan Yuan, Hao Li et al. (+3 more)
Cardiac motion over a cardiac cycle is crucial for quantifying regional function and is strongly affected by cardiovascular diseases. Since temporally dense mesh sequences are difficult to obtain in practice, we focus on leveraging the more accessible end-diastolic frame to infer a full-cycle sequence. Due to strong regional and disease-specific differences, traditional methods often oversmooth the data by relying on generative models that are optimized for global patterns. To address this problem, we propose Region-Aware and Phenotype-Adaptive Bi-Ventricular Cardiac Motion Synthesis (RePCM) for single frame Bi-ventricular mesh motion completion. In Stage I, a reconstruction network learns vertex wise motion descriptors and clustering yields a data driven functional partition, providing an explicit motion derived region structure. In Stage II, a Region-Specific Injection Module enforces masked, synchronized region exchange within a conditional VAE, preserving localized specific dynamics and restricting cross-region mixing. A Phenotype-Adaptive Mixture-of-Experts prior conditioned on ED shape uses anatomy-guided cues to model latent motion trends and capture inter-disease variability. Experiments on three
datasets covering different cardiovascular diseases show consistent gains in
geometric and functional metrics and improved preservation of region specific dynamics.
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All authors Xuan Yang, Xiaohan Yuan, Hao Li, Lingyu Chen, Yanan Liu, Lei Li
Computer Vision
PGC: Peak-Guided Calibration for Generalizable AI-Generated Image Detection
Xiaoyu Zhou, Jianwei Fei, Peipeng Yu et al. (+3 more)
The rapid
evolution of generative AI, from GANs to modern
diffusion models, has resulted in increasingly subtle discriminative clues. These fine-grained signals are often overshadowed by dominant, high-fidelity image content (e.g., the main subject), limiting the reliability of existing detectors that predominantly rely on global representations. To address this challenge, we propose the Peak-Guided Calibration (PGC) framework. PGC introduces a novel strategy that aggregates salient features via a peak-focusing mechanism. Specifically, by employing a peak-sensitive aggregation that accentuates the most discriminative local clues, PGC leverages these critical signals to calibrate the global decision. This approach recovers subtle patterns that would otherwise be submerged in the global context. Furthermore, to better simulate real-world threats, we introduce the CommGen15
dataset, a challenging
benchmark comprising samples from 15 commercial models. Extensive experiments demonstrate that PGC achieves state-of-the-art performance. Specifically, it improves mean accuracy by +12.3% on our CommGen15
dataset, and sets new records on standard
benchmarks, including GenImage (+2.1%), AIGI (+3.5%), and UniversalFakeDetect (+1.7%). Code is available at https://github.com/xiaoyu6868/PGC.
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All authors Xiaoyu Zhou, Jianwei Fei, Peipeng Yu, Jingchang Xie, Chong Cheng, Zhihua Xia
Computer Vision
RankE: End-to-End Post-Training for Discrete Text-to-Image Generation with Decoder Co-Evolution
Siyong Jian, Siyuan Li, Luyuan Zhang et al. (+5 more)
Discrete autoregressive (AR) text-to-image (T2I) models pair a VQ tokenizer with an AR policy, and current post-training pipelines optimize only the policy while keeping the VQ decoder frozen. Recent
diffusion T2I work, exemplified by REPA-E, has shown that the VAE itself constitutes a key
alignment bottleneck, yet no analogous investigation exists for discrete AR models. We show that policy-only optimization induces Latent
Covariate Shift: as the policy evolves, the resulting
token distribution diverges from the ground-truth distribution on which the decoder was trained, such that reward scores improve while decoded image quality degrades. To address this mismatch, we propose RankE, the first end-to-end post-training framework for discrete T2I generation. Rather than optimizing the policy against a fixed decoder, RankE co-evolves both components through alternating optimization: each module maximizes a ranking-based
alignment objective while being regularized by a stability-preserving anchor suited to its parameter space. This co-
evolution breaks the fidelity–
alignment trade-off that plagues frozen-decoder approaches: on LlamaGen-XL (775M), standard RL improves CLIP but degrades FID, whereas RankE improves both simultaneously (FID 15.21, CLIP 33.76 on MS-COCO 30K). Consistent gains on Janus-Pro (1B) confirm that decoder co-
evolution reliably converts reward optimization into pixel-space quality improvements.
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All authors Siyong Jian, Siyuan Li, Luyuan Zhang, Zedong Wang, Xin Jin, Ying Li, Cheng Tan, Huan Wang
Computer Vision
Semantic Granularity Navigation in Image Editing
Liangsi Lu, Minzhe Guo, Xuhang Chen et al. (+1 more)
Despite the generative capabilities of
diffusion and flow models, real-image editing remains constrained by a persistent trade-off between semantic editability and structural fidelity. We trace a primary cause of this limitation to the implicit coupling of edit progress with model scale in existing paradigms. Under this coupling, stronger edits typically require visiting noisier states, which spends computation on destabilizing layout before the semantic change is well localized. We introduce NaviEdit, a training-free inference-time controller that decouples edit progress from model scale traversal through a strict self-consistency contract. NaviEdit operates at the rollout level and leaves the underlying pretrained model unchanged. It treats scale as a control input and reallocates a fixed step budget toward semantically responsive intermediate scales instead of destructive high-noise regimes. Experiments show positive average gains across compatible editors and flow backbones, supporting decoupling as a portable inference-time control principle.
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All authors Liangsi Lu, Minzhe Guo, Xuhang Chen, Yang Shi
Computer Vision
SAM-Sode: Towards Faithful Explanations for Tiny Bacteria Detection
Wanying Tan, Shuo Yan, Dazhi Huang et al. (+7 more)
Interpretability in object detection provides crucial confidence support for clinical auxiliary diagnosis. However, in tiny bacteria detection, traditional explanation methods often suffer from blurred foreground boundaries and diffuse feature attribution due to the extreme sparsity of target morphological features and severe interference from complex backgrounds. Such limitations hinder the provision of logically coherent morphological evidence. To bridge this gap, we propose a novel eXplainable AI (XAI) framework, SAM-Sode. The framework innovatively transforms initial feature attribution maps into geometry-aware
prompts, leveraging the prior knowledge of the foundation model (SAM3) to achieve spatial refinement and morphological reconstruction of the explanatory mappings. Furthermore, we introduce a dual-constraint mechanism based on physical significance and
geometric alignment to perform instance-level denoising, generating coherent explanations that better align with human expert intuition. Experimental results on our self-constructed bacteria
dataset with complex
circuit backgrounds (containing 2,524 images) and other public
datasets demonstrate that the proposed method effectively suppresses background redundancy and significantly enhances the decision-making transparency of tiny object detection.
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All authors Wanying Tan, Shuo Yan, Dazhi Huang, Yazheng Liu, Zili Shao, Rufeng Chen, Hechang Chen, Mude Shi, Tianxing Ji, Sihong Xie
Computer Vision · Cross-listed from cs.CL
Manga109-v2026: Revisiting Manga109 Annotations for Modern Manga Understanding
Jeonghun Baek, Atsuyuki Miyai, Shota Onohara et al. (+2 more)
Manga is a culturally distinctive
multimodal medium and one of the most influential forms of Japanese popular culture. As AI systems increasingly target manga understanding, OCR, and translation, Manga109 has become a foundational
dataset for manga-related AI research. However, the current Manga109
dataset contains inaccurate transcriptions and coarse annotations, which do not align well with modern OCR and
multimodal manga understanding tasks. In this work, we revisit the dialogue text annotations of Manga109 and identify five categories of annotation issues, including inaccurate transcriptions, missing text regions, overlapping dialogue and onomatopoeia, and under-segmented
speech balloons. To address these issues, we combine OCR-based issue detection and manual revision to construct Manga109-v2026, revising approximately 29,000 dialogue annotations. Our revisions better align Manga109 with modern OCR and
multimodal manga understanding systems while preserving expressive structures characteristic of manga.
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All authors Jeonghun Baek, Atsuyuki Miyai, Shota Onohara, Hikaru Ikuta, Kiyoharu Aizawa
Computer Vision
Szymon Ruciński, Pietro Bonazzi, Engin Türetken et al. (+3 more)
Ternary
Vision Transformers offer substantial model compression, however state-of-the-art methods only ternarize the encoder layers, leaving patch
embeddings, LayerNorm parameters, and classifier heads in full precision. In compact models targeting resource-constrained processors, such as microcontrollers, these remaining full-precision components determine the total
memory footprint, severely limiting deployment efficiency and on-device feasibility. In this work, we introduce a fully ternarized
Vision Transformer in which all weight
matrices and normalization parameters are ternarized, while activations remain 8-bit (FTerViT). To this end, we introduce two novel operators: TernaryBitConv2d with per-channel scaling for patch
embedding and TernaryLayerNorm. FTerViT is trained using knowledge
distillation, followed by a lightweight quantization-aware recovery phase. On a matched DeiT-S backbone, FTerViT reaches 77.47% ImageNet-1K top-1 at 5.81 MB, +3.27 pp over TerViT. With the stronger DeiT-III-S backbone at 384x384 resolution, our ternary W2A8 model achieves 82.43% at 6.09 MB (~15x compression, -2.42 pp vs. FP32). This backbone- and resolution-advantaged result exceeds prior ternary ViTs by up to 8 pp. Finally, we demonstrate the first implementation of ternary
vision transformers on a dual-core Xtensa LX7 microcontroller inside the ESP32-S3 system-on-chip. By deploying FTerViT-Small (based on DeiT-III-Small at 224x224 resolution, 5.81 MB), we achieve 79.64% ImageNet-1K top-1 accuracy.
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All authors Szymon Ruciński, Pietro Bonazzi, Engin Türetken, Simon Narduzzi, Michele Magno, Nadim Maamari
Computer Vision
Comparative Analysis of Military Detection Using Drone Imagery Across Multiple Visual Spectrums
Sourov Roy Shuvo, Prajwal Panth, Rajesh Chowdhury et al. (+3 more)
In modern warfare, drones are becoming an essential part of intelligence gathering and carrying out precise attacks in different kinds of hostile environments. Their ability to operate in real-time and hostile environments from a safe distance makes them invaluable for surveillance and military operations. The KIIT-MiTA
dataset is comprised of images of different military scenarios taken from drones, and these provide a foundation for detecting military objects, but it does not take into account the various types of real-world scenarios. With that in mind, to evaluate how the models are performing under varying conditions, four different types of
datasets are created: Gray Scale, Thermal
Vision, Night
Vision, and Obscura
Vision. These simulate the real-world environments such as low visibility, heat-based imagery, and nighttime conditions. The YOLOv11-small model is trained and used to detect objects across diverse settings. This research boosts the performance and reliability of drone-based operations by contributing to the development of advanced detection systems in both defensive and offensive missions.
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All authors Sourov Roy Shuvo, Prajwal Panth, Rajesh Chowdhury, Sorup Chakraborty, Sudip Chakrabarty, Prasant Kumar Pattnaik
Computer Vision
Distill to Think, Foresee to Act: Cognitive-Physical Reinforcement Learning for Autonomous Driving
Yang Wu, Qiang Meng, Zhaojiang Liu et al. (+3 more)
Current end-to-end autonomous driving models are fundamentally constrained by the behavioral cloning ceiling of imitation learning. While reinforcement learning offers a path to smarter autonomy, it demands two missing pieces of infrastructure: (1) a cognitive foundation that understands traffic semantics and driving intent, and (2) a foresighted physical environment that can anticipate the consequences of candidate actions. To this end, we propose CoPhy, a CognitivePhysical reinforcement learning framework for autonomous driving. To distill to think, we distill VLM knowledge into the BEV encoder and then discard the VLM entirely, retaining cognitive ability at zero inference cost while releasing the cognitive channel as a pluggable interface for optional human
language commands. To foresee to act, we build an auto-regressive BEV world model that explicitly predicts future semantic maps conditioned on candidate actions, serving as an interpretable physical sandbox from which safety metrics are directly derived. Built upon this dual infrastructure, we optimize the driving policy via GRPO with a novel dual-reward mechanism: a physical reward derived from BEV rollouts enforces hard safety constraints, while a cognitive reward from a
language-aligned scorer ensures intent compliance. Extensive experiments demonstrate that CoPhy not only achieves state-of-the-art results on NAVSIM v1 and v2
benchmarks, but also enables safer driving via cognitively informed scene compliance and flexible intent control through user-defined
language instructions. Project page: https://wuyang98.github.io/CoPhy/.
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All authors Yang Wu, Qiang Meng, Zhaojiang Liu, Youquan Liu, Jian Yang, Jin Xie
Computer Vision
SurgOnAir: Hierarchy-Aware Real-Time Surgical Video Commentary
Jingyi He, Yue Zhou, Long Bai et al. (+3 more)
Understanding
surgical workflow in real time is fundamental for intelligent
surgical embodiment, where AI systems continuously perceive and respond as surgery proceeds. In the operating room, critical decisions depend on subtle, moment-to-moment changes, such as fine instrument movements and evolving tissue states, where even slight perceptual delays can limit assistance or compromise safety. Yet existing methods remain offline or operate at coarse temporal scales, generating descriptions only after processing clips, preventing immediate reaction. We address this by proposing SurgOnAir, a streaming
vision-
language model that processes frames sequentially without future access and progressively generates narration
tokens as visual input arrives. SurgOnAir achieves fine-grained frame-to-
token generation, enabling instant responsiveness to evolving
surgical dynamics. Built upon our curated hierarchical
dataset SurgOnAir-11k spanning action-, step-, and phase-level supervision, the model is trained to produce multi-level textual responses that reflect the inherent hierarchy of
surgical procedures. Furthermore, special transition
tokens are generated to explicitly mark state changes, allowing SurgOnAir to capture and signal key workflow transitions as they occur. Experiments show that SurgOnAir enables real-time understanding through a single
vision-
language model that unifies streaming across multiple hierarchies of the
surgical workflow, generating superior and hierarchy-aware narrations. The project is publicly available at https://github.com/KarolineZhy/SurgOnAir.
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All authors Jingyi He, Yue Zhou, Long Bai, Kun Yuan, Nassir Navab, Yuan Bi
Computer Vision
UniT: Unified Geometry Learning with Group Autoregressive Transformer
Haotian Wang, Yusong Huang, Zhaonian Kuang et al. (+4 more)
Recent feed-forward models have significantly advanced geometry perception for inferring dense 3D structure from sensor observations. However, its essential capabilities remain fragmented across multiple incompatible paradigms, including online perception, offline reconstruction, multi-modal integration, long-horizon scalability, and metric-scale estimation. We present UniT, a unified model built upon a novel Group Autoregressive
Transformer, which reformulates these seemingly disparate capabilities within a single framework. The key idea is to treat groups of sensor observations as the basic autoregressive units and predict the corresponding point maps in an anchor-free and scale-adaptive manner. More specifically, diverse view configurations in both online and offline settings are naturally unified within a single group autoregression process. By varying the group size, online mode operates over multiple autoregressive steps with single-frame groups, whereas offline mode aggregates a multi-frame group in a single forward pass. Meanwhile, a queue-style KV caching mechanism ensures bounded autoregressive
memory over long horizons. This is enabled by reducing long-range dependencies on early frames through anchor-free relational modeling, thereby allowing outdated
memory to be discarded on the fly. To improve metric-scale generalization across scenes, a scale-adaptive geometry loss is further introduced within this framework. It couples relative
geometric constraints with a partial absolute scale term, implicitly regularizing global scale and inducing a progressive transition from scale-invariant geometry to metric-scale solutions. Together with a dedicated modal
attention module for integrating auxiliary modalities, UniT achieves state-of-the-art performance in unified geometry perception, as validated on ten
benchmarks spanning seven representative tasks.
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All authors Haotian Wang, Yusong Huang, Zhaonian Kuang, Hongliang Lu, Xinhu Zheng, Meng Yang, Gang Hua
Computer Vision
VersusQ: Pairwise Margin Reasoning for Generalizable Video Quality Assessment
Shibei Meng, Binxin Yang, Yuan Liu et al. (+4 more)
Large
Multimodal Models (LMMs) have shown promise for
video quality assessment, but most methods still predict an absolute score for each
video. Such pointwise supervision often mixes perceptual quality with
dataset-specific calibration, including annotation protocols, rating habits, and score distributions. As a result, the learned scoring rule may work well within a
benchmark but transfer poorly across unseen domains. We argue that relative comparisons alleviate the absolute-scale calibration bias by focusing purely on perceptual differences rather than
dataset-specific rating habits. Consequently, we propose VersusQ, a pairwise margin
reasoning framework driven entirely by direct comparisons. Specifically, VersusQ performs LMM-based comparison between two
videos, reasons about their visual and temporal quality differences, and predicts a signed continuous margin that captures both the preferred choice and the degree of difference. Furthermore, to align interpretable comparison rationales with fine-grained numerical differences, we introduce Margin-Coupled GRPO, which jointly optimizes rollout-based relational
reasoning and continuous margin regression. Extensive experiments on multiple public VQA
benchmarks demonstrate that VersusQ achieves state-of-the-art performance, strong cross-domain generalization, and reliable fine-grained ranking under heterogeneous evaluation scenarios.
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All authors Shibei Meng, Binxin Yang, Yuan Liu, Jiexuan Zhang, Zhengyao Lv, Hubery Yin, Qiang Xu
Computer Vision
Linear-DPO: Linear Direct Preference Optimization for Diffusion and Flow-Matching Generative Models
Kesong Li, Yixuan Xu, Kuo-kun Tseng et al. (+3 more)
Direct Preference Optimization (DPO) is successful for
alignment in LLMs but still faces challenges in text-to-image generation. Existing studies are confined to denoising
diffusion models while overlooking flow-matching, and suffer from an objective mismatch when applying discrete NLP-based DPO to regression-based generative tasks.\ In this paper, we derive a generalized DPO objective that covers both
diffusion and flow-matching via a unified reverse-time SDE framework, and point out from a gradient perspective that the standard DPO objective is suboptimal for text-to-image generation. Consequently, we propose Linear-DPO, which replaces the aggressive sigmoid-based utility function with a sustained linear utility and incorporates an EMA-updated reference model. Qualitative and quantitative experiments on
diffusion models (SD1.5, SDXL) and flow-matching model (SD3-Medium) demonstrate the superiority of our approach over existing baselines.
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All authors Kesong Li, Yixuan Xu, Kuo-kun Tseng, Weiyi Lu, Kan Liu, Tao Lan
Computer Vision
ROAR-3D: Routing Arbitrary Views for High-Fidelity 3D Generation
Hanxiao Sun, Mingxin Yang, Shuhui Yang et al. (+5 more)
Single-image-to-3D generative models can now produce high-quality geometry, yet conditioning on a single view inevitably introduces ambiguity about unseen regions. Multi-view conditioning can reduce this ambiguity, but existing methods either require fixed canonical viewpoints or rely on external reconstruction modules that impose heavy training costs and limit generation quality. We observe that pretrained single-view models already possess strong 2D-to-3D grounding that can be reused for multi-view conditioning. However, a closer analysis reveals that their conditioning mechanism entangles orientation control with geometry transfer, two functions that conflict when images from different viewpoints are naively combined. Based on this analysis, we propose ROAR-3D, a lightweight method that upgrades a pretrained single-view model to accept an arbitrary number of unposed images. A
token-wise view router assigns each 3D latent
token to its most relevant view, implicitly establishing 2D-to-3D correspondences without explicit pose input. A dual-stream
attention design preserves the pretrained primary-view behavior while routing auxiliary views through a separate path dedicated to
geometric enrichment. An orientation perturbation strategy ensures the auxiliary path learns orientation-independent geometry transfer. These components introduce minimal trainable parameters and add negligible inference overhead relative to the single-view baseline. ROAR-3D achieves state-of-the-art multi-view 3D generation quality and supports test-time view scaling from 1 to 12+ views with consistent improvements.
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All authors Hanxiao Sun, Mingxin Yang, Shuhui Yang, Zebin He, Xintong Han, Hongbo Fu, Chunchao Guo, Wenhan Luo
Computer Vision
RCGDet3D: Rethinking 4D Radar-Camera Fusion-based 3D Object Detection with Enhanced Radar Feature Encoding
Weiyi Xiong and Bing Zhu
4D automotive radar is indispensable for autonomous driving due to its low cost and robustness, yet its point cloud sparsity challenges 3D object detection. Existing 4D radar-camera fusion methods focus on complex fusion strategies, trading inference speed for marginal gains. This trade-off hinders real-time deployment due to heavy computation on dense feature maps. In contrast, feature extraction from sparse radar points is less time-consuming but remains under-explored. This work uncovers that simply enhancing radar feature extraction can achieve comparable or even higher performance than elaborate fusion modules, while maintaining real-time performance. Based on this finding, we propose RCGDet3D, which centers on radar feature encoding and simplifies multi-modal fusion. Its encoder inherits from the efficient Gaussian Splatting-based Point Gaussian Encoder (PGE) in RadarGaussianDet3D with two key improvements. First, the Ray-centric PGE (R-PGE) predicts Gaussian attributes in ray-aligned coordinate systems before unifying them to Bird's-Eye View (BEV) space, significantly improving
geometric consistency and reducing learning difficulty by decoupling the coordinate transformation from representation learning. Second, a Semantic Injection (SI) module incorporates visual cues from images, producing more geometrically accurate and semantically enriched radar features. Experiments on View-of-Delft (VoD) and TJ4DRadSet show that RCGDet3D outperforms state-of-the-art methods in both accuracy and speed, setting a new
benchmark for real-time deployment.
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Computer Vision
R2AoP: Reliable and Robust Angle of Progression Estimation from Intrapartum Ultrasound
Yuanhan Wang, Yifei Chen, Beining Wu et al. (+7 more)
Accurate estimation of the Angle of Progression (AoP) from intrapartum transperineal ultrasound is critical for objective assessment of labor progression, yet remains highly sensitive to imaging noise, boundary ambiguities, and the
geometric amplification of local
segmentation errors. We propose R2AoP, a reliable and robust AoP estimation framework that integrates structurally informed
segmentation and confidence-guided
geometric modeling to achieve stable and reproducible measurements. A three-branch local-structure-enhanced backbone improves the delineation of the pubic symphysis (PS) and fetal head (FH), while confidence-weighted contour fitting explicitly suppresses the influence of unreliable boundary points in AoP computation. To further improve performance under heterogeneous acquisition conditions, we introduce a lightweight geometry-reliable test-time adaptation strategy as an auxiliary component, enabling stable inference without target annotations. Extensive evaluations on multi-center
benchmarks demonstrate consistent reductions in AoP error and boundary metrics compared with state-of-the-art AoP methods. Our source code is available at https://github.com/baiyou1234/R2AoP.
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All authors Yuanhan Wang, Yifei Chen, Beining Wu, Mingxuan Liu, Xiaotian Hu, Chunbo Jiang, Yijin Li, Changmiao Wang, Feiwei Qin, Qiyuan Tian
Computer Vision
TextSculptor: Training and Benchmarking Scene Text Editing
Yiheng Lin, Siyu Jiao, Xiaohan Lan et al. (+12 more)
Recent advances in
Multimodal Large
Language Models (MLLMs) and
diffusion-based generative models have substantially improved
prompt-driven image editing. However, scene text editing remains challenging, as it requires models to precisely modify textual content while preserving visual realism and non-target regions. Current open-source models still lag behind proprietary systems, largely due to the scarcity of high-quality training data and the lack of standardized
benchmarks tailored to text editing. To address these challenges, we present TextSculptor, a comprehensive framework for data construction and evaluation of scene text editing. We first develop an automated data construction pipeline that combines text-aware image synthesis with programmatic text rendering and compositing. Based on this pipeline, we build TextSculpt-Data, a large-scale
dataset containing 3.2M training samples, including 1.2M OCR-verified text-to-image samples and 2M paired text editing samples with naturally aligned source-target images and strong background consistency. We further introduce TextSculpt-Bench, a
benchmark covering four fundamental text editing tasks: text addition, text replacement, text removal, and hybrid editing. To support reliable evaluation, we design a tailored protocol that measures text accuracy, visual quality, and background preservation through OCR-based text
alignment,
multimodal judgment, and background-region similarity. Extensive experiments show that TextSculptor improves open-source text editing performance and narrows the gap to proprietary models. The data and
benchmark are available at https://github.com/linyiheng123/TextSculptor.
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All authors Yiheng Lin, Siyu Jiao, Xiaohan Lan, Wei Zhou, Qi She, Fei Yu, Heyun Chen, Zhengwei Wang, Jinghuan Chen, Moran Li, Yingchen Yu, Zijian Feng, Yao Zhao, Yunchao Wei, Yujie Zhong
Computer Vision
VDFP: Video Deflickering with Flicker-banding Priors
Zhiyi Zhou, Libo Zhu, Zihan Zhou et al. (+2 more)
Capturing digital screens with smartphones frequently induces severe banding due to hardware synchronization mismatches. Existing
video restoration methods struggle with these structured, periodic luminance fluctuations, often resulting in residual artifacts or over-smoothed textures. We firstly construct DeViD, a real-world
dataset in various scenes to deal with the lack of available
datasets. Then we propose VDFP (
Video Deflickering with Flicker-banding Priors), a novel perception-guided generation framework. First, we introduce a Degradation Field Modeling Based on Rolling Shutter Mechanism (DFM) capable of synthesizing complex multi-banding scenarios. Second, we present a spatial-temporal continuous prior perception (CPP). Unlike traditional binary
segmentation, this module is optimized via a Flicker-Aware Mean Squared Error (FA-MSE) to capture the luminance transitions. By zero-initializing an augmented input layer, our model preserves pre-trained generative priors as well as spatial-temporal prior perception. Extensive experiments demonstrate that VDFP significantly outperforms other methods, eliminating complex banding with high-fidelity spatial details and temporal consistency. Our
dataset and code will be released at https://github.com/ZhiyiZZhou/VDFP.
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All authors Zhiyi Zhou, Libo Zhu, Zihan Zhou, Yulun Zhang, Xiaokang Yang
Computer Vision
SpectralEarth-FM: Bringing Hyperspectral Imagery into Multimodal Earth Observation Pretraining
Nassim Ait Ali Braham, Aaron Banze, Conrad M. Albrecht et al. (+3 more)
Earth observation (EO) foundation models (FMs) are increasingly trained on multisensor data, spanning multispectral imagery (MSI), synthetic aperture radar (SAR), and derived geospatial layers, but hyperspectral imagery (HSI) remains underrepresented. Conversely, existing hyperspectral FMs are trained on HSI alone, leaving joint pretraining and fusion of HSI with co-located EO sensors unexplored. We introduce SpectralEarth-FM, a hierarchical
transformer for multisensor EO input with heterogeneous spectral dimensionality. The architecture combines spectral tokenization for hyperspectral inputs, sensor-specific encoders, a cross-sensor fusion module, and a shared hierarchical encoder, enabling joint processing of HSI and lower-channel observations. To pretrain SpectralEarth-FM, we curate SpectralEarth-MM, a
dataset that co-locates HSI from three spaceborne sensors (EnMAP, EMIT, DESIS) with Sentinel-2, Landsat-8/9 optical imagery, Landsat land surface temperature (LST), and Sentinel-1 SAR, over common geographic footprints. It comprises approximately 2M globally distributed locations, 25M georeferenced patches, and over 40TB of data. Pretraining uses a Joint-
Embedding Predictive Architecture (JEPA)-style objective that matches representations between global views and single-sensor local views from the same location. We evaluate SpectralEarth-FM on hyperspectral downstream tasks and standard EO
benchmarks following the PANGAEA protocol, achieving state-of-the-art results across both evaluation settings.
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All authors Nassim Ait Ali Braham, Aaron Banze, Conrad M. Albrecht, Julien Mairal, Jocelyn Chanussot, Xiao Xiang Zhu
Computer Vision
Q-ARVD: Quantizing Autoregressive Video Diffusion Models
Siao Tang, Xinyin Ma, Gongfan Fang et al. (+2 more)
Autoregressive
video diffusion models (ARVDs) have emerged as a promising architecture for streaming
video generation, paving the way for real-time interactive
video generation and world modeling. Despite their potential, the substantial inference cost of ARVDs remains a major obstacle to practical deployment, making model quantization a natural direction for improving efficiency. However, quantization for ARVDs remains largely unexplored. Our empirical analysis shows that directly applying existing quantization schemes developed for standard
diffusion transformers to ARVDs leads to suboptimal performance, revealing quantization behaviors that differ from those observed in bidirectional
diffusion models. In this paper, we identify two critical challenges in quantizing ARVDs: (C1) Highly unbalanced frame-wise quantization sensitivity. Error accumulation during autoregressive generation can induce severely skewed quantization sensitivity across frames, following an exponential-like decay pattern. (C2) Prominent and heterogeneous outlier patterns in weights. Weight distributions exhibit pronounced outlier channels, whose patterns vary substantially across layer types and block depths. To address these issues, we propose Q-ARVD, a novel framework for accurate ARVD quantization. (S1) To tackle the highly unbalanced frame-wise sensitivity, Q-ARVD incorporates a final-quality aware frame-weighting mechanism into the quantization objective. (S2) To prevent heterogeneous outliers from degrading performance, Q-ARVD introduces an outlier-aware adaptive dual-scale quantization, which automatically detects the presence and quantity of outlier channels for an arbitrary layer, and isolates them to protect normal channels. Extensive experiments demonstrate the superiority of Q-ARVD.
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All authors Siao Tang, Xinyin Ma, Gongfan Fang, Xingyi Yang, Xinchao Wang
Computer Vision
Grounding Driving VLA via Inverse Kinematics
Junsung Park and Hyunjung Shim
Existing Driving VLAs predict trajectories while largely ignoring their visual
tokens – a phenomenon we trace not to insufficient training but to a structurally ill-posed task formulation. We show that trajectory recovery, when viewed through the lens of inverse kinematics, requires both a current and a future visual state as boundary conditions; existing VLAs supply only the former, which encourages the model to shortcut through ego status and text commands alone. To address this, we re-design Driving VLA in the style of an inverse kinematics solver. First, a next visual state prediction objective that requires the LLM to predict the future visual scene provides dense visual supervision and suppresses shortcut paths. Second, a separate Inverse Kinematics Network (a cross-
attention-based conditional
diffusion model) that takes only the current and future visual states as input is designed to suppress reliance on ego status and textual shortcuts during trajectory decoding. With this simple prescription alone, our 0.5B-scale model recovers visual grounding and reaches trajectory planning performance comparable to 7B–8B VLAs more than an order of magnitude larger, on both the closed-loop NAVSIM-v2 and the nuScenes
benchmarks. Extensive analysis further shows that this improvement stems from a recovered ability to exploit visual features, with the effect being most pronounced in dynamic driving situations such as turning.
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Computer Vision
Multimodal LLMs under Pairwise Modalities
Yan Li, Yunlong Deng, Yuewen Sun et al. (+3 more)
Despite the impressive results achieved by
multimodal large
language models (MLLMs), their training typically relies on jointly curated
multimodal data, requiring substantial human effort to construct multi-way aligned
datasets and thereby limiting scalability across domains. In this work, we explore training MLLMs by only leveraging multiple paired modalities as a surrogate for the full joint
multimodal distribution. Specifically, we first provide a theoretical analysis of the conditions under which the representations are identifiable with only observing pairwise modalities. Building on this analysis, we propose a representation learning framework for aligning latent representations across modalities using only pairwise data. The framework consists of two stages: latent representation
alignment and cross-modal recomposition. Specifically, in the first stage, we learn the shared latent space across modalities by both self-modal reconstruction and pair-wise contrastive learning. We also incorporate an inductive bias in the contrastive learning process by partially aligning and minimal latent specification. In stage two, we integrate the encoder of newly introduced modalities with the decoders of the pre-trained modalities to facilitate cross-modal transfer and generation. We evaluate our method by newly adding 3D point clouds and tactile modalities into pre-trained MLLMs with three modality pairs and show that, by learning an aligned latent representation space, our model achieves strong cross-modal performance.
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All authors Yan Li, Yunlong Deng, Yuewen Sun, Gongxu Luo, Kun Zhang, Guangyi Chen
Computer Vision
Dynamic Video Generation: Shaping Video Generation Across Time and Space
Shikang Zheng, Jingkai Huang, Jiacheng Liu et al. (+5 more)
Diffusion models have achieved impressive performance in
video generation, but their iterative denoising process remains computationally expensive due to the large number of
tokens processed at each timestep. Recently, progressive resolution sampling has emerged as a promising acceleration approach by reducing latent resolution in early stages. However, scaling this idea to
video generation remains challenging, as the additional temporal dimension introduces diverse spatio-temporal demands across different
videos, and compressing only a single dimension often leads to limited acceleration or degraded quality. Therefore, we propose DVG, a Dynamic
Video Generation framework that jointly allocates computation across time and space, automatically selecting content-aware acceleration strategies without manual tuning or retraining. DVG achieves near-lossless acceleration across models and tasks, reaching up to 7 times speedup on HunyuanVideo and HunyuanVideo-1.5, and 18 times when combined with
distillation, demonstrating its potential as a key component in today's large-scale efficient
video generation systems. Our code is available at https://github.com/DarrenZheng303/DVG .
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All authors Shikang Zheng, Jingkai Huang, Jiacheng Liu, Guantao Chen, Lixuan, Yuqi Lin, Peiliang Cai, Linfeng Zhang
Computer Vision
Towards Physically Consistent 4D Scene Reconstruction for Closed-loop Autonomous Driving Simulation
Bowyn Tan, Yutong Xie, Bai Huang et al. (+5 more)
High-fidelity street scene reconstruction is pivotal for end-to-end autonomous driving simulation, where novel-view synthesis (NVS) and time-varying information modeling are two fundamental capabilities to facilitate closed-loop training. However, existing 3DGS methods and their 4D extensions fail to simultaneously achieve both. To bridge this gap, we establish an information-
geometric diagnostic framework, revealing that this limitation stems from a credit assignment dilemma between spatial and temporal parameters. Specifically, the deterministic coupling between viewpoint and time in single-source observation creates a low-rank structure that induces massive null-space ambiguity between static view-dependent and dynamic time-varying components. Temporal information overshadows spatial cues, causing the estimation variance of spatial parameters to diverge. To address this issue, we propose Orthogonal Projected Gradient (OPG), a hierarchical training method designed to restore spatial identifiability. OPG prioritizes the integrity of spatial representations by securing them in an initial stage, then restricts temporal updates to the spatial null space, enabling proactive credit assignment. While OPG isolates temporal updates algebraically, Temporal Regularization Strategy is proposed to further refine the temporal solution space by imposing a smoothness constraint based on the physical prior of consistent appearance
evolution, ensuring that the reconstructed scene remains physically consistent in closed-loop simulation. Extensive experiments demonstrate that our method not only maintains stable NVS capabilities but also demonstrates superior performance in traditional observation-reproducing metrics, which indirectly reflect the capability of modeling temporal dynamics.
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All authors Bowyn Tan, Yutong Xie, Bai Huang, Fan Luo, Xiao Li, Naizheng Wang, Yang Guan, Shengbo Eben Li
Computer Vision
DySink: Dynamic Frame Sinks for Autoregressive Long Video Generation
Bo Ye, Xinyu Cui, Jian Zhao et al. (+2 more)
Autoregressive long
video generation often adopts bounded-
memory streaming for efficiency, typically combining local windows for short-term continuity with static early-frame sinks as long-range anchors. However, this fixed allocation keeps early frames cached even when the current visual state has substantially diverged from them, while discarding potentially more relevant intermediate history. As a result, the retained long-range context may become less adaptive and bias generation toward outdated cues; in severe cases, RoPE-induced phase re-
alignment can homogenize inter-head
attention and cause sink collapse, where content regresses toward sink frames. We propose DySink, a
retrieval-based framework that maintains a compact
memory bank and selects visually relevant historical frames as dynamic frame sinks. DySink couples adaptive
retrieval with a sink anomaly gate that filters retrieved context exhibiting excessive inter-head consensus, an
attention pattern associated with sink collapse. Experiments on 50–100-second
videos show that DySink achieves the highest measured temporal quality among the evaluated autoregressive baselines, while retaining competitive text
alignment and framewise quality. The code is available at https://github.com/yebo0216best/DySink.
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All authors Bo Ye, Xinyu Cui, Jian Zhao, Tong Wei, Min-Ling Zhang
Computer Vision
LiteViLNet: Lightweight Vision-LiDAR Fusion Network for Efficient Road Segmentation
Daojie Peng, Bingtao Wang, Fulong Ma et al. (+2 more)
Road
segmentation is a fundamental perception task for autonomous driving and mobile robotics, where both appearance and
geometric cues must be processed under edge-computing constraints. Existing multi-modal approaches often improve accuracy with large encoders or expensive global interaction, which limits their use on embedded platforms. We present LiteViLNet, a lightweight RGB-geometry fusion network that combines a MobileNetV3 RGB encoder with a 0.12M-parameter depth-wise-separable geometry encoder. A multi-scale feature fusion module performs modality-specific enhancement, global-query cross-modal interaction, and adaptive gating, while a depth-wise large-kernel bridge enlarges the contextual support of the deepest representation with low overhead. The resulting U-Net-style decoder uses deep supervision only during training. On the KITTI Road
benchmark, the 14.04M-parameter full model obtains $97.23\pm0.15\%$ MaxF. On the held-out ORFD test set under the released OFF-Net evaluation protocol, the full model achieves $96.74\pm0.09\%$ F-score and $93.68\pm0.18\%$ IoU. On a Jetson Orin NX, model-only PyTorch FP16 inference reaches $22.18\pm0.21$ FPS; a separate TensorRT FP16 measurement reaches $68.73\pm0.06$ FPS on the Jetson. Camera-depth adaptations and perception-and-control demonstrations on three heterogeneous
robot platforms further illustrate the portability of the dual-stream design.
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All authors Daojie Peng, Bingtao Wang, Fulong Ma, Liang Zhang, Jun Ma
Computer Vision · Cross-listed from cs.CR
Verifiable Provenance and Watermarking for Generative AI: An Evidentiary Framework for International Operational Law and Domestic Courts
Gustav Olaf Yunus Laitinen-Fredriksson Lundström-Imanov and Nurana Abdullayeva
Generative artificial intelligence now synthesizes photorealistic imagery, audio, and
video at a cost that defeats traditional forensic intuition. The legal consequences span three regimes studied so far in isolation: international operational law, domestic procedure, and product regulation. This article presents a unified evidentiary framework that maps cryptographic content provenance, robust statistical watermarking, and zero knowledge attestation to the proof requirements of each regime. We define a five tier threat model spanning naive regeneration,
adversarial laundering, cross model regeneration, active watermark removal, and insider provenance forgery. We release a public
benchmark of 12000 generated items across image, audio, and
video modalities under six laundering pipelines for 72000 evaluation samples. We evaluate four representative schemes and report true positive rate at fixed false positive rate, robustness area under the curve, computational overhead, and a regime conditioned legal sufficiency score. We translate empirical detection bounds into legal sufficiency thresholds for command decisions under the law of armed conflict, for criminal and civil admissibility under domestic procedure, and for persistence audits under the European Union Artificial Intelligence Act and analogous regimes. The result is a reproducible reference pipeline, a public
benchmark, and model annexes that lawyers, engineers, and operators can deploy together.
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Computer Vision
DAMA: Disentangled Body-Anchored Gaussians for Controllable Multi-Layered Avatars
Daniel Eskandar, Berna Kabadayi, Garvita Tiwari et al. (+1 more)
Existing 3D clothed avatar reconstruction methods achieve high visual fidelity but ignore
geometric structure and physical plausibility. They either model clothed humans as a single deformable surface or attempt garment disentanglement without enforcing
geometric constraints, resulting in ambiguous garment boundaries and no control over stacking or layer ordering. To address these limitations, we introduce DAMA (Disentangled body-Anchored Gaussians for Controllable Multi-layered Avatars), a 3D avatar reconstruction method that produces physically plausible clothed avatars through a dedicated representation and reconstruction method. At the representation level, we bind Gaussians to SMPL-X faces using barycentric in-plane coordinates and a positive normal offset. Based on this parameterization, the reconstruction method lifts 2D
segmentations to body-anchored Gaussians, refines layers using
topology-guided correction, and jointly optimizes geometry and appearance. DAMA is the first Gaussian avatar reconstruction method from multi-view images to achieve physically plausible layering, clean garment separation, and explicit stacking control. On the full 4D-DRESS
dataset (82 scans), it achieves state-of-the-art performance in geometry reconstruction, garment separation, penetration rate, and penetration depth. The representation further supports user-defined garment reordering and fast conversion of body-conforming garments to simulation-ready meshes. Project Page: https://danieleskandar.github.io/dama/
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All authors Daniel Eskandar, Berna Kabadayi, Garvita Tiwari, Gerard Pons-Moll