Computer Vision
SWNet: A Cross-Spectral Network for Camouflaged Weed Detection
Henry O. Velesaca, Luigi Miranda and Angel D. Sappa
This paper presents SWNet, a bimodal end-to-end cross-spectral network specifically engineered for the detection of camouflaged weeds in dense agricultural environments. Plant camouflage, characterized by homochromatic blending where invasive species mimic the phenotypic traits of primary crops, poses a significant challenge for traditional computer
vision systems. To overcome these limitations, SWNet utilizes a Pyramid
Vision Transformer v2 backbone to capture long-range dependencies and a Bimodal Gated Fusion Module to dynamically integrate Visible and Near-Infrared information. By leveraging the physiological differences in chlorophyll reflectance captured in the NIR spectrum, the proposed architecture effectively discriminates targets that are otherwise indistinguishable in the visible range. Furthermore, an Edge-Aware Refinement module is employed to produce sharper object boundaries and reduce structural ambiguity. Experimental results on the Weeds-Banana
dataset indicate that SWNet outperforms ten state-of-the-art methods. The study demonstrates that the integration of cross-spectral data and boundary-guided refinement is essential for high
segmentation accuracy in complex crop canopies. The code is available on GitHub: https://cod-espol.github.io/SWNet/
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Computer Vision
Motion-Adapter: A Diffusion Model Adapter for Text-to-Motion Generation of Compound Actions
Yue Jiang, Mingyu Yang, Liuyuxin Yang et al. (+3 more)
Recent advances in generative motion synthesis have enabled the production of realistic human motions from diverse input modalities. However, synthesizing compound actions from texts, which integrate multiple concurrent actions into coherent full-body sequences, remains a major challenge. We identify two key limitations in current text-to-motion
diffusion models: (i) catastrophic neglect, where earlier actions are overwritten by later ones due to improper handling of temporal information, and (ii)
attention collapse, which arises from excessive feature fusion in cross-
attention mechanisms. As a result, existing approaches often depend on overly detailed textual descriptions (e.g., raising right hand), explicit body-part specifications (e.g., editing the upper body), or the use of large
language models (LLMs) for body-part interpretation. These strategies lead to deficient semantic representations of physical structures and kinematic mechanisms, limiting the ability to incorporate natural behaviors such as greeting while walking. To address these issues, we propose the Motion-Adapter, a plug-and-play module that guides text-to-motion
diffusion models in generating compound actions by computing decoupled cross-
attention maps, which serve as structural masks during the denoising process. Extensive experiments demonstrate that our method consistently produces more faithful and coherent compound motions across diverse textual
prompts, surpassing state-of-the-art approaches.
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All authors Yue Jiang, Mingyu Yang, Liuyuxin Yang, Yang Xu, Bingxin Yun, Yuhe Zhang
Computer Vision
From Articles to Canopies: Knowledge-Driven Pseudo-Labelling for Tree Species Classification using LLM Experts
Michał Romaszewski, Dominik Kopeć, Michał Cholewa et al. (+6 more)
Hyperspectral tree species classification is challenging due to limited and imbalanced class labels, spectral mixing (overlapping light signatures from multiple species), and ecological heterogeneity (variability among ecological systems). Addressing these challenges requires methods that integrate biological and structural characteristics of vegetation, such as canopy architecture and interspecific interactions, rather than relying solely on spectral signatures. This paper presents a biologically informed, semi-supervised deep learning method that integrates multi-sensor Earth observation data, specifically hyperspectral imaging (HSI) and airborne
laser scanning (ALS), with expert, ecological knowledge. The approach relies on biologically inspired pseudo-labelling over a precomputed canopy graph, yielding improved classification with a lightweight classifier. In addition, ecological priors are constructed using an LLM-assisted, citation-constrained workflow and encoded as a cohabitation
matrix with likelihoods of species occurring together. These priors are incorporated into the pseudo-labelling strategy, effectively introducing expert knowledge into the model. Experiments on two real-world forest
datasets showed macro-F1 improvements of 6.01 and 4.85 percentage points over the strongest classical HSI–ALS reference pipelines.
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All authors Michał Romaszewski, Dominik Kopeć, Michał Cholewa, Katarzyna Kołodziej, Przemysław Głomb, Jan Niedzielko, Jakub Charyton, Justyna Wylazłowska, Anna Jarocińska
Computer Vision
Towards In-Context Tone Style Transfer with A Large-Scale Triplet Dataset
Yuhai Deng, Huimin She, Wei Shen et al. (+4 more)
Tone style transfer for photo retouching aims to adapt the stylistic tone of the reference image to a given content image. However, the lack of high-quality large-scale triplet
datasets with stylized ground truth forces existing methods to rely on self-supervised or proxy objectives, which limits model capability. To mitigate this gap, we design a data construction pipeline to build TST100K, a large-scale
dataset of 100,000 content-reference-stylized triplets. At the core of this pipeline, we train a tone style scorer to ensure strict stylistic consistency for each triplet. In addition, existing methods typically extract content and reference features independently and then fuse them in a decoder, which may cause semantic loss and lead to inappropriate color transfer and degraded visual aesthetics. Instead, we propose ICTone, a
diffusion-based framework that performs tone transfer in an in-context manner by jointly conditioning on both images, leveraging the semantic priors of generative models for semantic-aware transfer. Reward feedback learning using the tone style scorer is further incorporated to improve stylistic fidelity and visual quality. Experiments demonstrate the effectiveness of TST100K, and ICTone achieves state-of-the-art performance on both quantitative metrics and human evaluations. The project page is available online: https://dengyuhai.github.io/ICTone_Project/.
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All authors Yuhai Deng, Huimin She, Wei Shen, Meng Li, Ruoxi Wu, Lunxi Yuan, Xiang Li
Computer Vision
Polyglot: Multilingual Style Preserving Speech-Driven Facial Animation
Federico Nocentini, Kwanggyoon Seo, Qingju Liu et al. (+6 more)
Speech-Driven Facial Animation (SDFA) has gained significant
attention due to its applications in movies,
video games, and virtual reality. However, most existing models are trained on single-
language data, limiting their effectiveness in real-world
multilingual scenarios. In this work, we address
multilingual SDFA, which is essential for realistic generation since
language influences phonetics, rhythm, intonation, and facial expressions. Speaking style is also shaped by individual differences, not only by
language. Existing methods typically rely on either
language-specific or speaker-specific conditioning, but not both, limiting their ability to model their interaction. We introduce Polyglot, a unified
diffusion-based architecture for personalized
multilingual SDFA. Our method uses transcript
embeddings to encode
language information and style
embeddings extracted from reference facial sequences to capture individual speaking characteristics. Polyglot does not require predefined
language or speaker labels, enabling generalization across
languages and speakers through self-supervised learning. By jointly conditioning on
language and style, it captures expressive traits such as rhythm, articulation, and habitual facial movements, producing temporally coherent and realistic animations. Experiments show improved performance in both monolingual and
multilingual settings, providing a unified framework for modeling
language and personal style in SDFA.
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All authors Federico Nocentini, Kwanggyoon Seo, Qingju Liu, Claudio Ferrari, Stefano Berretti, David Ferman, Hyeongwoo Kim, Pablo Garrido, Akin Caliskan
Computer Vision · Cross-listed from eess.IV
Dual-Modal Lung Cancer AI: Interpretable Radiology and Microscopy with Clinical Risk Integration
Baramee Sukumal and Aueaphum Aueawatthanaphisut
Lung cancer remains one of the leading causes of cancer-related mortality worldwide. Conventional computed
tomography (CT) imaging, while essential for detection and staging, has limitations in distinguishing benign from malignant lesions and providing interpretable diagnostic insights. To address this challenge, this study proposes a dual-modal artificial intelligence framework that integrates CT radiology with hematoxylin and eosin (H&E) histopathology for lung cancer diagnosis and subtype classification. The system employs convolutional neural networks to extract radiologic and histopathologic features and incorporates clinical metadata to improve robustness. Predictions from both modalities are fused using a weighted decision-level integration mechanism to classify adenocarcinoma, squamous
cell carcinoma, large
cell carcinoma, small
cell lung cancer, and normal tissue. Explainable AI techniques including Grad-CAM, Grad-CAM++, Integrated Gradients, Occlusion, Saliency Maps, and SmoothGrad are applied to provide visual interpretability. Experimental results show strong performance with accuracy up to 0.87, AUROC above 0.97, and macro F1-score of 0.88. Grad-CAM++ achieved the highest faithfulness and localization accuracy, demonstrating strong correspondence with expert-annotated tumor regions. These results indicate that
multimodal fusion of radiology and histopathology can improve diagnostic performance while maintaining model transparency, suggesting potential for future clinical decision support systems in precision oncology.
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Computer Vision
DenTab: A Dataset for Table Recognition and Visual QA on Real-World Dental Estimates
Laziz Hamdi, Amine Tamasna and Thierry Paquet
Tables condense key transactional and administrative information into compact layouts, but practical extraction requires more than text recognition: systems must also recover structure (rows, columns, merged
cells, headers) and interpret roles such as line items, subtotals, and totals under common capture artifacts. Many existing resources for table structure recognition and TableVQA are built from clean digital-born sources or rendered tables, and therefore only partially reflect noisy administrative conditions. We introduce DenTab, a
dataset of 2{,}000 cropped table images from dental estimates with high-quality HTML annotations, enabling evaluation of table recognition (TR) and table visual question answering (TableVQA) on the same inputs. DenTab includes 2{,}208 questions across eleven categories spanning
retrieval, aggregation, and logic/consistency checks. We
benchmark 16 systems, including 14
vision–
language models (VLMs) and two OCR baselines. Across models, strong structure recovery does not consistently translate into reliable performance on multi-step arithmetic and consistency questions, and these
reasoning failures persist even when using ground-truth HTML table inputs. To improve arithmetic reliability without training, we propose the Table Router Pipeline, which routes arithmetic questions to deterministic execution. The pipeline combines (i) a VLM that produces a baseline answer, a structured table representation, and a constrained table program with (ii) a rule-based executor that performs exact computation over the parsed table. The source code and
dataset will be made publicly available at https://github.com/hamdilaziz/DenTab.
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Computer Vision
Stylistic-STORM (ST-STORM) : Perceiving the Semantic Nature of Appearance
Hamed Ouattara, Pierre Duthon, Pascal Houssam Salmane et al. (+2 more)
One of the dominant paradigms in self-supervised learning (SSL), illustrated by MoCo or DINO, aims to produce robust representations by capturing features that are insensitive to certain image transformations such as illumination, or
geometric changes. This strategy is appropriate when the objective is to recognize objects independently of their appearance. However, it becomes counterproductive as soon as appearance itself constitutes the discriminative signal. In weather analysis, for example, rain streaks, snow granularity, atmospheric scattering, as well as reflections and
halos, are not noise: they carry the essential information. In critical applications such as autonomous driving, ignoring these cues is risky, since grip and visibility depend directly on ground conditions and atmospheric conditions. We introduce ST-STORM, a hybrid SSL framework that treats appearance (style) as a semantic modality to be disentangled from content. Our architecture explicitly separates two latent streams, regulated by gating mechanisms. The Content branch aims at a stable semantic representation through a JEPA scheme coupled with a contrastive objective, promoting invariance to appearance variations. In parallel, the Style branch is constrained to capture appearance signatures (textures, contrasts, scattering) through feature prediction and reconstruction under an
adversarial constraint. We evaluate ST-STORM on several tasks, including object classification (ImageNet-1K), fine-grained weather characterization, and melanoma detection (ISIC 2024 Challenge). The results show that the Style branch effectively isolates complex appearance phenomena (F1=97% on Multi-Weather and F1=94% on ISIC 2024 with 10% labeled data), without degrading the semantic performance (F1=80% on ImageNet-1K) of the Content branch, and improves the preservation of critical appearance
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All authors Hamed Ouattara, Pierre Duthon, Pascal Houssam Salmane, Frédéric Bernardin, Omar Ait Aider
Computer Vision
DINOv3 Beats Specialized Detectors: A Simple Foundation Model Baseline for Image Forensics
Jieming Yu, Qiuxiao Feng, Zhuohan Wang et al. (+1 more)
With the rapid advancement of deep generative models, realistic fake images have become increasingly accessible, yet existing localization methods rely on complex designs and still struggle to generalize across manipulation types and imaging conditions. We present a simple but strong baseline based on DINOv3 with LoRA adaptation and a lightweight convolutional decoder. Under the CAT-Net protocol, our best model improves average pixel-level F1 by 17.0 points over the previous state of the art on four standard
benchmarks using only 9.1\,M trainable parameters on top of a frozen ViT-L backbone, and even our smallest variant surpasses all prior specialized methods. LoRA consistently outperforms full fine-tuning across all backbone scales. Under the data-scarce MVSS-Net protocol, LoRA reaches an average F1 of 0.774 versus 0.530 for the strongest prior method, while full fine-tuning becomes highly unstable, suggesting that pre-trained representations encode forensic information that is better preserved than overwritten. The baseline also exhibits strong robustness to Gaussian noise, JPEG re-compression, and Gaussian blur. We hope this work can serve as a reliable baseline for the research community and a practical starting point for future image-forensic applications. Code is available at https://github.com/Irennnne/DINOv3-IML.
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All authors Jieming Yu, Qiuxiao Feng, Zhuohan Wang, Xiaochen Ma
Computer Vision
Early Detection of Acute Myeloid Leukemia (AML) Using YOLOv12 Deep Learning Model
Enas E. Ahmed, Salah A. Aly and Mayar Moner
Acute Myeloid Leukemia (AML) is one of the most life-threatening type of blood cancers, and its accurate classification is considered and remains a challenging task due to the visual similarity between various
cell types. This study addresses the classification of the multiclasses of AML
cells Utilizing YOLOv12 deep learning model. We applied two
segmentation approaches based on
cell and
nucleus features, using Hue channel and Otsu thresholding techniques to preprocess the images prior to classification. Our experiments demonstrate that YOLOv12 with Otsu thresholding on
cell-based
segmentation achieved the highest level of validation and test accuracy, both reaching 99.3%.
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Computer Vision
The Amazing Stability of Flow Matching
Rania Briq, Michael Kamp, Ohad Fried et al. (+2 more)
The success of deep generative models in generating high-quality and diverse samples is often attributed to particular architectures and large training
datasets. In this paper, we investigate the impact of these factors on the quality and diversity of samples generated by flow-matching models. Surprisingly, in our experiments on CelebA-HQ
dataset, flow matching remains stable even when
pruning 50% of the
dataset. That is, the quality and diversity of generated samples are preserved. Moreover,
pruning impacts the latent representation only slightly, that is, samples generated by models trained on the full and pruned
dataset map to visually similar outputs for a given seed. We observe similar stability when changing the architecture or training configuration, such that the latent representation is maintained under these changes as well. Our results quantify just how strong this stability can be in practice, and help explain the reliability of flow-matching models under various perturbations.
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All authors Rania Briq, Michael Kamp, Ohad Fried, Sarel Cohen, Stefan Kesselheim
Computer Vision
TableSeq: Unified Generation of Structure, Content, and Layout
Laziz Hamdi, Amine Tamasna, Pascal Boisson et al. (+1 more)
We present TableSeq, an image-only, end-to-end framework for joint table structure recognition, content recognition, and
cell localization. The model formulates these tasks as a single sequence-generation problem: one decoder produces an interleaved stream of HTML tags,
cell text, and discretized coordinate
tokens, thereby aligning logical structure, textual content, and
cell geometry within a unified autoregressive sequence. This design avoids external OCR, auxiliary decoders, and complex multi-stage post-processing. TableSeq combines a lightweight high-resolution FCN-H16 encoder with a minimal structure-prior head and a single-layer
transformer encoder, yielding a compact architecture that remains effective on challenging layouts. Across standard
benchmarks, TableSeq achieves competitive or state-of-the-art results while preserving architectural simplicity. It reaches 95.23 TEDS / 96.83 S-TEDS on PubTabNet, 97.45 TEDS / 98.69 S-TEDS on FinTabNet, and 99.79 / 99.54 / 99.66 precision / recall / F1 on SciTSR under the CAR protocol, while remaining competitive on PubTables-1M under GriTS. Beyond TSR/TCR, the same sequence interface generalizes to index-based table querying without task-specific heads, achieving the best IRDR score and competitive ICDR/ICR performance. We also study multi-
token prediction for faster blockwise decoding and show that it reduces inference latency with only limited accuracy degradation. Overall, TableSeq provides a practical and reproducible single-stream baseline for unified table recognition, and the source code will be made publicly available at https://github.com/hamdilaziz/TableSeq.
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All authors Laziz Hamdi, Amine Tamasna, Pascal Boisson, Thierry Paquet
Computer Vision · Cross-listed from cs.LG
AEGIS: Anchor-Enforced Gradient Isolation for Knowledge-Preserving Vision-Language-Action Fine-Tuning
Guransh Singh
Fine-tuning pre-trained
Vision-
Language Models (VLMs) for robotic manipulation introduces a fundamental stability-plasticity dilemma: continuous flow-matching action experts backpropagate concentrated, low-rank regression gradients into
transformer backbones trained on high-dimensional cross-
entropy objectives. This cross-modal gradient asymmetry rapidly degrades pre-trained visual
reasoning. Existing solutions either disconnect continuous gradient flow via stop-gradients or constrain updates via LoRA, which restricts update rank but remains directionally blind to semantic corruption; both typically rely on mixed-batch VQA co-training, doubling training compute. We introduce AEGIS (Anchor-Enforced Gradient Isolation System), a buffer-free, layer-wise orthogonal gradient projection framework enabling continuous flow-matching fine-tuning while isolating pre-trained representations from destructive parameter updates. Prior to training, AEGIS estimates per-layer Gaussian activation statistics from pre-training data as a static reference anchor. During fine-tuning, a closed-form Wasserstein-2 transport penalty generates an anchor-restoration gradient through the active computation graph. A sequential dual-backward pass applies layer-wise Gram-Schmidt orthogonalization, projecting task gradients onto the orthogonal complement of the restoration vector during directional conflict. We establish an exact energy preservation bound for layer-wise orthogonal projection, showing that AEGIS sheds only 0.62% of gradient energy empirically while halting cumulative feature drift. On PaliGemma2-3B fine-tuned on the LIBERO manipulation
benchmark, AEGIS fully preserves pre-trained Visual Question Answering performance and baseline holdout loss while matching continuous action convergence, without replay buffers, teacher models, or co-training data.
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Computer Vision
Chain-of-Thought Degrades Visual Spatial Reasoning Capabilities of Multimodal LLMs
Sai Srinivas Kancheti, Aditya Sanjiv Kanade, Vineeth N. Balasubramanian et al. (+1 more)
Multimodal Reasoning Models (MRMs) leveraging Chain-of-Thought (CoT) based thinking have revolutionized mathematical and logical problem-solving. However, we show that this paradigm struggles with generalized spatial intelligence. We perform a comprehensive evaluation of seventeen models across thirteen spatial
benchmarks and identify a critical gap: CoT prompting consistently degrades performance in visual spatial
reasoning. Furthermore, through a novel No-Image++ ablation, we demonstrate that MRMs and CoT prompted MLMs suffer from severe shortcut learning, and hallucinate visual details from textual priors even when the image is absent. These findings challenge the efficacy of text-only CoT for spatial tasks and underscore the need for
vision-centric
reasoning paradigms.
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All authors Sai Srinivas Kancheti, Aditya Sanjiv Kanade, Vineeth N. Balasubramanian, Tanuja Ganu
Computer Vision
Mind's Eye: A Benchmark of Visual Abstraction, Transformation and Composition for Multimodal LLMs
Rohit Sinha, Aditya Kanade, Sai Srinivas Kancheti et al. (+2 more)
Multimodal large
language models (MLLMs) have achieved impressive progress on
vision language benchmarks, yet their capacity for visual cognitive and visuospatial
reasoning remains less understood. We introduce "Mind's Eye", a multiple-choice
benchmark of eight visuo-cognitive tasks inspired by classic human intelligence tests and organized under a novel "A-R-T" taxonomy: Abstraction, Relation, and Transformation. The tasks probe core processes of fluid intelligence such as pattern induction, analogical relation mapping, and mental transformation. We evaluate a diverse suite of closed-source and open-source MLLMs and compare their performance with human participants. Humans achieve 80% accuracy, while top performing MLLMs remain below 50%. Error analysis reveals failures in: (i) visual
attention allocation, (ii) internal perceptual manipulation, and (iii) weak abstraction of underlying visual concepts. Our findings suggest that current MLLMs exhibit limited visuospatial
reasoning capabilities, when compared with human participants, highlighting the need for more cognitively grounded evaluation frameworks.
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All authors Rohit Sinha, Aditya Kanade, Sai Srinivas Kancheti, Vineeth N Balasubramanian, Tanuja Ganu
Computer Vision
Elucidating the SNR-t Bias of Diffusion Probabilistic Models
Meng Yu, Lei Sun, Jianhao Zeng et al. (+2 more)
Diffusion Probabilistic Models have demonstrated remarkable performance across a wide range of generative tasks. However, we have observed that these models often suffer from a Signal-to-Noise Ratio-timestep (SNR-t) bias. This bias refers to the misalignment between the SNR of the denoising sample and its corresponding timestep during the inference phase. Specifically, during training, the SNR of a sample is strictly coupled with its timestep. However, this correspondence is disrupted during inference, leading to error accumulation and impairing the generation quality. We provide comprehensive empirical evidence and theoretical analysis to substantiate this phenomenon and propose a simple yet effective differential correction method to mitigate the SNR-t bias. Recognizing that
diffusion models typically reconstruct low-frequency components before focusing on high-frequency details during the reverse denoising process, we decompose samples into various frequency components and apply differential correction to each component individually. Extensive experiments show that our approach significantly improves the generation quality of various
diffusion models (IDDPM, ADM, DDIM, A-DPM, EA-DPM, EDM, PFGM++, and FLUX) on
datasets of various resolutions with negligible computational overhead. The code is at https://github.com/AMAP-ML/DCW.
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All authors Meng Yu, Lei Sun, Jianhao Zeng, Xiangxiang Chu, Kun Zhan
Computer Vision
Ranking XAI Methods for Head and Neck Cancer Outcome Prediction
Baoqiang Ma, Djennifer K. Madzia-Madzou, Rosa C.J. Kraaijveld et al. (+1 more)
For head and neck cancer (HNC) patients, prognostic outcome prediction can support personalized treatment strategy selection. Improving prediction performance of HNC outcomes has been extensively explored by using advanced artificial intelligence (AI) techniques on PET/CT data. However, the interpretability of AI remains a critical obstacle for its clinical adoption. Unlike previous HNC studies that empirically selected explainable AI (XAI) techniques, we are the first to comprehensively evaluate and rank 13 XAI methods across 24 metrics, covering faithfulness, robustness, complexity and plausibility. Experimental results on the multi-center HECKTOR challenge
dataset show large variations across evaluation aspects among different XAI methods, with Integrated Gradients (IG) and DeepLIFT (DL) consistently obtained high rankings for faithfulness, complexity and plausibility. This work highlights the importance of comprehensive XAI method evaluation and can be extended to other medical imaging tasks.
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All authors Baoqiang Ma, Djennifer K. Madzia-Madzou, Rosa C.J. Kraaijveld, Jin Ouyang
Computer Vision · Cross-listed from cs.MA
AstroVLM: Expert Multi-agent Collaborative Reasoning for Astronomical Imaging Quality Diagnosis
Yaohui Han, Tianshuo Wang, Zixi Zhao et al. (+6 more)
Vision Language Models (VLMs) have been applied to several specific domains and have shown strong problem-solving capabilities. However, astronomical imaging, a quite complex problem involving multidisciplinary knowledge and several subtasks, has not been adequately studied. Due to the complexity of the astronomical imaging process, both world-class astronomical organizations, such as NASA, and expert enthusiasts devote a great deal of time and effort. This is because the processes in astronomical imaging have complex underlying correlations that significantly influence one another, making the quality diagnosis and error localization of astronomical images challenging. To address this problem, we propose AstroVLM, a collaborative multi-
agent system for diagnosing the quality of astronomical images. Experiment results show that AstroVLM outperforms all baselines on real-world astronomical imaging quality diagnosis tasks, providing a reference for
language models to handle complicated multi-process tasks.
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All authors Yaohui Han, Tianshuo Wang, Zixi Zhao, Zhengchun Zhu, Shuo Ren, Yiru Wang, Rongliang Fu, Tinghuan Chen, Tsung-Yi Ho
Computer Vision
Breakout-picker: Reducing false positives in deep learning-based borehole breakout characterization from acoustic image logs
Guangyu Wang, Xiaodong Ma and Xinming Wu
Borehole breakouts are stress-induced spalling on the borehole wall, which are identifiable in acoustic image logs as paired zones with near-
symmetry azimuths, low acoustic amplitudes, and increased borehole radius. Accurate breakout characterization is crucial for in-situ stress analysis. In recent years, deep learning has been introduced to automate the time-consuming and labor-intensive breakout picking process. However, existing approaches often suffer from misclassification of non-breakout features, leading to high false positive rates. To address this limitation, this study develops a deep learning framework, termed Breakout-picker, with a specific focus on reducing false positives in automatic breakout characterization. Breakout-picker reduces false positives through two strategies. First, the training of Breakout-picker incorporates negative samples of non-breakout features, including natural fractures, keyseats, and logging artifacts. They share similar characteristics with breakouts, such as low acoustic amplitude or locally enlarged borehole radius. These negative training samples enables Breakout-picker to better discriminate true breakouts and similar non-breakout features. Second, candidate breakouts identified by Breakout-picker are further validated by azimuthal
symmetry criteria, whereby detections that do not exhibit the near-
symmetry characteristics of breakout azimuth are excluded. The performance of Breakout-picker is evaluated using three acoustic image log
datasets from different regions. The results demonstrate that Breakout-picker outperforms other automatic methods with higher accuracy and substantially lower false positive rates. By reducing false positives, Breakout-picker enhances the reliability of automatic breakout characterization from acoustic image logs, which in turn benefits in-situ stress analysis based on borehole breakouts.
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Computer Vision
IA-CLAHE: Image-Adaptive Clip Limit Estimation for CLAHE
Rikuto Otsuka, Yuho Shoji, Yuka Ogino et al. (+2 more)
This paper proposes image-adaptive contrast limited adaptive histogram equalization (IA-CLAHE). Conventional CLAHE is widely used to boost the performance of various computer
vision tasks and to improve visual quality for human perception in practical industrial applications. CLAHE applies contrast limited histogram equalization to each local region to enhance local contrast. However, CLAHE often leads to over-enhancement, because the contrast-limiting parameter clip limit is fixed regardless of the histogram distribution of each local region. Our IA-CLAHE addresses this limitation by adaptively estimating tile-wise clip limits from the input image. To achieve this, we train a lightweight clip limits
estimator with a differentiable extension of CLAHE, enabling end-to-end optimization. Unlike prior learning-based CLAHE methods, IA-CLAHE does not require pre-searched ground-truth clip limits or task-specific
datasets, because it learns to map input image histograms toward a domain-invariant uniform distribution, enabling zero-shot generalization across diverse conditions. Experimental results show that IA-CLAHE consistently improves recognition performance, while simultaneously enhancing visual quality for human perception, without requiring any task-specific training data.
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All authors Rikuto Otsuka, Yuho Shoji, Yuka Ogino, Takahiro Toizumi, Atsushi Ito
Computer Vision · Cross-listed from cs.CY
From Vulnerable Data Subjects to Vulnerabilizing Data Practices: Navigating the Protection Paradox in AI-Based Analyses of Platformized Lives
Delfina S. Martinez Pandiani, Ella Streefkerk, Laurens Naudts et al. (+1 more)
This paper traces a conceptual shift from understanding vulnerability as a static, essentialized property of data subjects to examining how it is actively enacted through data practices. Unlike reflexive ethical frameworks focused on missing or counter-data, we address the condition of abundance inherent to platformized life-a context where a near inexhaustible mass of data points already exists, shifting the ethical challenge to the researcher's choices in operating upon this existing mass. We argue that the ethical integrity of data science depends not just on who is studied, but on how technical pipelines transform "vulnerable" individuals into data subjects whose vulnerability can be further precarized. We develop this argument through an AI for Social Good (AI4SG) case: a journalist's request to use computer
vision to quantify child presence in monetized YouTube 'family vlogs' for regulatory advocacy. This case reveals a "protection paradox": how data-driven efforts to protect vulnerable subjects can inadvertently impose new forms of computational exposure, reductionism, and extraction. Using this request as a point of departure, we perform a methodological deconstruction of the AI pipeline to show how granular technical decisions are ethically constitutive. We contribute a reflexive ethics protocol that translates these insights into a reflexive roadmap for research ethics surrounding platformized data subjects. Organized around four critical junctures-
dataset design, operationalization, inference, and dissemination-the protocol identifies technical questions and ethical tensions where well-intentioned work can slide into renewed extraction or exposure. For every decision point, the protocol offers specific
prompts to navigate four cross-cutting vulnerabilizing factors: exposure, monetization, narrative fixing, and algorithmic optimization. Rather than uncritically...
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All authors Delfina S. Martinez Pandiani, Ella Streefkerk, Laurens Naudts, Paula Helm
Computer Vision
MMGait: Towards Multi-Modal Gait Recognition
Chenye Wang, Qingyuan Cai, Saihui Hou et al. (+2 more)
Gait recognition has emerged as a powerful biometric technique for identifying individuals at a distance without requiring user cooperation. Most existing methods focus primarily on RGB-derived modalities, which fall short in real-world scenarios requiring multi-modal collaboration and cross-modal
retrieval. To overcome these challenges, we present MMGait, a comprehensive multi-modal gait
benchmark integrating data from five heterogeneous sensors, including an RGB camera, a depth camera, an infrared camera, a
LiDAR scanner, and a 4D Radar system. MMGait contains twelve modalities and 334,060 sequences from 725 subjects, enabling systematic exploration across
geometric, photometric, and motion domains. Based on MMGait, we conduct extensive evaluations on single-modal, cross-modal, and multi-modal paradigms to analyze modality robustness and complementarity. Furthermore, we introduce a new task, Omni Multi-Modal Gait Recognition, which aims to unify the above three gait recognition paradigms within a single model. We also propose a simple yet powerful baseline, OmniGait, which learns a shared
embedding space across diverse modalities and achieves promising recognition performance. The MMGait
benchmark, codebase, and pretrained checkpoints are publicly available at https://github.com/BNU-IVC/MMGait.
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All authors Chenye Wang, Qingyuan Cai, Saihui Hou, Aoqi Li, Yongzhen Huang
Computer Vision · Cross-listed from cs.CR
When Safe Concepts Become Unsafe: Multi-Concept Compositional Vulnerabilities in Text-to-Image Models
Chaoshuo Zhang, Yibo Liang, Mengke Tian et al. (+7 more)
Text-to-image (T2I) models are increasingly optimized for following user instructions faithfully. However, we find that this capability introduces a safety vulnerability we call Multi-Concept Compositional Unsafety (MCCU). MCCU occurs when multiple individually safe concepts, if combined in a single generation request, lead to harmful or sensitive visual outputs. Unlike prior jailbreak settings, MCCU does not rely on
adversarial prompts, model access, or explicitly disallowed content. Instead, the risk emerges from how the model composes multiple safe visual concepts into a single scene. To systematically measure this threat, we build TwoHamsters, a large-scale evaluation framework consisting of 20k
prompts, 51 curated concept pairs, and six risk categories. We evaluate 13 T2I models under a black-box setting. Our results show a clear conflict between instruction-following and safety: models that follow
prompts more faithfully tend to produce more MCCU failures. For example, FLUX.1 achieves a 99.35% Unsafe
Alignment Rate while only reaching a 1.57% MCCU Defense Rate. We further evaluate three representative defenses, including safety filtering, MCCU-specific detector fine-tuning, and concept erasure, all of which fail against unseen concept combinations. Our findings suggest that compositional
reasoning in T2I models creates an attack surface that is not captured by existing safety mechanisms. We anticipate the release of TwoHamsters will catalyze community development of advanced generative defense mechanisms.
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All authors Chaoshuo Zhang, Yibo Liang, Mengke Tian, Chenhao Lin, Zhengyu Zhao, Le Yang, Chong Zhang, Yang Zhang, Qian Wang, Chao Shen
Computer Vision · Cross-listed from eess.IV
Topology-Driven Fusion of nnU-Net and MedNeXt for Accurate Brain Tumor Segmentation on Sub-Saharan Africa Dataset
Prabin Bohara, Pralhad Kumar Shrestha, Arpan Rai et al. (+8 more)
Accurate automatic
brain tumor
segmentation in Low and Middle-Income (LMIC) countries is challenging due to the lack of defined national imaging protocols, diverse imaging data, extensive use of low-field
Magnetic Resonance Imaging (MRI) scanners and limited health-care resources. As part of the
Brain Tumor
Segmentation (BraTS) Africa 2025 Challenge, we applied
topology refinement to the state-of-the-art
segmentation models like nnU-Net, MedNeXt, and a combination of both. Since the BraTS-Africa
dataset has low MRI image quality, we incorporated the BraTS 2025 challenge data of pre-treatment adult glioma (Task 1) to pre-train the
segmentation model and use it to fine-tune on the BraTS-Africa
dataset. We added an extra
topology refinement module to address the issue of deformation in prediction that arose due to topological error. With the introduction of this module, we achieved a better Normalized Surface Distance (NSD) of 0.810, 0.829, and 0.895 on Surrounding Non-Enhancing FLAIR Hyperintensity (SNFH) , Non-Enhancing Tumor Core (NETC) and Enhancing tumor (ET).
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All authors Prabin Bohara, Pralhad Kumar Shrestha, Arpan Rai, Usha Poudel Lamgade, Confidence Raymond, Dong Zhang, Aondona Lorumbu, Craig Jones, Mahesh Shakya, Bishesh Khanal, Pratibha Kulung
Computer Vision
From Competition to Coopetition: Coopetitive Training-Free Image Editing Based on Text Guidance
Jinhao Shen, Haoqian Du, Xulu Zhang et al. (+2 more)
Text-guided image editing, a pivotal task in modern multimedia content creation, has seen remarkable progress with training-free methods that eliminate the need for additional optimization. Despite recent progress, existing methods are typically constrained by a competitive paradigm in which the editing and reconstruction branches are independently driven by their respective objectives to maximize
alignment with target and source
prompts. The
adversarial strategy causes semantic conflicts and unpredictable outcomes due to the lack of coordination between branches. To overcome these issues, we propose Coopetitive Training-Free Image Editing (CoEdit), a novel zero-shot framework that transforms
attention control from competition to coopetitive negotiation, achieving editing harmony across spatial and temporal dimensions. Spatially, CoEdit introduces Dual-
Entropy Attention Manipulation, which quantifies directional entropic interactions between branches to reformulate
attention control as a harmony-maximization problem, eventually improving the localization of editable and preservable regions. Temporally, we present Entropic Latent Refinement mechanism to dynamically adjust latent representations over time, minimizing accumulated editing errors and ensuring consistent semantic transitions throughout the denoising trajectory. Additionally, we propose the Fidelity-Constrained Editing Score, a composite metric that jointly evaluates semantic editing and background fidelity. Extensive experiments on standard
benchmarks demonstrate that CoEdit achieves superior performance in both editing quality and structural preservation, enhancing multimedia information utilization by enabling more effective interaction between visual and textual modalities. The code will be available at https://github.com/JinhaoShen/CoEdit.
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All authors Jinhao Shen, Haoqian Du, Xulu Zhang, Xiao-Yong Wei, Qing Li
Computer Vision
SENSE: Stereo OpEN Vocabulary SEmantic Segmentation
Thomas Campagnolo, Ezio Malis, Philippe Martinet et al. (+1 more)
Open-vocabulary semantic
segmentation enables models to segment objects or image regions beyond fixed class sets, offering flexibility in dynamic environments. However, existing methods often rely on single-view images and struggle with spatial precision, especially under occlusions and near object boundaries. We propose SENSE, the first work on Stereo OpEN Vocabulary SEmantic
Segmentation, which leverages stereo
vision and
vision-
language models to enhance open-vocabulary semantic
segmentation. By incorporating stereo image pairs, we introduce
geometric cues that improve spatial
reasoning and
segmentation accuracy. Trained on the PhraseStereo
dataset, our approach achieves strong performance in phrase-grounded tasks and demonstrates generalization in zero-shot settings. On PhraseStereo, we show a +2.9% improvement in Average Precision over the baseline method and +0.76% over the best competing method. SENSE also provides a relative improvement of +3.5% mIoU on Cityscapes and +18% on KITTI compared to the baseline work. By jointly
reasoning over semantics and geometry, SENSE supports accurate scene understanding from natural
language, essential for autonomous
robots and Intelligent Transportation Systems.
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All authors Thomas Campagnolo, Ezio Malis, Philippe Martinet, Gaétan Bahl
Computer Vision
Neural Gabor Splatting: Enhanced Gaussian Splatting with Neural Gabor for High-frequency Surface Reconstruction
Haato Watanabe and Nobuyuki Umetani
Recent years have witnessed the rapid emergence of 3D Gaussian splatting (3DGS) as a powerful approach for 3D reconstruction and novel view synthesis. Its explicit representation with Gaussian primitives enables fast training, real-time rendering, and convenient post-processing such as editing and surface reconstruction. However, 3DGS suffers from a critical drawback: the number of primitives grows drastically for scenes with high-frequency appearance details, since each primitive can represent only a single color, requiring multiple primitives for every sharp color transition. To overcome this limitation, we propose neural Gabor splatting, which augments each Gaussian primitive with a lightweight multi-layer perceptron that models a wide range of color variations within a single primitive. To further control primitive numbers, we introduce a frequency-aware densification strategy that selects mismatch primitives for
pruning and cloning based on frequency energy. Our method achieves accurate reconstruction of challenging high-frequency surfaces. We demonstrate its effectiveness through extensive experiments on both standard
benchmarks, such as Mip-NeRF360 and High-Frequency
datasets (e.g., checkered patterns), supported by comprehensive ablation studies.
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Computer Vision · Cross-listed from cs.SD
Jiaxin Ye, Gaoxiang Cong, Chenhui Wang et al. (+4 more)
Video-to-
Speech (VTS) generation aims to synthesize
speech from a silent
video without auditory signals. However, existing VTS methods disregard the hierarchical nature of
speech, which spans coarse speaker-aware semantics to fine-grained prosodic details. This oversight hinders direct
alignment between visual and
speech features at specific hierarchical levels during property matching. In this paper, leveraging the hierarchical structure of Residual Vector Quantization (RVQ)-based codec, we propose HiCoDiT, a novel Hierarchical Codec
Diffusion Transformer that exploits the inherent hierarchy of discrete
speech tokens to achieve strong audio-visual
alignment. Specifically, since lower-level
tokens encode coarse speaker-aware semantics and higher-level
tokens capture fine-grained prosody, HiCoDiT employs low-level and high-level blocks to generate
tokens at different levels. The low-level blocks condition on lip-synchronized motion and facial identity to capture speaker-aware content, while the high-level blocks use facial expression to modulate prosodic dynamics. Finally, to enable more effective coarse-to-fine conditioning, we propose a dual-scale adaptive instance layer normalization that jointly captures global vocal style through channel-wise normalization and local prosody dynamics through temporal-wise normalization. Extensive experiments demonstrate that HiCoDiT outperforms baselines in fidelity and expressiveness, highlighting the potential of discrete modelling for VTS. The code and
speech demo are both available at https://github.com/Jiaxin-Ye/HiCoDiT.
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All authors Jiaxin Ye, Gaoxiang Cong, Chenhui Wang, Xin-Cheng Wen, Zhaoyang Li, Boyuan Cao, Hongming Shan
Computer Vision
Making Image Editing Easier via Adaptive Task Reformulation with Agentic Executions
Bo Zhao, Kairui Guo, Runnan Du et al. (+6 more)
Instruction guided image editing has advanced substantially with recent generative models, yet it still fails to produce reliable results across many seemingly simple cases. We observe that a large portion of these failures stem not from insufficient model capacity, but from poorly formulated editing tasks, such as those involving small targets, implicit spatial relations, or under-specified instructions. In this work, we frame image editing failures as a task formulation problem and propose an adaptive task reformulation framework that improves editing performance without modifying the underlying model. Our key idea is to transform the original image-instruction pair into a sequence of operations that are dynamically determined and executed by a MLLM
agent through analysis, routing, reformulation, and feedback-driven refinement. Experiments on multiple
benchmarks, including ImgEdit, PICA, and RePlan, across diverse editing backbones such as Qwen Image Edit and Nano Banana, show consistent improvements, with especially large gains on challenging cases. These results suggest that task reformulation is a critical but underexplored factor, and that substantial gains can be achieved by better matching editing tasks to the effective operating regime of existing models.
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All authors Bo Zhao, Kairui Guo, Runnan Du, Haiyang Sun, Pengshan Wang, Huan Yang, Kun Gai, Yixin Cao, Wei Ji
Computer Vision
Efficient Video Diffusion Models: Advancements and Challenges
Shitong Shao, Lichen Bai, Pengfei Wan et al. (+2 more)
Video diffusion models have rapidly become the dominant paradigm for high-fidelity generative
video synthesis, but their practical deployment remains constrained by severe inference costs. Compared with image generation,
video synthesis compounds computation across spatial-temporal
token growth and iterative denoising, making
attention and
memory traffic major bottlenecks in real-world settings. This survey provides a systematic and deployment-oriented review of efficient
video diffusion models. We propose a unified categorization that organizes existing methods into four classes of main paradigms, including step
distillation, efficient
attention, model compression, and cache/trajectory optimization. Building on this categorization, we respectively analyze algorithmic trends of these four paradigms and examine how different design choices target two core objectives: reducing the number of function evaluations and minimizing per-step overhead. Finally, we discuss open challenges and future directions, including quality preservation under composite acceleration, hardware-software co-design, robust real-time long-horizon generation, and open infrastructure for standardized evaluation. To the best of our knowledge, our work is the first comprehensive survey on efficient
video diffusion models, offering researchers and engineers a structured overview of the field and its emerging research directions.
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All authors Shitong Shao, Lichen Bai, Pengfei Wan, James Kwok, Zeke Xie