Computer Vision · Cross-listed from eess.IV
Entropy-Guided Agreement-Diversity: A Semi-Supervised Active Learning Framework for Fetal Head Segmentation in Ultrasound
Fangyijie Wang, Siteng Ma, Guénolé Silvestre et al. (+1 more)
Fetal ultrasound (US) data is often limited due to
privacy and regulatory restrictions, posing challenges for training deep learning (DL) models. While semi-supervised learning (SSL) is commonly used for fetal US image analysis, existing SSL methods typically rely on random limited selection, which can lead to suboptimal model performance by overfitting to homogeneous labeled data. To address this, we propose a two-stage Active Learning (AL) sampler,
Entropy-Guided Agreement-Diversity (EGAD), for fetal head
segmentation. Our method first selects the most uncertain samples using predictive
entropy, and then refines the final selection using the agreement-diversity score combining cosine similarity and mutual information. Additionally, our SSL framework employs a consistency learning strategy with feature downsampling to further enhance
segmentation performance. In experiments, SSL-EGAD achieves an average Dice score of 94.57% and 96.32% on two public
datasets for fetal head
segmentation, using 5% and 10% labeled data for training, respectively. Our method outperforms current SSL models and showcases consistent robustness across diverse pregnancy stage data. The code is available on \href{https://github.com/13204942/Semi-supervised-EGAD}{GitHub}.
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All authors Fangyijie Wang, Siteng Ma, Guénolé Silvestre, Kathleen M. Curran
Computer Vision
Coronary Artery Segmentation and Vessel-Type Classification in X-Ray Angiography
Mehdi Yousefzadeh, Siavash Shirzadeh Barough, Ashkan Fakharifar et al. (+12 more)
X-ray coronary angiography (XCA) is the clinical reference standard for assessing coronary artery disease, yet quantitative analysis is limited by the difficulty of robust vessel
segmentation in routine data. Low contrast, motion, foreshortening, overlap, and catheter confounding degrade
segmentation and contribute to domain shift across centers. Reliable
segmentation, together with vessel-type labeling, enables vessel-specific coronary analytics and downstream measurements that depend on anatomical localization. From 670 cine sequences (407 subjects), we select a best frame near peak opacification using a low-intensity histogram criterion and apply joint super-resolution and enhancement. We
benchmark classical Meijering, Frangi, and Sato vesselness filters under per-image oracle tuning, a single global mean setting, and per-image parameter prediction via Support Vector Regression (SVR). Neural baselines include U-Net, FPN, and a Swin
Transformer, trained with coronary-only and merged coronary+catheter supervision. A second stage assigns vessel identity (LAD, LCX, RCA). External evaluation uses the public DCA1 cohort. SVR per-image tuning improves Dice over global means for all classical filters (e.g., Frangi: 0.759 vs. 0.741). Among deep models, FPN attains 0.914+/-0.007 Dice (coronary-only), and merged coronary+catheter labels further improve to 0.931+/-0.006. On DCA1 as a strict external test, Dice drops to 0.798 (coronary-only) and 0.814 (merged), while light in-domain fine-tuning recovers to 0.881+/-0.014 and 0.882+/-0.015. Vessel-type labeling achieves 98.5% accuracy (Dice 0.844) for RCA, 95.4% (0.786) for LAD, and 96.2% (0.794) for LCX. Learned per-image tuning strengthens classical pipelines, while high-resolution FPN models and merged-label supervision improve stability and external transfer with modest adaptation.
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All authors Mehdi Yousefzadeh, Siavash Shirzadeh Barough, Ashkan Fakharifar, Yashar Tayyarazad, Narges Eghbali, Mohaddeseh Mozaffari, Hoda Taeb, Negar Sadat Rafiee Tabatabaee, Parsa Esfahanian, Ghazaleh Sadeghi Gohar, Amineh Safavirad, Saeideh Mazloomzadeh, Ehsan khalilipur, Armin Elahifar, Majid Maleki
Computer Vision
CoT-Seg: Rethinking Segmentation with Chain-of-Thought Reasoning and Self-Correction
Shiu-hong Kao, Chak Ho Huang, Huaiqian Liu et al. (+2 more)
Existing works of
reasoning segmentation often fall short in complex cases, particularly when addressing complicated queries and out-of-domain images. Inspired by the chain-of-thought
reasoning, where harder problems require longer thinking steps/time, this paper aims to explore a system that can think step-by-step, look up information if needed, generate results, self-evaluate its own results, and refine the results, in the same way humans approach harder questions. We introduce CoT-Seg, a training-free framework that rethinks
reasoning segmentation by combining chain-of-thought
reasoning with self-correction. Instead of fine-tuning, CoT-Seg leverages the inherent
reasoning ability of pre-trained MLLMs (GPT-4o) to decompose queries into meta-instructions, extract fine-grained semantics from images, and identify target objects even under implicit or complex
prompts. Moreover, CoT-Seg incorporates a self-correction stage: the model evaluates its own
segmentation against the original query and
reasoning trace, identifies mismatches, and iteratively refines the mask. This tight integration of
reasoning and correction significantly improves reliability and robustness, especially in ambiguous or error-prone cases. Furthermore, our CoT-Seg framework allows easy incorporation of
retrieval-augmented
reasoning, enabling the system to access external knowledge when the input lacks sufficient information. To showcase CoT-Seg's ability to handle very challenging cases ,we introduce a new
dataset ReasonSeg-Hard. Our results highlight that combining chain-of-thought
reasoning, self-correction, offers a powerful paradigm for
vision-
language integration driven
segmentation.
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All authors Shiu-hong Kao, Chak Ho Huang, Huaiqian Liu, Yu-Wing Tai, Chi-Keung Tang
Computer Vision
Cloud-Enabled IoT System for Real-Time Environmental Monitoring and Remote Device Control Using Firebase
Abdul Hasib and A. S. M. Ahsanul Sarkar Akib
The proliferation of Internet of Things (IoT) devices has created unprecedented opportunities for remote monitoring and control applications across various domains. Traditional monitoring systems often suffer from limitations in real-time data accessibility, remote controllability, and cloud integration. This paper presents a cloud-enabled IoT system that leverages Google's Firebase Realtime Database for synchronized environmental monitoring and device control. The system utilizes an ESP32 microcontroller to interface with a DHT22 temperature/humidity sensor and an HC-SR04 ultrasonic distance sensor, while enabling remote control of two LED indicators through a cloud-based interface. Real-time sensor data is transmitted to Firebase, providing a synchronized platform accessible from multiple devices simultaneously. Experimental results demonstrate reliable data transmission with 99.2% success rate, real-time control latency under 1.5 seconds, and persistent data storage for historical analysis. The system architecture offers a scalable framework for various IoT applications, from smart home automation to industrial monitoring, with a total implementation cost of \$32.50. The integration of Firebase provides robust cloud capabilities without requiring complex server infrastructure, making advanced IoT applications accessible to developers and researchers with limited resources.
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Computer Vision
Source-Free Domain Adaptation by Optimizing Batch-Wise Cosine Similarity
Harsharaj Pathak and Vineeth N Balasubramanian
Source-Free Domain Adaptation (SFDA) is an emerging area of research that aims to adapt a model trained on a labeled source domain to an unlabeled target domain without accessing the source data. Most of the successful methods in this area rely on the concept of neighborhood consistency but are prone to errors due to misleading neighborhood information. In this paper, we explore this approach from the point of view of learning more informative clusters and mitigating the effect of noisy neighbors using a concept called neighborhood signature, and demonstrate that adaptation can be achieved using just a single loss term tailored to optimize the similarity and dissimilarity of predictions of samples in the target domain. In particular, our proposed method outperforms existing methods in the challenging VisDA
dataset while also yielding competitive results on other
benchmark datasets.
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Computer Vision
HAAF: Hierarchical Adaptation and Alignment of Foundation Models for Few-Shot Pathology Anomaly Detection
Chunze Yang, Wenjie Zhao, Yue Tang et al. (+5 more)
Precision pathology relies on detecting fine-grained morphological abnormalities within specific Regions of Interest (ROIs), as these local, texture-rich cues - rather than global slide contexts - drive expert diagnostic
reasoning. While
Vision-
Language (V-L) models promise data efficiency by leveraging semantic priors, adapting them faces a critical Granularity Mismatch, where generic representations fail to resolve such subtle defects. Current adaptation methods often treat modalities as independent streams, failing to ground semantic
prompts in ROI-specific visual contexts. To bridge this gap, we propose the Hierarchical Adaptation and
Alignment Framework (HAAF). At its core is a novel Cross-Level Scaled
Alignment (CLSA) mechanism that enforces a sequential calibration order: visual features first inject context into text
prompts to generate content-adaptive descriptors, which then spatially guide the visual encoder to spotlight anomalies. Additionally, a dual-branch inference strategy integrates semantic scores with
geometric prototypes to ensure stability in few-shot settings. Experiments on four
benchmarks show HAAF significantly outperforms state-of-the-art methods and effectively scales with domain-specific backbones (e.g., CONCH) in low-resource scenarios.
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All authors Chunze Yang, Wenjie Zhao, Yue Tang, Junbo Lu, Jiusong Ge, Qidong Liu, Zeyu Gao, Chen Li
Computer Vision
ReLE: A Scalable System and Structured Benchmark for Diagnosing Capability Anisotropy in Chinese LLMs
Rui Fang, Jian Li, Wei Chen et al. (+4 more)
Large
Language Models (LLMs) have achieved rapid progress in Chinese
language understanding, yet accurately evaluating their capabilities remains challenged by
benchmark saturation and prohibitive computational costs. While static leaderboards provide snapshot rankings, they often mask the structural trade-offs between capabilities. In this work, we present ReLE (Robust Efficient Live Evaluation), a scalable system designed to diagnose Capability Anisotropy, the non-uniformity of model performance across domains. Using ReLE, we evaluate 304 models (189 commercial, 115 open-source) across a Domain $\times$ Capability orthogonal
matrix comprising 207,843 samples. We introduce two methodological contributions to address current evaluation pitfalls: (1) A Symbolic-Grounded Hybrid Scoring Mechanism that eliminates
embedding-based false positives in
reasoning tasks; (2) A Dynamic Variance-Aware Scheduler based on Neyman allocation with noise correction, which reduces compute costs by 70% compared to full-pass evaluations while maintaining a ranking correlation of $\rho=0.96$. Our analysis reveals that aggregate rankings are highly sensitive to weighting schemes: models exhibit a Rank Stability Amplitude (RSA) of 11.4 in ReLE versus $\sim$5.0 in traditional
benchmarks, confirming that modern models are highly specialized rather than generally superior. We position ReLE not as a replacement for comprehensive static
benchmarks, but as a high-frequency diagnostic monitor for the evolving model landscape.
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All authors Rui Fang, Jian Li, Wei Chen, Bin Hu, Ying-Cong Chen, Xin Tang, Liang Diao
Computer Vision
SMV-EAR: Bring Spatiotemporal Multi-View Representation Learning into Efficient Event-Based Action Recognition
Rui Fan and Weidong Hao
Event cameras action recognition (EAR) offers compelling
privacy-protecting and efficiency advantages, where temporal motion dynamics is of great importance. Existing spatiotemporal multi-view representation learning (SMVRL) methods for event-based object recognition (EOR) offer promising solutions by projecting H-W-T events along spatial axis H and W, yet are limited by its translation-variant spatial binning representation and naive early concatenation fusion architecture. This paper reexamines the key SMVRL design stages for EAR and propose: (i) a principled spatiotemporal multi-view representation through translation-invariant dense conversion of sparse events, (ii) a dual-branch, dynamic fusion architecture that models sample-wise complementarity between motion features from different views, and (iii) a bio-inspired temporal warping augmentation that mimics speed variability of real-world human actions. On three challenging EAR
datasets of HARDVS, DailyDVS-200 and THU-EACT-50-CHL, we show +7.0%, +10.7%, and +10.2% Top-1 accuracy gains over existing SMVRL EOR method with surprising 30.1% reduced parameters and 35.7% lower computations, establishing our framework as a novel and powerful EAR paradigm.
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Computer Vision
ONRW: Optimizing inversion noise for high-quality and robust watermark
Xuan Ding, Xiu Yan, Chuanlong Xie et al. (+1 more)
Watermarking methods have always been effective means of protecting intellectual property, yet they face significant challenges. Although existing deep learning-based watermarking systems can hide watermarks in images with minimal impact on image quality, they often lack robustness when encountering image corruptions during transmission, which undermines their practical application value. To this end, we propose a high-quality and robust watermark framework based on the
diffusion model. Our method first converts the clean image into inversion noise through a null-text optimization process, and after optimizing the inversion noise in the latent space, it produces a high-quality watermarked image through an iterative denoising process of the
diffusion model. The iterative denoising process serves as a powerful purification mechanism, ensuring both the visual quality of the watermarked image and enhancing the robustness of the watermark against various corruptions. To prevent the optimizing of inversion noise from distorting the original semantics of the image, we specifically introduced self-
attention constraints and pseudo-mask strategies. Extensive experimental results demonstrate the superior performance of our method against various image corruptions. In particular, our method outperforms the stable signature method by an average of 10% across 12 different image transformations on COCO
datasets. Our codes are available at https://github.com/920927/ONRW.
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All authors Xuan Ding, Xiu Yan, Chuanlong Xie, Yao Zhu
Computer Vision
Physical Prompt Injection Attacks on Large Vision-Language Models
Chen Ling, Kai Hu, Hangcheng Liu et al. (+3 more)
Large
Vision-
Language Models (LVLMs) are increasingly deployed in real-world intelligent systems for perception and
reasoning in open physical environments. While LVLMs are known to be vulnerable to
prompt injection attacks, existing methods either require access to input channels or depend on knowledge of user queries, assumptions that rarely hold in practical deployments. We propose the first Physical
Prompt Injection Attack (PPIA), a black-box, query-agnostic attack that embeds malicious typographic instructions into physical objects perceivable by the LVLM. PPIA requires no access to the model, its inputs, or internal pipeline, and operates solely through visual observation. It combines offline selection of highly recognizable and semantically effective visual
prompts with strategic environment-aware placement guided by spatiotemporal
attention, ensuring that the injected
prompts are both perceivable and influential on model behavior. We evaluate PPIA across 10 state-of-the-art LVLMs in both simulated and real-world settings on tasks including visual question answering, planning, and
navigation, PPIA achieves attack success rates up to 98%, with strong robustness under varying physical conditions such as distance, viewpoint, and illumination. Our code is publicly available at https://github.com/2023cghacker/Physical-
Prompt-Injection-Attack.
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All authors Chen Ling, Kai Hu, Hangcheng Liu, Xingshuo Han, Tianwei Zhang, Changhai Ou
Computer Vision
UCAD: Uncertainty-guided Contour-aware Displacement for semi-supervised medical image segmentation
Chengbo Ding, Fenghe Tang and Shaohua Kevin Zhou
Existing displacement strategies in semi-supervised
segmentation only operate on rectangular regions, ignoring anatomical structures and resulting in boundary distortions and semantic inconsistency. To address these issues, we propose UCAD, an Uncertainty-Guided Contour-Aware Displacement framework for semi-supervised medical image
segmentation that preserves contour-aware semantics while enhancing consistency learning. Our UCAD leverages superpixels to generate anatomically coherent regions aligned with anatomy boundaries, and an uncertainty-guided selection mechanism to selectively displace challenging regions for better consistency learning. We further propose a dynamic uncertainty-weighted consistency loss, which adaptively stabilizes training and effectively regularizes the model on unlabeled regions. Extensive experiments demonstrate that UCAD consistently outperforms state-of-the-art semi-supervised
segmentation methods, achieving superior
segmentation accuracy under limited annotation. The code is available at:https://github.com/dcb937/UCAD.
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Computer Vision
PocketGS: High-Fidelity On-Device Training for 3D Gaussian Splatting
Wenzhi Guo, Guangchi Fang, Shu Yang et al. (+1 more)
Although 3D Gaussian Splatting (3DGS) supports real-time rendering, training relies on offline geometry, long-horizon densification, and
memory-intensive differentiation. We cast post-capture static-scene training as resource-constrained finite-horizon optimization, where terminal quality depends jointly on the initial representation and reference-consistent gradient execution. To the best of our knowledge, PocketGS is the first publicly documented pipeline to complete geometry construction, Gaussian initialization, and 3DGS optimization on a commodity smartphone. Its I, G warm start combines refined poses, a filtered dense prior, and surface-aligned anisotropic Gaussians. Its T operator replays compositing interactions and scatters gradients to canonical parameters, matching a numerical reference at the reported precision. Controlled 500-step experiments on LLFF, Mip-NeRF 360, and MobileScan show high-quality reconstruction at the device horizon. Across MobileScan, every scene remains below 3 GB peak
memory and post-capture processing averages 255.2 s, enabling an on-device capture-to-render workflow. Separate 30k experiments characterize native-schedule behavior.
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All authors Wenzhi Guo, Guangchi Fang, Shu Yang, Bing Wang
Computer Vision
HyDeMiC: A Deep Learning-based Mineral Classifier using Hyperspectral Data
M. L. Mamud, Piyoosh Jaysaval, Frederick D Day-Lewis et al. (+1 more)
Hyperspectral imaging (HSI) has emerged as a powerful remote sensing tool for mineral exploration, capitalizing on unique spectral signatures of minerals. However, traditional classification methods such as discriminant analysis, logistic regression, and support vector machines often struggle with environmental noise in data, sensor limitations, and the computational complexity of analyzing high-dimensional HSI data. This study presents HyDeMiC (Hyperspectral Deep Learning-based Mineral Classifier), a convolutional neural network (CNN) model designed for robust mineral classification under noisy data. To train HyDeMiC, laboratory-measured hyperspectral data for 115 minerals spanning various mineral groups were used from the United States Geological Survey (USGS) library. The training
dataset was generated by convolving reference mineral spectra with an HSI sensor response function. These
datasets contained three copper-bearing minerals, Cuprite, Malachite, and Chalcopyrite, used as case studies for performance demonstration. The trained CNN model was evaluated on several synthetic 2D hyperspectral
datasets with noise levels of 1%, 2%, 5%, and 10%. Our noisy data analysis aims to replicate realistic field conditions. The HyDeMiC's performance was assessed using the Matthews Correlation Coefficient (MCC), providing a comprehensive measure across different noise regimes. Results demonstrate that HyDeMiC achieved near-perfect classification accuracy (MCC = 1.00) on clean and low-noise
datasets and maintained strong performance under moderate noise conditions. These findings emphasize HyDeMiC's robustness in the presence of moderate noise, highlighting its potential for real-world applications in hyperspectral imaging, where noise is often a significant challenge.
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All authors M. L. Mamud, Piyoosh Jaysaval, Frederick D Day-Lewis, M. K. Mudunuru
Computer Vision
NeRF-MIR: Towards High-Quality Restoration of Masked Images with Neural Radiance Fields
Xianliang Huang, Zhizhou Zhong, Shuhang Chen et al. (+3 more)
Neural Radiance Fields (NeRF) have demonstrated remarkable performance in novel view synthesis. However, there is much improvement room on restoring 3D scenes based on NeRF from corrupted images, which are common in natural scene captures and can significantly impact the effectiveness of NeRF. This paper introduces NeRF-MIR, a novel neural rendering approach specifically proposed for the restoration of masked images, demonstrating the potential of NeRF in this domain. Recognizing that randomly emitting rays to pixels in NeRF may not effectively learn intricate image textures, we propose a Patch-based Entropy for Ray Emitting (PERE) strategy to distribute emitted rays properly. This enables NeRF-MIR to fuse comprehensive information from images of different views. Additionally, we introduce a Progressively Iterative REstoration (PIRE) mechanism to restore the masked regions in a self-training process. Furthermore, we design a dynamically-weighted loss function that automatically recalibrates the loss weights for masked regions. As existing
datasets do not support NeRF-based masked image restoration, we construct three masked
datasets to simulate corrupted scenarios. Extensive experiments on real data and constructed
datasets demonstrate the superiority of NeRF-MIR over its counterparts in masked image restoration.
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All authors Xianliang Huang, Zhizhou Zhong, Shuhang Chen, Yi Xu, Juhong Guan, Shuigeng Zhou
Computer Vision
Revisiting Lightweight Low-Light Image Enhancement: From a YUV Color Space Perspective
Hailong Yan, Shice Liu, Xiangtao Zhang et al. (+4 more)
In the current era of mobile internet, Lightweight Low-Light Image Enhancement (L3IE) is critical for mobile devices, which faces a persistent trade-off between visual quality and model compactness. While recent methods employ disentangling strategies to simplify lightweight architectural design, such as Retinex theory and YUV color space transformations, their performance is fundamentally limited by overlooking channel-specific degradation patterns and cross-channel interactions. To address this gap, we perform a frequency-domain analysis that confirms the superiority of the YUV color space for L3IE. We identify a key insight: the Y channel primarily loses low-frequency content, while the UV channels are corrupted by high-frequency noise. Leveraging this finding, we propose a novel YUV-based paradigm that strategically restores channels using a Dual-Stream Global-Local
Attention module for the Y channel, a Y-guided Local-Aware Frequency
Attention module for the UV channels, and a Guided Interaction module for final feature fusion. Extensive experiments validate that our model establishes a new state-of-the-art on multiple
benchmarks, delivering superior visual quality with a significantly lower parameter count.
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All authors Hailong Yan, Shice Liu, Xiangtao Zhang, Lujian Yao, Fengxiang Yang, Jinwei Chen, Bo Li
Computer Vision
STARS: Shared-specific Translation and Alignment for missing-modality Remote Sensing Semantic Segmentation
Tong Wang, Xiaodong Zhang, Guanzhou Chen et al. (+7 more)
Multimodal remote sensing technology significantly enhances the understanding of surface semantics by integrating heterogeneous data such as optical images, Synthetic Aperture Radar (SAR), and Digital Surface Models (DSM). However, in practical applications, the missing of modality data (e.g., optical or DSM) is a common and severe challenge, which leads to performance decline in traditional
multimodal fusion models. Existing methods for addressing missing modalities still face limitations, including feature collapse and overly generalized recovered features. To address these issues, we propose
STARS (Shared-specific Translation and
Alignment for missing-modality Remote Sensing), a robust semantic
segmentation framework for incomplete
multimodal inputs.
STARS is built on two key designs. First, we introduce an asymmetric
alignment mechanism with bidirectional translation and stop-gradient, which effectively prevents feature collapse and reduces sensitivity to hyperparameters. Second, we propose a Pixel-level Semantic sampling
Alignment (PSA) strategy that combines class-balanced pixel sampling with cross-modality semantic
alignment loss, to mitigate
alignment failures caused by severe class imbalance and improve minority-class recognition.
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All authors Tong Wang, Xiaodong Zhang, Guanzhou Chen, Jiaqi Wang, Chenxi Liu, Xiaoliang Tan, Wenchao Guo, Xuyang Li, Xuanrui Wang, Zifan Wang
Computer Vision
TEXTS-Diff: TEXTS-Aware Diffusion Model for Real-World Text Image Super-Resolution
Haodong He, Xin Zhan, Yancheng Bai et al. (+3 more)
Real-world text image super-resolution aims to restore overall visual quality and text legibility in images suffering from diverse degradations and text distortions. However, the scarcity of text image data in existing
datasets results in poor performance on text regions. In addition,
datasets consisting of isolated text samples limit the quality of background reconstruction. To address these limitations, we construct Real-Texts, a large-scale, high-quality
dataset collected from real-world images, which covers diverse scenarios and contains natural text instances in both Chinese and English. Additionally, we propose the TEXTS-Aware
Diffusion Model (TEXTS-Diff) to achieve high-quality generation in both background and textual regions. This approach leverages abstract concepts to improve the understanding of textual elements within visual scenes and concrete text regions to enhance textual details. It mitigates distortions and
hallucination artifacts commonly observed in text regions, while preserving high-quality visual scene fidelity. Extensive experiments demonstrate that our method achieves state-of-the-art performance across multiple evaluation metrics, exhibiting superior generalization ability and text restoration accuracy in complex scenarios. All the code, model, and
dataset will be released.
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All authors Haodong He, Xin Zhan, Yancheng Bai, Rui Lan, Lei Sun, Xiangxiang Chu
Computer Vision
AGE-Net: Spectral–Spatial Fusion and Anatomical Graph Reasoning with Evidential Ordinal Regression for Knee Osteoarthritis Grading
Xiaoyang Li, Runni Zhou, Xinghao Yan et al. (+5 more)
Automated Kellgren–Lawrence (KL) grading from knee radiographs is challenging due to subtle structural changes, long-range anatomical dependencies, and ambiguity near grade boundaries. We propose AGE-Net, a ConvNeXt-based framework that integrates Spectral–Spatial Fusion (SSF), Anatomical Graph
Reasoning (AGR), and Differential Refinement (DFR). To capture predictive uncertainty and preserve label ordinality, AGE-Net employs a Normal-Inverse-Gamma (NIG) evidential regression head and a pairwise ordinal ranking constraint. On a knee KL
dataset, AGE-Net achieves a quadratic weighted kappa (QWK) of 0.9017 +/- 0.0045 and a mean squared error (MSE) of 0.2349 +/- 0.0028 over three random seeds, outperforming strong CNN baselines and showing consistent gains in ablation studies. We further outline evaluations of uncertainty quality, robustness, and explainability, with additional experimental figures to be included in the full manuscript.
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All authors Xiaoyang Li, Runni Zhou, Xinghao Yan, Liehao Yan, Zhaochen Li, Chenjie Zhu, Rongrong Fu, Yuan Chai
Computer Vision
Learning with Geometric Priors in U-Net Variants for Polyp Segmentation
Fabian Vazquez, Jose A. Nuñez, Diego Adame et al. (+7 more)
Accurate and robust polyp
segmentation is essential for early colorectal cancer detection and for computer-aided diagnosis. While convolutional neural network-,
Transformer-, and Mamba-based U-Net variants have achieved strong performance, they still struggle to capture
geometric and structural cues, especially in low-contrast or cluttered colonoscopy scenes. To address this challenge, we propose a novel
Geometric Prior-guided Module (GPM) that injects explicit
geometric priors into U-Net-based architectures for polyp
segmentation. Specifically, we fine-tune the Visual Geometry Grounded
Transformer (VGGT) on a simulated ColonDepth
dataset to estimate depth maps of polyp images tailored to the endoscopic domain. These depth maps are then processed by GPM to encode
geometric priors into the encoder's feature maps, where they are further refined using spatial and channel
attention mechanisms that emphasize both local spatial and global channel information. GPM is plug-and-play and can be seamlessly integrated into diverse U-Net variants. Extensive experiments on five public polyp
segmentation datasets demonstrate consistent gains over three strong baselines. Code and the generated depth maps are available at: https://github.com/fvazqu/GPM-PolypSeg
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All authors Fabian Vazquez, Jose A. Nuñez, Diego Adame, Alissen Moreno, Augustin Zhan, Huimin Li, Jinghao Yang, Haoteng Tang, Bin Fu, Pengfei Gu
Computer Vision · Cross-listed from cs.LG
Thermodynamically Optimal Regularization under Information-Geometric Constraints
Laurent Caraffa
Modern machine learning relies on a collection of empirically successful but theoretically heterogeneous regularization techniques, such as weight decay, dropout, and exponential moving averages. At the same time, the rapidly increasing energetic cost of training large models raises the question of whether learning
algorithms approach any fundamental efficiency bound. In this work, we propose a unifying theoretical framework connecting thermodynamic optimality, information geometry, and regularization. Under three explicit assumptions – (A1) that optimality requires an intrinsic, parametrization-invariant measure of information, (A2) that belief states are modeled by maximum-
entropy distributions under known constraints, and (A3) that optimal processes are quasi-static – we prove a conditional optimality
theorem. Specifically, the Fisher–Rao metric is the unique admissible geometry on belief space, and thermodynamically optimal regularization corresponds to minimizing squared Fisher–Rao distance to a reference state. We derive the induced geometries for Gaussian and circular belief models, yielding
hyperbolic and von Mises
manifolds, respectively, and show that classical regularization schemes are structurally incapable of guaranteeing thermodynamic optimality. We introduce a notion of thermodynamic efficiency of learning and propose experimentally testable predictions. This work provides a principled
geometric and thermodynamic foundation for regularization in machine learning.
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Computer Vision
SymbolSight: Minimizing Inter-Symbol Interference for Reading with Prosthetic Vision
Jasmine Lesner and Michael Beyeler
Retinal prostheses restore limited visual perception, but low spatial resolution and temporal persistence make reading difficult. In sequential letter presentation, the afterimage of one symbol can interfere with perception of the next, leading to systematic recognition errors. Rather than relying on future hardware improvements, we investigate whether optimizing the visual symbols themselves can mitigate this temporal interference. We present SymbolSight, a computational framework that selects symbol-to-letter mappings to minimize confusion among frequently adjacent letters. Using simulated prosthetic
vision (SPV) and a neural proxy observer, we estimate pairwise symbol confusability and optimize assignments using
language-specific bigram statistics. Across simulations in Arabic, Bulgarian, and English, the resulting heterogeneous symbol sets reduced predicted confusion by a median factor of 22 relative to native alphabets. These results suggest that standard typography is poorly matched to serial, low-bandwidth prosthetic
vision and demonstrate how computational modeling can narrow the design space of visual encodings, identifying high-potential candidates for future psychophysical and clinical evaluation rather than predicting present-day clinical reading performance directly.
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Computer Vision
SkyReels-V3 Technique Report
Debang Li, Zhengcong Fei, Tuanhui Li et al. (+19 more)
Video generation serves as a cornerstone for building world models, where
multimodal contextual inference stands as the defining test of capability. In this end, we present SkyReels-V3, a conditional
video generation model, built upon a unified
multimodal in-context learning framework with
diffusion Transformers. SkyReels-V3 model supports three core generative paradigms within a single architecture: reference images-to-
video synthesis,
video-to-
video extension and audio-guided
video generation. (i) reference images-to-
video model is designed to produce high-fidelity
videos with strong subject identity preservation, temporal coherence, and narrative consistency. To enhance reference adherence and compositional stability, we design a comprehensive data processing pipeline that leverages cross frame pairing, image editing, and semantic rewriting, effectively mitigating copy paste artifacts. During training, an image
video hybrid strategy combined with multi-resolution joint optimization is employed to improve generalization and robustness across diverse scenarios. (ii)
video extension model integrates spatio-temporal consistency modeling with large-scale
video understanding, enabling both seamless single-shot continuation and intelligent multi-shot switching with professional cinematographic patterns. (iii) Talking avatar model supports minute-level audio-conditioned
video generation by training first-and-last frame insertion patterns and reconstructing key-frame inference paradigms. On the basis of ensuring visual quality, synchronization of audio and
videos has been optimized. Extensive evaluations demonstrate that SkyReels-V3 achieves state-of-the-art or near state-of-the-art performance on key metrics including visual quality, instruction following, and specific aspect metrics, approaching leading closed-source systems. Github: https://github.com/SkyworkAI/SkyReels-V3.
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All authors Debang Li, Zhengcong Fei, Tuanhui Li, Yikun Dou, Zheng Chen, Jiangping Yang, Mingyuan Fan, Jingtao Xu, Jiahua Wang, Baoxuan Gu, Mingshan Chang, Wenjing Cai, Yuqiang Xie, Binjie Mao, Youqiang Zhang, Nuo Pang, Hao Zhang, Yuzhe Jin, Zhiheng Xu, Dixuan Lin, Guibin Chen, Yahui Zhou
Computer Vision
ClinNet: Evidential Ordinal Regression with Bilateral Asymmetry and Prototype Memory for Knee Osteoarthritis Grading
Xiaoyang Li and Runni Zhou
Knee osteoarthritis (KOA) grading based on radiographic images is a critical yet challenging task due to subtle inter-grade differences, annotation uncertainty, and the inherently ordinal nature of disease progression. Conventional deep learning approaches typically formulate this problem as deterministic multi-class classification, ignoring both the continuous progression of degeneration and the uncertainty in expert annotations. In this work, we propose ClinNet, a novel trustworthy framework that addresses KOA grading as an evidential ordinal regression problem. The proposed method integrates three key components: (1) a Bilateral Asymmetry Encoder (BAE) that explicitly models medial-lateral structural discrepancies; (2) a Diagnostic
Memory Bank that maintains class-wise prototypes to stabilize feature representations; and (3) an Evidential Ordinal Head based on the Normal-Inverse-Gamma (NIG) distribution to jointly estimate continuous KL grades and epistemic uncertainty. Extensive experiments demonstrate that ClinNet achieves a Quadratic Weighted Kappa of 0.892 and Accuracy of 0.768, statistically outperforming state-of-the-art baselines (p < 0.001). Crucially, we demonstrate that the model's uncertainty estimates successfully flag out-of-distribution samples and potential misdiagnoses, paving the way for safe clinical deployment.
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Computer Vision
Dynamic Meta-Ensemble Framework for Efficient and Accurate Deep Learning in Plant Leaf Disease Detection on Resource-Constrained Edge Devices
Weloday Fikadu Moges, Jianmei Su and Amin Waqas
Deploying deep learning models for plant disease detection on edge devices such as IoT sensors, smartphones, and embedded systems is severely constrained by limited computational resources and energy budgets. To address this challenge, we introduce a novel Dynamic Meta-Ensemble Framework (DMEF) for high-accuracy plant disease diagnosis under resource constraints. DMEF employs an adaptive weighting mechanism that dynamically combines the predictions of three lightweight convolutional neural networks (MobileNetV2, NASNetMobile, and InceptionV3) by optimizing a trade-off between accuracy improvements (DeltaAcc) and computational efficiency (model size). During training, the ensemble weights are updated iteratively, favoring models exhibiting high performance and low complexity. Extensive experiments on
benchmark datasets for potato and maize diseases demonstrate state-of-the-art classification accuracies of 99.53% and 96.61%, respectively, surpassing standalone models and static ensembles by 2.1% and 6.3%. With computationally efficient inference latency (<75ms) and a compact footprint (<1 million parameters), DMEF shows strong potential for edge-based agricultural monitoring, suggesting viability for scalable crop disease management. This bridges the gap between high-accuracy AI and practical field applications.
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Computer Vision
Fluxamba: Topology-Aware Anisotropic State Space Models for Geological Lineament Segmentation in Multi-Source Remote Sensing
Jin Bai, Huiyao Zhang, Qi Wen et al. (+3 more)
The precise
segmentation of geological linear features, spanning from planetary lineaments to terrestrial fractures, demands capturing long-range dependencies across complex anisotropic
topologies. Although State Space Models (SSMs) offer near-linear computational complexity, their dependence on rigid, axis-aligned scanning trajectories induces a fundamental topological mismatch with curvilinear targets, resulting in fragmented context and feature erosion. To bridge this gap, we propose Fluxamba, a lightweight architecture that introduces a
topology-aware feature rectification framework. Central to our design is the Structural Flux Block (SFB), which orchestrates an anisotropic information flux by integrating an Anisotropic Structural Gate (ASG) with a Prior-Modulated Flow (PMF). This mechanism decouples feature orientation from spatial location, dynamically gating context aggregation along the target's intrinsic geometry rather than rigid paths. Furthermore, to mitigate serialization-induced noise in low-contrast environments, we incorporate a Hierarchical Spatial Regulator (HSR) for multi-scale semantic
alignment and a High-Fidelity Focus Unit (HFFU) to explicitly maximize the signal-to-noise ratio of faint features. Extensive experiments on diverse geological
benchmarks (LROC-Lineament, LineaMapper, and GeoCrack) demonstrate that Fluxamba establishes a new state-of-the-art. Notably, on the challenging LROC-Lineament
dataset, it achieves an F1-score of 89.22% and mIoU of 89.87%. Achieving a real-time inference speed of over 24 FPS with only 3.4M parameters and 6.3G FLOPs, Fluxamba reduces computational costs by up to two orders of magnitude compared to heavy-weight baselines, thereby establishing a new Pareto frontier between
segmentation fidelity and onboard deployment feasibility.
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All authors Jin Bai, Huiyao Zhang, Qi Wen, Shengyang Li, Xiaolin Tian, Atta ur Rahman
Computer Vision · Cross-listed from cs.AR
SPADE: A SIMD Posit-enabled compute engine for Accelerating DNN Efficiency
Sonu Kumar, Lavanya Vinnakota, Mukul Lokhande et al. (+2 more)
The growing demand for edge-AI systems requires arithmetic units that balance numerical precision, energy efficiency, and compact hardware while supporting diverse formats. Posit arithmetic offers advantages over floating- and fixed-point representations through its tapered precision, wide dynamic range, and improved numerical robustness. This work presents SPADE, a unified multi-precision SIMD Posit-based multiplyaccumulate (MAC) architecture supporting Posit (8,0), Posit (16,1), and Posit (32,2) within a single framework. Unlike prior single-precision or floating/fixed-point SIMD MACs, SPADE introduces a regime-aware, lane-fused SIMD Posit datapath that hierarchically reuses Posit-specific submodules (LOD, complementor, shifter, and multiplier) across 8/16/32-bit precisions without datapath replication. FPGA implementation on a Xilinx Virtex-7 shows 45.13% LUT and 80% slice reduction for Posit (8,0), and up to 28.44% and 17.47% improvement for Posit (16,1) and Posit (32,2) over prior work, with only 6.9% LUT and 14.9% register overhead for multi-precision support. ASIC results across TSMC nodes achieve 1.38 GHz at 6.1 mW (28 nm). Evaluation on MNIST, CIFAR-10/100, and alphabet
datasets confirms competitive inference accuracy.
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All authors Sonu Kumar, Lavanya Vinnakota, Mukul Lokhande, Santosh Kumar Vishvakarma, Adam Teman
Computer Vision
Cross360: 360{\deg} Monocular Depth Estimation via Cross Projections Across Scales
Kun Huang, Fang-Lue Zhang and Neil Dodgson
360{\deg} depth estimation is a challenging research problem due to the difficulty of finding a representation that both preserves global continuity and avoids distortion in spherical images. Existing methods attempt to leverage complementary information from multiple projections, but struggle with balancing global and local consistency. Their local patch features have limited global perception, and the combined global representation does not address discrepancies in feature extraction at the boundaries between patches. To address these issues, we propose Cross360, a novel cross-
attention-based architecture integrating local and global information using less-distorted tangent patches along with equirectangular features. Our Cross Projection Feature
Alignment module employs cross-
attention to align local tangent projection features with the equirectangular projection's 360{\deg} field of view, ensuring each tangent projection patch is aware of the global context. Additionally, our Progressive Feature Aggregation with
Attention module refines multi-scaled features progressively, enhancing depth estimation accuracy. Cross360 significantly outperforms existing methods across most
benchmark datasets, especially those in which the entire 360{\deg} image is available, demonstrating its effectiveness in accurate and globally consistent depth estimation. The code and model are available at https://github.com/huangkun101230/Cross360.
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Computer Vision
Inference-Time Loss-Guided Colour Preservation in Diffusion Sampling
Angad Singh Ahuja and Aarush Ram Anandh
Precise color control remains a persistent failure mode in text-to-image
diffusion systems, particularly in design-oriented workflows where outputs must satisfy explicit, user-specified color targets. We present an inference-time, region-constrained color preservation method that steers a pretrained
diffusion model without any additional training. Our approach combines (i) ROI-based inpainting for spatial selectivity, (ii) background-latent re-imposition to prevent color drift outside the ROI, and (iii) latent nudging via gradient guidance using a composite loss defined in CIE Lab and linear RGB. The loss is constructed to control not only the mean ROI color but also the tail of the pixelwise error distribution through CVaR-style and soft-maximum penalties, with a late-start gate and a time-dependent schedule to stabilize guidance across denoising steps. We show that mean-only baselines can satisfy average color constraints while producing perceptually salient local failures, motivating our distribution-aware objective. The resulting method provides a practical, training-free mechanism for targeted color adherence that can be integrated into standard Stable
Diffusion inpainting pipelines.
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Computer Vision
FineVAU: A Novel Human-Aligned Benchmark for Fine-Grained Video Anomaly Understanding
João Pereira, Vasco Lopes, João Neves et al. (+1 more)
Video Anomaly Understanding (VAU) is a novel task focused on describing unusual occurrences in
videos. Despite growing interest, the evaluation of VAU remains an open challenge. Existing
benchmarks rely on n-gram-based metrics (e.g., BLEU, ROUGE-L) or LLM-based evaluation. The first fails to capture the rich, free-form, and visually grounded nature of LVLM responses, while the latter focuses on assessing
language quality over factual relevance, often resulting in subjective judgments that are misaligned with human perception. In this work, we address this issue by proposing FineVAU, a new
benchmark for VAU that shifts the focus towards rich, fine-grained and domain-specific understanding of anomalous
videos. We formulate VAU as a three-fold problem, with the goal of comprehensively understanding key descriptive elements of anomalies in
video: events (What), participating entities (Who) and location (Where). Our
benchmark introduces a) FVScore, a novel, human-aligned evaluation metric that assesses the presence of critical visual elements in LVLM answers, providing interpretable, fine-grained feedback; and b) FineW3, a novel, comprehensive
dataset curated through a structured and fully automatic procedure that augments existing human annotations with high quality, fine-grained visual information. Human evaluation reveals that our proposed metric has a superior
alignment with human perception of anomalies in comparison to current approaches. Detailed experiments on FineVAU unveil critical limitations in LVLM's ability to perceive anomalous events that require spatial and fine-grained temporal understanding, despite strong performance on coarse grain, static information, and events with strong visual cues.
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All authors João Pereira, Vasco Lopes, João Neves, David Semedo
Computer Vision
Multi-stage Bridge Inspection System: Integrating Foundation Models with Location Anonymization
Takato Yasuno
In Japan, civil infrastructure condition monitoring is mandated through visual inspection every five years. Field-captured damage images frequently contain concrete cracks and rebar exposure, often accompanied by construction signs revealing regional information. To enable safe infrastructure use without causing public anxiety, it is essential to protect regional information while accurately extracting damage features and visualizing key indicators for repair decision-making. This paper presents an open-source bridge damage detection system with regional
privacy protection capabilities. We employ Segment Anything Model (SAM) 3 for rebar corrosion detection and utilize DBSCAN for automatic completion of missed regions. Construction sign regions are detected and protected through Gaussian blur. Four preprocessing methods improve OCR accuracy, and GPU optimization enables 1.7-second processing per image. The technology stack includes SAM3, PyTorch, OpenCV, pytesseract, and scikit-learn, achieving efficient bridge inspection with regional information protection.
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