Computer Vision
Panoptic Pairwise Distortion Graph
Muhammad Kamran Janjua, Abdul Wahab and Bahador Rashidi
In this work, we introduce a new perspective on comparative image assessment by representing an image pair as a structured composition of its regions. In contrast, existing methods focus on whole image analysis, while implicitly relying on region-level understanding. We extend the intra-image notion of a scene graph to inter-image, and propose a novel task of Distortion Graph (DG). DG treats paired images as a structured
topology grounded in regions, and represents dense degradation information such as distortion type, severity, comparison and quality score in a compact interpretable graph structure. To realize the task of learning a distortion graph, we contribute (i) a region-level
dataset, PandaSet, (ii) a
benchmark suite, PandaBench, with varying region-level difficulty, and (iii) an efficient architecture, Panda, to generate distortion graphs. We demonstrate that PandaBench poses a significant challenge for state-of-the-art
multimodal large
language models (MLLMs) as they fail to understand region-level degradations even when fed with explicit region cues. We show that training on PandaSet or prompting with DG elicits region-wise distortion understanding, opening a new direction for fine-grained, structured pairwise image assessment.
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Computer Vision
TraversalBench: Challenging Paths to Follow for Vision Language Models
Clara Petrova, Zhuo Chen and Marin Soljačić
Vision-
language models (VLMs) perform strongly on
multimodal benchmarks, but their ability to follow complex visual paths remains under-tested. We introduce TraversalBench, a controlled
benchmark for exact visual path traversal. Each instance contains a continuous polyline with a unique start marker and labeled vertices; models must recover the ordered sequence encountered from start to finish. The
benchmark balances self-intersection count, tortuosity, vertex count, and nearby confounding lines while limiting reliance on OCR, world knowledge, or open-ended planning. We find that self-intersections are the dominant source of difficulty. A first-crossing analysis localizes failures to crossing points: performance is stable before the first crossing, then drops sharply when the model must resolve the correct continuation. Nearby confounders have weaker but compounding effects, and an auxiliary reading-order
benchmark reveals a consistent left-to-right bias. Together, these results characterize how VLMs perceive and fail on visual paths. Finally, we position TraversalBench as a new contribution to the growing line of sustained and precise visual grounding
benchmarks for VLMs. Code,
benchmark data, and rendered examples are available at https://github.com/clarapetrova/traversalbench.
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Computer Vision
LumiMotion: Improving Gaussian Relighting with Scene Dynamics
Joanna Kaleta, Piotr Wójcik, Kacper Marzol et al. (+3 more)
In 3D reconstruction, the problem of inverse rendering, namely recovering the illumination of the scene and the material properties, is fundamental. Existing Gaussian Splatting-based methods primarily target static scenes and often assume simplified or moderate lighting to avoid entangling shadows with surface appearance. This limits their ability to accurately separate lighting effects from material properties, particularly in real-world conditions. We address this limitation by leveraging dynamic elements - regions of the scene that undergo motion - as a supervisory signal for inverse rendering. Motion reveals the same surfaces under varying lighting conditions, providing stronger cues for disentangling material and illumination. This thesis is supported by our experimental results which show we improve LPIPS by 23% for albedo estimation and by 15% for scene relighting relative to next-best baseline. To this end, we introduce LumiMotion, the first Gaussian-based approach that leverages dynamics for inverse rendering and operates in arbitrary dynamic scenes. Our method learns a dynamic 2D Gaussian Splatting representation that employs a set of novel constraints which encourage the dynamic regions of the scene to deform, while keeping static regions stable. As we demonstrate, this separation is crucial for correct optimization of the albedo. Finally, we release a new synthetic
benchmark comprising five scenes under four lighting conditions, each in both static and dynamic variants, for the first time enabling systematic evaluation of inverse rendering methods in dynamic environments and challenging lighting. Link to project page: https://joaxkal.github.io/LumiMotion/
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All authors Joanna Kaleta, Piotr Wójcik, Kacper Marzol, Tomasz Trzciński, Kacper Kania, Marek Kowalski
Computer Vision
ArtiCAD: Articulated CAD Assembly Design via Multi-Agent Code Generation
Yuan Shui, Yandong Guan, Zhanwei Zhang et al. (+4 more)
Parametric Computer-Aided Design (CAD) of articulated assemblies is essential for product development, yet generating these multi-part, movable models from high-level descriptions remains unexplored. To address this, we propose ArtiCAD, the first training-free multi-
agent system capable of generating editable, articulated CAD assemblies directly from text or images. Our system divides this complex task among four specialized
agents: Design, Generation, Assembly, and Review. One of our key insights is to predict assembly relationships during the initial design stage rather than the assembly stage. By utilizing a Connector that explicitly defines attachment points and joint parameters, ArtiCAD determines these relationships before geometry generation, effectively bypassing the limited spatial
reasoning capabilities of current LLMs and VLMs. To further ensure high-quality outputs, we introduce validation steps in the generation and assembly stages, accompanied by a cross-stage rollback mechanism that accurately isolates and corrects design- and code-level errors. Additionally, a self-evolving experience store accumulates design knowledge to continuously improve performance on future tasks. Extensive evaluations on three
datasets (ArtiCAD-Bench, CADPrompt, and ACD) validate the effectiveness of our approach. We further demonstrate the applicability of ArtiCAD in requirement-driven conceptual design, physical prototyping, and the generation of
embodied AI training assets through URDF export.
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All authors Yuan Shui, Yandong Guan, Zhanwei Zhang, Juncheng Hu, Jing Zhang, Dong Xu, Qian Yu
Computer Vision · Cross-listed from cs.AI
WebForge: Breaking the Realism-Reproducibility-Scalability Trilemma in Browser Agent Benchmark
Peng Yuan, Yuyang Yin, Yuxuan Cai et al. (+1 more)
Existing browser
agent benchmarks face a fundamental trilemma: real-website
benchmarks lack reproducibility due to content drift, controlled environments sacrifice realism by omitting real-web noise, and both require costly manual curation that limits scalability. We present WebForge, the first fully automated framework that resolves this trilemma through a four-
agent pipeline – Plan, Generate, Refine, and Validate – that produces interactive, self-contained web environments end-to-end without human annotation. A seven-dimensional difficulty control framework structures task design along
navigation depth, visual complexity,
reasoning difficulty, and more, enabling systematic capability profiling beyond single aggregate scores. Using WebForge, we construct WebForge-Bench, a
benchmark of 934 tasks spanning 7 domains and 3 difficulty levels. Multi-model experiments show that difficulty stratification effectively differentiates model capabilities, while cross-domain analysis exposes capability biases invisible to aggregate metrics. Together, these results confirm that multi-dimensional evaluation reveals distinct capability profiles that a single aggregate score cannot capture. Code and
benchmark are publicly available at https://github.com/yuandaxia2001/WebForge.
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All authors Peng Yuan, Yuyang Yin, Yuxuan Cai, Zheng Wei
Computer Vision · Cross-listed from cs.AI
Back to the Barn with LLAMAs: Evolving Pretrained LLM Backbones in Finetuning Vision Language Models
Sameera Horawalavithana, Lauren Phillips, Ian Stewart et al. (+2 more)
Vision-
Language Models (VLMs) have rapidly advanced by leveraging powerful pre-trained Large
Language Models (LLMs) as core
reasoning backbones. As new and more capable LLMs emerge with improved
reasoning, instruction-following, and generalization, there is a pressing need to efficiently update existing VLMs to incorporate these advancements. However, the integration of new LLMs into VLMs, particularly how the evolving LLMs contribute to
multimodal reasoning,
alignment, and task-specific performance remains underexplored. Addressing this gap is important for VLM development, given the rapid
evolution of pretrained LLM backbones. This study presents a controlled and systematic investigation of how changes in the pretrained LLM backbone affect downstream VLM task performance. By having the
vision encoder, training data, and post-training
algorithm remain same across LLAMA-1, LLAMA-2, and LLAMA-3 based VLMs, we find that newer LLM backbones do not always lead to better VLMs, but the performance depends on the downstream VLM task. For example, in visual question and answering tasks, newer LLM backbones tend to solve different questions rather than just more questions, and our analysis shows this is driven by differences in how the models process information, including better calibrated confidence and more stable internal representations. We also find that some VLM capabilities appear only in the newest LLM generation, while tasks that depend mainly on visual understanding see little benefit from a newer LLM backbone.
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All authors Sameera Horawalavithana, Lauren Phillips, Ian Stewart, Sai Munikoti, Karl Pazdernik
Computer Vision
Energy-oriented Diffusion Bridge for Image Restoration with Foundational Diffusion Models
Jinhui Hou, Zhiyu Zhu and Junhui Hou
Diffusion bridge models have shown great promise in image restoration by explicitly connecting clean and degraded image distributions. However, they often rely on complex and high-cost trajectories, which limit both sampling efficiency and final restoration quality. To address this, we propose an Energy-oriented
diffusion Bridge (E-Bridge) framework to approximate a set of low-cost
manifold geodesic trajectories to boost the performance of the proposed method. We achieve this by designing a novel bridge process that evolves over a shorter time horizon and makes the reverse process start from an
entropy-regularized point that mixes the degraded image and Gaussian noise, which theoretically reduces the required trajectory energy. To solve this process efficiently, we draw inspiration from consistency models to learn a single-step mapping function, optimized via a continuous-time consistency objective tailored for our trajectory, so as to analytically map any state on the trajectory to the target image. Notably, the trajectory length in our framework becomes a tunable task-adaptive knob, allowing the model to adaptively balance information preservation against generative power for tasks of varying degradation, such as denoising versus super-resolution. Extensive experiments demonstrate that our E-Bridge achieves state-of-the-art performance across various image restoration tasks while enabling high-quality recovery with a single or fewer sampling steps. Our project page is https://jinnh.github.io/E-Bridge/.
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Computer Vision
MMR-AD: A Large-Scale Multimodal Dataset for Benchmarking General Anomaly Detection with Multimodal Large Language Models
Xincheng Yao, Zefeng Qian, Chao Shi et al. (+2 more)
In the progress of industrial anomaly detection, general anomaly detection (GAD) is an emerging trend and also the ultimate goal. Unlike the conventional single- and multi-class AD, general AD aims to train a general AD model that can directly detect anomalies in diverse novel classes without any retraining or fine-tuning on the target data. Recently,
Multimodal Large
Language Models (MLLMs) have shown great promise in achieving general anomaly detection due to their revolutionary visual understanding and
language reasoning capabilities. However, MLLM's general AD ability remains underexplored due to: (1) MLLMs are pretrained on amounts of data sourced from the Web, these data still have significant gaps with the data in AD scenarios. Moreover, the image-text pairs during pretraining are also not specifically for AD tasks. (2) The current mainstream AD
datasets are image-based and not yet suitable for post-training MLLMs. To facilitate MLLM-based general AD research, we present MMR-AD, which is a comprehensive
benchmark for both training and evaluating MLLM-based AD models. With MMR-AD, we reveal that the AD performance of current SOTA generalist MLLMs still falls far behind the industrial requirements. Based on MMR-AD, we also propose a baseline model, Anomaly-R1, which is a
reasoning-based AD model that learns from the CoT data in MMR-AD and is further enhanced by reinforcement learning. Extensive experiments show that our Anomaly-R1 achieves remarkable improvements over generalist MLLMs in both anomaly detection and localization.
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All authors Xincheng Yao, Zefeng Qian, Chao Shi, Jiayang Song, Chongyang Zhang
Computer Vision
Using Deep Learning Models Pretrained by Self-Supervised Learning for Protein Localization
Ben Isselmann, Dilara Göksu, Heinz Neumann et al. (+1 more)
Background: Task-specific microscopy
datasets are often small, making it difficult to train deep learning models that learn robust features. While self-supervised learning (SSL) has shown promise through pretraining on large, domain-specific
datasets, generalizability across
datasets with differing staining protocols and channel configurations remains underexplored. We investigated the generalizability of SSL models pretrained on ImageNet-1k and HPA FOV, evaluating their
embeddings on OpenCell with and without fine-tuning, two channel-mismatch strategies, and varying fine-tuning data fractions. We additionally analyzed single-
cell embeddings on a labeled OpenCell subset. Result: DINO-based ViT backbones pretrained on HPA FOV or ImageNet-1k transfer well to OpenCell even without fine-tuning. Fine-tuning further improved performance to 0.704 $\pm$ 0.027 (17 classes, multi-class labels). At the single-
cell level, the HPA single-
cell-pretrained model achieved the highest k-nearest neighbor performance across all neighborhood sizes (macro $F_1$ $\geq$ 0.515). Conclusion: SSL methods like DINO, pretrained on large domain-relevant
datasets, enable effective use of deep learning features for fine-tuning on small, task-specific microscopy
datasets.
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All authors Ben Isselmann, Dilara Göksu, Heinz Neumann, Andreas Weinmann
Computer Vision
Towards Automated Solar Panel Integrity: Hybrid Deep Feature Extraction for Advanced Surface Defect Identification
Muhammad Junaid Asif, Muhammad Saad Rafaqat, Usman Nazakat et al. (+2 more)
To ensure energy efficiency and reliable operations, it is essential to monitor
solar panels in generation plants to detect defects. It is quite labor-intensive, time consuming and costly to manually monitor large-scale
solar plants and those installed in remote areas. Manual inspection may also be susceptible to human errors. Consequently, it is necessary to create an automated, intelligent defect-detection system, that ensures continuous monitoring, early fault detection, and maximum power generation. We proposed a novel hybrid method for defect detection in
SOLAR plates by combining both handcrafted and deep learning features. Local Binary Pattern (LBP), Histogram of Gradients (HoG) and Gabor Filters were used for the extraction of handcrafted features. Deep features extracted by leveraging the use of DenseNet-169. Both handcrafted and deep features were concatenated and then fed to three distinct types of classifiers, including Support Vector Machines (SVM), Extreme Gradient Boost (XGBoost) and Light Gradient-Boosting Machine (LGBM). Experimental results evaluated on the augmented
dataset show the superior performance, especially DenseNet-169 + Gabor (SVM), had the highest scores with 99.17% accuracy which was higher than all the other systems. In general, the proposed hybrid framework offers better defect-detection accuracy, resistance, and flexibility that has a solid basis on the real-life use of the automated PV panels monitoring system.
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All authors Muhammad Junaid Asif, Muhammad Saad Rafaqat, Usman Nazakat, Uzair Khan, Rana Fayyaz Ahmad
Computer Vision
You Only Judge Once: Multi-response Reward Modeling in a Single Forward Pass
Yinuo Yang, Zixian Ma, Manasi Ganti et al. (+2 more)
We present a discriminative
multimodal reward model that scores all candidate responses in a single forward pass. Conventional discriminative reward models evaluate each response independently, requiring multiple forward passes, one for each potential response. Our approach concatenates multiple responses with separator
tokens and applies cross-
entropy over their scalar scores, enabling direct comparative
reasoning and efficient $N$-way preference learning. The multi-response design also yields up to $N\times$ wall-clock speedup and FLOPs reduction over conventional single-response scoring. To enable $N$-way reward evaluation beyond existing pairwise
benchmarks, we construct two new
benchmarks: (1) MR$^2$Bench-Image contains human-annotated rankings over responses from 8 diverse models; (2) MR$^2$Bench-
Video is a large-scale
video-based reward
benchmark derived from 94K crowdsourced pairwise human judgments over
video question-answering spanning 19 models, denoised via preference graph ensemble. Both
benchmarks provide 4-response evaluation variants sampled from the full rankings. Built on a 4B
vision-
language backbone with LoRA fine-tuning and a lightweight MLP value head, our model achieves state-of-the-art results on six
multimodal reward
benchmarks, including MR$^2$Bench-Image, MR$^2$Bench-
Video, and four other existing
benchmarks. Our model outperforms existing larger generative and discriminative reward models. We further demonstrate that our reward model, when used in reinforcement learning with GRPO, produces improved policy models that maintain performance across standard
multimodal benchmarks while substantially improving open-ended generation quality, outperforming a single-response discriminative reward model (RM) baseline by a large margin in both training stability and open-ended generation quality.
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All authors Yinuo Yang, Zixian Ma, Manasi Ganti, Jieyu Zhang, Ranjay Krishna
Computer Vision
FineEdit: Fine-Grained Image Edit with Bounding Box Guidance
Haohang Xu, Lin Liu, Zhibo Zhang et al. (+3 more)
Diffusion-based image editing models have achieved significant progress in real world applications. However, conventional models typically rely on natural
language prompts, which often lack the precision required to localize target objects. Consequently, these models struggle to maintain background consistency due to their global image regeneration paradigm. Recognizing that visual cues provide an intuitive means for users to highlight specific areas of interest, we utilize bounding boxes as guidance to explicitly define the editing target. This approach ensures that the
diffusion model can accurately localize the target while preserving background consistency. To achieve this, we propose FineEdit, a multi-level bounding box injection method that enables the model to utilize spatial conditions more effectively. To support this high precision guidance, we present FineEdit-1.2M, a large scale, fine-grained
dataset comprising 1.2 million image editing pairs with precise bounding box annotations. Furthermore, we construct a comprehensive
benchmark, termed FineEdit-Bench, which includes 1,000 images across 10 subjects to effectively evaluate region based editing capabilities. Evaluations on FineEdit-Bench demonstrate that our model significantly outperforms state-of-the-art open-source models (e.g., Qwen-Image-Edit and LongCat-Image-Edit) in instruction compliance and background preservation. Further assessments on open
benchmarks (GEdit and ImgEdit Bench) confirm its superior generalization and robustness.
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All authors Haohang Xu, Lin Liu, Zhibo Zhang, Rong Cong, Xiaopeng Zhang, Qi Tian
Computer Vision
Bootstrapping Video Semantic Segmentation Model via Distillation-assisted Test-Time Adaptation
Jihun Kim, Hoyong Kwon, Hyeokjun Kweon et al. (+1 more)
Fully supervised
Video Semantic
Segmentation (VSS) relies heavily on densely annotated
video data, limiting practical applicability. Alternatively, applying pre-trained Image Semantic
Segmentation (ISS) models frame-by-frame avoids annotation costs but ignores crucial temporal coherence. Recent foundation models such as SAM2 enable high-quality mask propagation yet remain impractical for direct VSS due to limited semantic understanding and computational overhead. In this paper, we propose DiTTA (
Distillation-assisted Test-Time Adaptation), a novel framework that converts an ISS model into a temporally-aware VSS model through efficient test-time adaptation (TTA), without annotated
videos. DiTTA distills SAM2's temporal
segmentation knowledge into the ISS model during a brief, single-pass initialization phase, complemented by a lightweight temporal fusion module to aggregate cross-frame context. Crucially, DiTTA achieves robust generalization even when adapting with highly limited partial
video snippets (e.g., initial 10%), significantly outperforming zero-shot refinement approaches that repeatedly invoke SAM2 during inference. Extensive experiments on VSPW and Cityscapes demonstrate DiTTA's effectiveness, achieving competitive or superior performance relative to fully-supervised VSS methods, thus providing a practical and annotation-free solution for real-world VSS tasks.
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All authors Jihun Kim, Hoyong Kwon, Hyeokjun Kweon, Kuk-Jin Yoon
Computer Vision
Pseudo-Unification: Entropy Probing Reveals Divergent Information Patterns in Unified Multimodal Models
Songlin Yang, Xianghao Kong and Anyi Rao
Unified
multimodal models (UMMs) were designed to combine the
reasoning ability of large
language models (LLMs) with the generation capability of
vision models. In practice, however, this synergy remains elusive: UMMs fail to transfer LLM-like
reasoning to image synthesis and exhibit divergent response behaviors. We term this phenomenon pseudo-unification. Diagnosing its internal causes is important, but existing probing methods either lack model-internal insight or ignore
prompt-response dependencies. To address these limitations, we propose an information-theoretic probing framework that jointly analyzes how UMMs encode inputs and generate outputs. Applied to ten representative UMMs, our framework reveals that pseudo-unification stems from a dual divergence: (i) Modality-Asymmetric Encoding, where
vision and
language follow different
entropy trajectories, and (ii) Pattern-Split Response, where text generation exhibits high-
entropy creativity while image synthesis enforces low-
entropy fidelity. Only models that unify both sides (e.g., via contextual prediction) achieve more genuine unification, enabling stronger
reasoning-based text-to-image generation even with fewer parameters. Our work provides the first model-internal probing of unification, demonstrating that real
multimodal synergy requires consistency in information flow, not just shared parameters.
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Computer Vision
Progressive Deep Learning for Automated Spheno-Occipital Synchondrosis Maturation Assessment
Omid Halimi Milani, Amanda Nikho, Marouane Tliba et al. (+4 more)
Accurate assessment of spheno-occipital synchondrosis (SOS) maturation is a key indicator of craniofacial growth and a critical determinant for orthodontic and
surgical timing. However, SOS staging from cone-beam CT (CBCT) relies on subtle, continuously evolving morphological cues, leading to high inter-observer variability and poor reproducibility, especially at transitional fusion stages. We frame SOS assessment as a fine-grained visual recognition problem and propose a progressive representation-learning framework that explicitly mirrors how expert clinicians reason about synchondral fusion: from coarse anatomical structure to increasingly subtle patterns of closure. Rather than training a full-capacity network end-to-end, we sequentially grow the model by activating deeper blocks over time, allowing early layers to first encode stable cranial base morphology before higher-level layers specialize in discriminating adjacent maturation stages. This yields a curriculum over network depth that aligns deep feature learning with the biological continuum of SOS fusion. Extensive experiments across convolutional and
transformer-based architectures show that this expert-inspired training strategy produces more stable optimization and consistently higher accuracy than standard training, particularly for ambiguous intermediate stages. Importantly, these gains are achieved without changing network architectures or loss functions, demonstrating that training dynamics alone can substantially improve anatomical representation learning. The proposed framework establishes a principled link between expert dental intuition and deep visual representations, enabling robust, data-efficient SOS staging from CBCT and offering a general strategy for modeling other continuous biological processes in medical imaging.
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All authors Omid Halimi Milani, Amanda Nikho, Marouane Tliba, Lauren Mills, Emadeldeen Hamdan, Ahmet Enis Cetin, Mohammed H. Elnagar
Computer Vision
AmodalSVG: Amodal Image Vectorization via Semantic Layer Peeling
Juncheng Hu, Ziteng Xue, Guotao Liang et al. (+5 more)
We introduce AmodalSVG, a new framework for amodal image vectorization that produces semantically organized and geometrically complete SVG representations from natural images. Existing vectorization methods operate under a modal paradigm: tracing only visible pixels and disregarding occlusion. Consequently, the resulting SVGs are semantically entangled and geometrically incomplete, limiting SVG's structural editability. In contrast, AmodalSVG reconstructs full object geometries, including occluded regions, into independent, editable vector layers. To achieve this, AmodalSVG reformulates image vectorization as a two-stage framework, performing semantic decoupling and completion in the raster domain to produce amodally complete semantic layers, which are then independently vectorized. In the first stage, we introduce Semantic Layer Peeling (SLP), a VLM-guided strategy that progressively decomposes an image into semantically coherent layers. By hybrid inpainting, SLP recovers complete object appearances under occlusions, enabling explicit semantic decoupling. To vectorize these layers efficiently, we propose Adaptive Layered Vectorization (ALV), which dynamically modulates the primitive budget via an error-budget-driven adjustment mechanism. Extensive experiments demonstrate that AmodalSVG significantly outperforms prior methods in visual fidelity. Moreover, the resulting amodal layers enable object-level editing directly in the vector domain, capabilities not supported by existing vectorization approaches. Code will be released upon acceptance.
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All authors Juncheng Hu, Ziteng Xue, Guotao Liang, Anran Qi, Buyu Li, Sheng Wang, Dong Xu, Qian Yu
Computer Vision · Cross-listed from cs.CR
QShield: Securing Neural Networks Against Adversarial Attacks using Quantum Circuits
Navid Azimi, Aditya Prakash, Yao Wang et al. (+1 more)
Deep neural networks remain highly vulnerable to
adversarial perturbations, limiting their reliability in security- and safety-critical applications. To address this challenge, we introduce QShield, a modular hybrid
quantum-classical neural network (HQCNN) architecture designed to enhance the
adversarial robustness of classical deep learning models. QShield integrates a conventional convolutional neural network (CNN) backbone for feature extraction with a
quantum processing module that encodes the extracted features into
quantum states, applies structured
entanglement operations under realistic noise models, and outputs a hybrid prediction through a dynamically weighted fusion mechanism implemented via a lightweight multilayer perceptron (MLP). We systematically evaluate both classical and hybrid
quantum-classical models on the MNIST, OrganAMNIST, and CIFAR-10
datasets, using a comprehensive set of robustness, efficiency, and computational performance metrics. Our results demonstrate that classical models are highly vulnerable to
adversarial attacks, whereas the proposed hybrid models with
entanglement patterns maintain high predictive accuracy while substantially reducing attack success rates across a wide range of
adversarial attacks. Furthermore, the proposed hybrid architecture significantly increased the computational cost required to generate
adversarial examples, thereby introducing an additional layer of defense. These findings indicate that the proposed modular hybrid architecture achieves a practical balance between predictive accuracy and
adversarial robustness, positioning it as a promising approach for secure and reliable machine learning in sensitive and safety-critical applications.
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All authors Navid Azimi, Aditya Prakash, Yao Wang, Li Xiong
Computer Vision
LiveGesture Streamable Co-Speech Gesture Generation Model
Muhammad Usama Saleem, Mayur Jagdishbhai Patel, Ekkasit Pinyoanuntapong et al. (+6 more)
We propose LiveGesture, the first fully streamable,
speech-driven full-body gesture generation framework that operates with zero look-ahead and supports arbitrary sequence length. Unlike existing co-
speech gesture methods, which are designed for offline generation and either treat body regions independently or entangle all joints within a single model, LiveGesture is built from the ground up for causal, region-coordinated motion generation. LiveGesture consists of two main modules: the Streamable Vector Quantized Motion Tokenizer (SVQ) and the Hierarchical Autoregressive
Transformer (HAR). The SVQ tokenizer converts the motion sequence of each body region into causal, discrete motion
tokens, enabling real-time, streamable
token decoding. On top of SVQ, HAR employs region-expert autoregressive (xAR)
transformers to model expressive, fine-grained motion dynamics for each body region. A causal spatio-temporal fusion module (xAR Fusion) then captures and integrates correlated motion dynamics across regions. Both xAR and xAR Fusion are conditioned on live, continuously arriving audio signals encoded by a streamable causal audio encoder. To enhance robustness under streaming noise and prediction errors, we introduce autoregressive masking training, which leverages uncertainty-guided
token masking and random region masking to expose the model to imperfect, partially erroneous histories during training. Experiments on the BEAT2
dataset demonstrate that LiveGesture produces coherent, diverse, and beat-synchronous full-body gestures in real time, matching or surpassing state-of-the-art offline methods under true zero look-ahead conditions.
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All authors Muhammad Usama Saleem, Mayur Jagdishbhai Patel, Ekkasit Pinyoanuntapong, Zhongxing Qin, Li Yang, Hongfei Xue, Ahmed Helmy, Chen Chen, Pu Wang
Computer Vision
ReXSonoVQA: A Video QA Benchmark for Procedure-Centric Ultrasound Understanding
Xucheng Wang, Xiaoman Zhang, Sung Eun Kim et al. (+2 more)
Ultrasound acquisition requires skilled probe manipulation and real-time adjustments.
Vision-
language models (VLMs) could enable autonomous ultrasound systems, but existing
benchmarks evaluate only static images, not dynamic procedural understanding. We introduce ReXSonoVQA, a
video QA
benchmark with 514
video clips and 514 questions (249 MCQ, 265 free-response) targeting three competencies: Action-Goal
Reasoning, Artifact Resolution & Optimization, and Procedure Context & Planning. Zero-shot evaluation of Gemini 3 Pro, Qwen3.5-397B, LLaVA-
Video-72B, and Seed 2.0 Pro shows VLMs can extract some procedural information, but troubleshooting questions remain challenging with minimal gains over text-only baselines, exposing limitations in causal
reasoning. ReXSonoVQA enables developing perception systems for ultrasound training, guidance, and robotic automation.
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All authors Xucheng Wang, Xiaoman Zhang, Sung Eun Kim, Ankit Pal, Pranav Rajpurkar
Computer Vision
TAMISeg: Text-Aligned Multi-scale Medical Image Segmentation with Semantic Encoder Distillation
Qiang Gao, Yi Wang, Yong Zhang et al. (+4 more)
Medical image
segmentation remains challenging due to limited fine-grained annotations, complex anatomical structures, and image degradation from noise, low contrast, or illumination variation. We propose TAMISeg, a text-guided
segmentation framework that incorporates clinical
language prompts and semantic
distillation as auxiliary semantic cues to enhance visual understanding and reduce reliance on pixel-level fine-grained annotations. TAMISeg integrates three core components: a consistency-aware encoder pretrained with strong perturbations for robust feature extraction, a semantic encoder
distillation module with supervision from a frozen DINOv3 teacher to enhance semantic discriminability, and a scale-adaptive decoder that segments anatomical structures across different spatial scales. Experiments on the Kvasir-SEG, MosMedData+, and QaTa-COV19
datasets demonstrate that TAMISeg consistently outperforms existing uni-modal and multi-modal methods in both qualitative and quantitative evaluations. Code will be made publicly available at https://github.com/qczggaoqiang/TAMISeg.
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All authors Qiang Gao, Yi Wang, Yong Zhang, Yong Li, Yongbing Deng, Lan Du, Cunjian Chen
Computer Vision
STGV: Spatio-Temporal Hash Encoding for Gaussian-based Video Representation
Jierun Lin, Jiacong Chen, Qingyu Mao et al. (+4 more)
2D Gaussian Splatting (2DGS) has recently become a promising paradigm for high-quality
video representation. However, existing methods employ content-agnostic or spatio-temporal feature overlapping
embeddings to predict canonical Gaussian primitive deformations, which entangles static and dynamic components in
videos and prevents modeling their distinct properties effectively. These result in inaccurate predictions for spatio-temporal deformations and unsatisfactory representation quality. To address these problems, this paper proposes a Spatio-Temporal hash encoding framework for Gaussian-based
Video representation (STGV). By decomposing
video features into learnable 2D spatial and 3D temporal hash encodings, STGV effectively facilitates the learning of motion patterns for dynamic components while maintaining background details for static elements. In addition, we construct a more stable and consistent initial canonical Gaussian representation through a key frame canonical initialization strategy, preventing from feature overlapping and a structurally incoherent geometry representation. Experimental results demonstrate that our method attains better
video representation quality (+0.98 PSNR) against other Gaussian-based methods and achieves competitive performance in downstream
video tasks.
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All authors Jierun Lin, Jiacong Chen, Qingyu Mao, Shuai Liu, Xiandong Meng, Fanyang Meng, Yongsheng Liang
Computer Vision
Evaluating the Impact of Medical Image Reconstruction on Downstream AI Fairness and Performance
Matteo Wohlrapp, Niklas Bubeck, Daniel Rueckert et al. (+1 more)
AI-based image reconstruction models are increasingly deployed in clinical workflows to improve image quality from noisy data, such as low-dose X-rays or accelerated MRI scans. However, these models are typically evaluated using pixel-level metrics like PSNR, leaving their impact on downstream diagnostic performance and
fairness unclear. We introduce a scalable evaluation framework that applies reconstruction and diagnostic AI models in tandem, which we apply to two tasks (classification,
segmentation), three reconstruction approaches (U-Net, GAN,
diffusion), and two data types (X-ray, MRI) to assess the potential downstream implications of reconstruction. We find that conventional reconstruction metrics poorly track task performance, where diagnostic accuracy remains largely stable even as reconstruction PSNR declines with increasing image noise.
Fairness metrics exhibit greater variability, with reconstruction sometimes amplifying demographic biases, particularly regarding patient sex. However, the overall magnitude of this additional bias is modest compared to the inherent biases already present in diagnostic models. To explore potential bias mitigation, we adapt two strategies from classification literature to the reconstruction setting, but observe limited efficacy. Overall, our findings emphasize the importance of holistic performance and
fairness assessments throughout the entire medical imaging workflow, especially as generative reconstruction models are increasingly deployed.
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All authors Matteo Wohlrapp, Niklas Bubeck, Daniel Rueckert, William Lotter
Computer Vision
EviRCOD: Evidence-Guided Probabilistic Decoding for Referring Camouflaged Object Detection
Ye Wang, Kai Huang, Sumin Shen et al. (+1 more)
Referring Camouflaged Object Detection (Ref-COD) focuses on segmenting specific camouflaged targets in a query image using category-aligned references. Despite recent advances, existing methods struggle with reference-target semantic
alignment, explicit uncertainty modeling, and robust boundary preservation. To address these issues, we propose EviRCOD, an integrated framework consisting of three core components: (1) a Reference-Guided Deformable Encoder (RGDE) that employs hierarchical reference-driven modulation and multi-scale deformable aggregation to inject semantic priors and align cross-scale representations; (2) an Uncertainty-Aware Evidential Decoder (UAED) that incorporates Dirichlet evidence estimation into hierarchical decoding to model uncertainty and propagate confidence across scales; and (3) a Boundary-Aware Refinement Module (BARM) that selectively enhances ambiguous boundaries by exploiting low-level edge cues and prediction confidence. Experiments on the Ref-COD
benchmark demonstrate that EviRCOD achieves state-of-the-art detection performance while providing well-calibrated uncertainty estimates. Code is available at: https://github.com/blueecoffee/EviRCOD.
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All authors Ye Wang, Kai Huang, Sumin Shen, Chenyang Ma
Computer Vision
Product Review Based on Optimized Facial Expression Detection
Vikrant Chaugule, Abhishek D, Aadheeshwar Vijayakumar et al. (+2 more)
This paper proposes a method to review public acceptance of products based on their brand by analyzing the facial expression of the customer intending to buy the product from a supermarket or hypermarket. In such cases, facial expression recognition plays a significant role in product review. Here, facial expression detection is performed by extracting feature points using a modified Harris
algorithm. The modified Harris
algorithm reduced the time complexity of the existing feature extraction Harris
Algorithm. A comparison of time complexities of existing
algorithms is done with proposed
algorithm. The
algorithm proved to be significantly faster and nearly accurate for the needed application by reducing the time complexity for corner points detection.
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All authors Vikrant Chaugule, Abhishek D, Aadheeshwar Vijayakumar, Pravin Bhaskar Ramteke, Shashidhar G. Koolagudi
Computer Vision
LRD-Net: A Lightweight Real-Centered Detection Network for Cross-Domain Face Forgery Detection
Xuecen Zhang and Vipin Chaudhary
The rapid advancement of
diffusion-based generative models has made face forgery detection a critical challenge in digital forensics. Current detection methods face two fundamental limitations: poor cross-domain generalization when encountering unseen forgery types, and substantial computational overhead that hinders deployment on resource-constrained devices. We propose LRD-Net (Lightweight Real-centered Detection Network), a novel framework that addresses both challenges simultaneously. Unlike existing dual-branch approaches that process spatial and frequency information independently, LRD-Net adopts a sequential frequency-guided architecture where a lightweight Multi-Scale Wavelet Guidance Module generates
attention signals that condition a MobileNetV3-based spatial backbone. This design enables effective exploitation of frequency-domain cues while avoiding the redundancy of parallel feature extraction. Furthermore, LRD-Net employs a real-centered learning strategy with exponential moving average prototype updates and drift regularization, anchoring representations around authentic facial images rather than modeling diverse forgery patterns. Extensive experiments on the DiFF
benchmark demonstrate that LRD-Net achieves state-of-the-art cross-domain detection accuracy, consistently outperforming existing methods. Critically, LRD-Net accomplishes this with only 2.63M parameters - approximately 9x fewer than conventional approaches - while achieving over 8x faster training and nearly 10x faster inference. These results demonstrate that robust cross-domain face forgery detection can be achieved without sacrificing computational efficiency, making LRD-Net suitable for real-time deployment in mobile authentication systems and resource-constrained environments.
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