Computer Vision
Jie Zhu, Mingyu Ding, Boqiang Duan et al. (+2 more)
Diffusion transformer (DiT), a rapidly emerging architecture for image generation, has gained much
attention. However, despite ongoing efforts to improve its performance, the understanding of DiT remains superficial. In this work, we delve into and investigate a critical conditioning mechanism within DiT, adaLN-Zero, which achieves superior performance compared to adaLN. Our work studies three potential elements driving this performance, including an SE-like structure, zero-initialization, and a "gradual" update order, among which zero-initialization is proved to be the most influential. Building on this understanding, we propose an analysis-guided initialization strategy, termed adaLN-Gaussian, which serves both as an empirical validation of our analysis and as a practical initialization method that consistently improves optimization efficiency. On the other hand, inspired by the SE-like structure, we introduce an improved conditioning mechanism called SE-adaLN-Zero. Extensive experiments following DiT on four
datasets, especially on ImageNet1K, demonstrate the effectiveness and generalization of adaLN-Gaussian and SE-adaLN-Zero. Beyond class-to-image generation, we also evaluate the generalization of the two improved methods on text-to-image generation.
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All authors Jie Zhu, Mingyu Ding, Boqiang Duan, Leye Wang, Jingdong Wang
Computer Vision
Foundation Models are Implicit Deepfake Detectors
Stefan Smeu, Dragos-Alexandru Boldisor, Elisabeta Oneata et al. (+1 more)
Pretrained self-supervised representations have emerged as a core component of current deepfake detection methods, yet it remains unclear which of their properties make real and fake media distinguishable. In this work, we uncover a surprisingly consistent phenomenon: across multiple pretrained models,
datasets, and both image and
video domains, fake samples systematically produce lower-magnitude representations than their real counterparts. Motivated by this finding, we formulate deepfake detection as an anomaly detection problem and show that simple statistics of feature magnitude achieve competitive performance with far more sophisticated deepfake detection methods. We further investigate the origin of this effect and demonstrate that reduced feature magnitude is primarily associated with semantic shifts introduced by fake content, while low-level generative fingerprints play a comparatively smaller role. Finally, we show that this discriminative signal strengthens as the size of the underlying foundation model grows, suggesting that advances in representation learning naturally translate into stronger zero-shot deepfake detectors.
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All authors Stefan Smeu, Dragos-Alexandru Boldisor, Elisabeta Oneata, Dan Oneata
Computer Vision
MeanSR: Restoration Trajectory Learning for One-Step Perceptual Super-Resolution
Axi Niu, Jiawei Kou, Kang Zhang et al. (+3 more)
Diffusion-based super-resolution (SR) achieves strong perceptual quality but requires costly iterative denoising. Existing one-step
distillation methods reduce inference time but depend on expensive pretrained teachers, whereas CTMSR avoids
distillation through PF-ODE consistency training yet does not explicitly model the restoration dynamics from low-resolution (LR) inputs to high-resolution (HR) images. We propose MeanSR, a one-step perceptual SR method that learns an LR-conditioned average velocity field to directly capture the finite-time transition from degraded or noisy inputs to plausible HR outputs. We further reformulate distribution trajectory matching for average-velocity generation and introduce a Stage-Aware Temporal Sampling strategy to improve trajectory learning. Experiments on synthetic and real-world
benchmarks show that MeanSR outperforms CTMSR on CLIPIQA, MUSIQ, and MANIQA while substantially reducing FLOPs and inference latency. MeanSR also reconstructs sharper structures and more realistic textures with fewer perceptual artifacts.
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All authors Axi Niu, Jiawei Kou, Kang Zhang, Qingsen Yan, Jinqiu Sun, Yanning Zhang
Computer Vision
One Model to Magnify Them All: Efficient Scale-Invariant Histopathology via Conditional Normalization and Continuous Magnification Training
Agnieszka Florkowska, Henning Müller and Marek Wodzinski
Whole slide images (WSIs) in digital histopathology are acquired at discrete magnification levels encoding complementary diagnostic information from global tissue architecture to fine-grained cellular morphology. Yet, deep learning models remain sensitive to scale variation. Existing magnification-invariant methods rely on multi-scale architectures at predefined discrete resolutions, while in clinical deployment the acquisition magnification varies continuously, rarely aligns with a model's fixed training resolution, and intermediate scales are common, so robust coverage otherwise demands a costly ensemble of magnification-specific models. We propose Conditional Layer Normalization (CLN), a lightweight mechanism that generates affine normalization parameters from input pixel size via a small MLP, integrated into standard CNN architectures for both WSI classification and
segmentation. Trained on patches sampled continuously across a range of pixel sizes, the model decouples inference from scanner-dependent magnification and generalizes to arbitrary, previously unseen scales at test time. On the PANDA prostate cancer
dataset, our approach on average matches or exceeds independently trained single-magnification models and ranks among the top three performers at every evaluated magnification, including those unseen during training. This collapses a five-model ensemble into a single network and reduces training, and inference cost roughly 4-5 times, while leaving the multiply-accumulate count unchanged. The code is available at: https://github.com/aflorkowska/OneModelToMagnifyThemAll.
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Computer Vision
Sign Language Recognition Using Original and Synthetic Depth Image Based Point Cloud Data Models
Rustem Ozakar and Eyup Gedikli
Research regarding the sign
language recognition mostly relies on RGB images, whileas sign
language datasets that provide depth images are limited. Point clouds obtained from depth images can be used for sign
language recognition with neural networks like PointNet. In recent years, various neural networks are used for generating realistic depth images from
monocular RGB images. In this work, synthetic depth images were created from RGB images using Depth Anything V2 network. For this purpose, three sign
language datasets (Real-time ASL Fingerspelling, KArSL, AUTSL) which contain both RGB and depth images were used. Classification accuracies of the point cloud data created from both original and synthetic depth images using various PointNet architectures were measured for sign
language recognition. From the original and synthetic point clouds, frame based, Point Gesture Map and Long Short Term
Memory data models were used for classification and their performances were compared. In the results, both original and synthetic based data achieved acceptable performance in most models. In general, original depth based point cloud models performed better than synthetic ones, however in some models synthetic depth based models performed better than the originals.
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Computer Vision
CableDex: Cable Length Estimation on Industrial Reels Using a Handheld Device
Francisco Guillén, Ricardo Almeida, Bruno Silva et al. (+1 more)
CableDex is a computer
vision system that addresses the time-consuming and inaccurate manual measurement of cable length on industrial reels from a single photograph captured with a mobile phone. The system combines camera calibration, instance
segmentation, pose estimation, and volumetric calculation to estimate the cable length across five different reel types and various cable sizes. This system is based on an instance
segmentation model trained on 1,000 manually annotated images, achieving 99.5% mAP50 with an inference time of 5.66 ms per image. Evaluated on 75 reels across five reel types, the system achieves a MAPE of 4.90%, within the 10% error tolerance commonly accepted in industrial cable-reel measurement. The demonstration presents the end-to-end pipeline, from reel label scanning and image capture to
segmentation and length estimation, through the mobile application.
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All authors Francisco Guillén, Ricardo Almeida, Bruno Silva, João C. Neves
Computer Vision
CoInS-Net: A Continuous Position-Aware Network for Joint Medical Image Interpolation and Segmentation
Yujia Sun, Ningfeng Que, Peiting Shi et al. (+4 more)
Accurate medical image interpolation and anatomical structure
segmentation are fundamental for computer-aided diagnosis and treatment planning. Anisotropic medical volumes with sparse through-plane sampling often suffer from structural discontinuity and boundary blur, hindering reliable clinical image analysis. Most existing methods implement interpolation and
segmentation independently, which introduces redundant computation and fails to fully exploit complementary cross-slice structural information between sequential slices. To address these issues, we propose a continuous position-aware interaction network, termed CoInS-Net, for joint frame interpolation and lesion
segmentation. Unlike conventional cascaded interpolation-then-
segmentation paradigms, the framework enables bidirectional interaction under a shared Swin encoder with continuous spatial coordinate queries. A spatially continuous position interpolation module generates target-position features at every scale from the relative coordinate and physical spacing, and a prototype-based task mutual interaction module lets the
segmentation and interpolation branches exchange global structure through a small set of shared prototypes rather than dense feature mixing. A multi-scale task-cooperative decoder further separates each scale into shared and task-specific components, so the two tasks reinforce common anatomy while preserving their distinct requirements down to the boundary level, without extra annotations. Experiments on four public medical imaging
datasets with diverse modalities and anatomical regions demonstrate that the proposed method outperforms conventional single-task schemes. The joint optimization framework effectively realizes mutual promotion between interpolation and
segmentation tasks, providing a reliable and universal technical scheme for intelligent clinical medical image analysis.
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All authors Yujia Sun, Ningfeng Que, Peiting Shi, Rongrong Fu, Yingying Yang, Xinhang Li, Yin Dai
Computer Vision
Efficient Human-Contact Representation for Human-Scene Interaction
Nghia Vu, Tuong Do, Binh X. Nguyen et al. (+2 more)
Human-scene interaction is an active research topic with several industrial applications in virtual reality, gaming, robotics, and surveillance. Despite significant progress in network architectures to improve the results or optimize models' parameters for fast inference speed, the efficient representation of contact between humans and their environments remains an open challenge. In this paper, we propose a new efficient human-contact representation for human-scene interaction. Our primary contribution is the introduction of sparse contact masks that strategically select essential contact information, significantly reducing redundant data in high-dimensional inputs. Leveraging this efficient contact representation, we propose a suite of sparse operators to replace traditional dense operators within deep network layers for faster computation. Our approach not only enhances computational speed but also filters out non-essential contact data, thereby improving the precision of human-scene interaction models. To validate the effectiveness of our method, we conduct intensive experiments across three public
benchmark datasets, focusing on two critical tasks for human-scene interaction: contact prediction and scene synthesis. The experimental results show that our approach outperforms state-of-the-art models in reconstruction accuracy and achieves a computation speed-up of at least 12 times over recent baselines.
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All authors Nghia Vu, Tuong Do, Binh X. Nguyen, Erman Tjiputra, Anh Nguyen
Computer Vision · Cross-listed from cs.LG
Imaginative Generative AI: Crossing the Entropy Wall into Worlds Beyond Imitation
Farzan Farnia, Hossein Goli and Amin Gohari
Generative AI models are primarily designed to imitate the data distribution, an objective that neither corrects diversity lost by a learned generator nor defines how generation should extend beyond the diversity of the data itself. We introduce Imaginative Generative AI (IGA), a framework that makes diversity part of the target-distribution design problem: among distributions close to a reference, IGA selects one whose spectral diversity reaches a prescribed level. Diversity is measured by the von Neumann
entropy of the generated distribution's kernel covariance operator in a fixed representation space, providing a reference-free representation-guided measure of how broadly probability mass occupies
embedding directions. The spectral
entropy of the population data distribution defines an
Entropy Wall. Below the wall, IGA performs diversity repair, recovering variation that a learned generator has lost while remaining within the diversity level of the data. Beyond the wall, the data distribution itself becomes infeasible, and IGA deliberately departs from it to produce distributions with greater representation-relative spectral diversity, an operational notion of imaginative generation. These regimes form a single regularization path from imitation to imagination and define an i.i.d. target distribution at each prescribed diversity level. We develop the theory of this
entropy-constrained projection and show that, under a KL anchor to a pretrained generator, the optimum satisfies a self-consistent exponential-tilt relation. This characterization leads to IGA Guidance, a retraining-free inference-time method for score-based and
diffusion models, including DDPM and DDIM samplers. Experiments on synthetic and
vision benchmarks demonstrate diversity repair below the
Entropy Wall and controlled spectral extrapolation beyond it.
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Computer Vision · Cross-listed from cs.AI
CircuitReason-1k: Benchmarking Long-Horizon Visual-to-Symbolic Reasoning inElectrical Circuits
Xinqi Yang, Kang An, Tengyue Wang et al. (+8 more)
Electrical
circuit analysis requires more than recognizing components in an image. A solver must ground symbols and labels, recover latent
topology, select a physical model, formulate coupled equations, propagate intermediate quantities, and preserve units, signs, directions, and phase conventions. We introduce \benchmark, a
benchmark of 1,000 authentic textbook problems for evaluating this complete long-horizon visual-to-symbolic
reasoning process. Each problem pairs one or more
circuit diagrams with a self-contained question, a typed or semantically specified answer, and a reference worked solution. An evidence-first construction pipeline aligns questions, figures, and solutions, while a
reasoning-oriented taxonomy organizes problems by
circuit type and dependency depth. Evaluation combines conservative typed scoring with identity-blinded multi-model semantic consensus, retaining every problem in the denominator. Across three commercial chatbot systems and six open-source
multimodal large
language models, the highest-scoring system reaches 84.8% accuracy. However, performance consistently deteriorates on long-horizon problems, and qualitative analysis exposes persistent failures in
topology-to-target binding, physical conventions, and late-stage output propagation. \benchmark{} provides a focused testbed for measuring whether
multimodal models can transform technical visual evidence into sustained, physically valid symbolic
reasoning. Code are available at GitHub - CircuitReason/CircuitReason1K.
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All authors Xinqi Yang, Kang An, Tengyue Wang, Zhongyu Yang, Chenxu Du, Yuanchi Zhu, Hebao Zhu, Ziliang Wang, Faqiang Qian, Yunli Yang, Qibing Ren
Computer Vision
Preserve More Details: Mitigating Content Drift in Real-World Image Super-Resolution
Chunxiao Liu, Wei Liu, Anbin Xiong et al. (+1 more)
Real-world image super-resolution (Real-ISR) aims to reconstruct high-quality (HQ) images from low-quality (LQ) inputs subject to diverse real-world degradations. Recent advances have leveraged the LQ inputs and natural image priors learned by Stable
Diffusion models to achieve impressive results. However, existing methods often overlook insufficient clarity of LQ inputs inevitably induce content drift in the generated HQ images. This manifests primarily as visual detail degradation and textual semantic shift, severely compromising both fidelity and perceptual quality. To address this challenge, we propose FSP-Diff, a novel one-step
diffusion model featuring a dual-pathway architecture. This architecture comprises a Detail-Conditioned Pathway for injecting structured details to recover fine structures, and a Detail-Modulated Semantic Pathway that refines semantic guidance using structured details to mitigate semantic deviations. Extensive experiments on standard Real-ISR
benchmarks demonstrate that FSP-Diff surpasses existing one-step
diffusion methods in both quantitative and qualitative metrics.
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All authors Chunxiao Liu, Wei Liu, Anbin Xiong, Erli Meng
Computer Vision
FeedbackTrack: Visual-Cortex-Inspired Cross-Frame Feedback for Transformer Tracking
Yueyang Cang, Xiaoteng Zhang, Zhiyuan Ning et al. (+2 more)
Visual object tracking requires effective temporal integration, yet most
Transformer trackers still rely on predominantly feed-forward feature extraction. Existing temporal mechanisms typically update templates,
prompts, queries, or prediction states, while intermediate representations are rarely reused to modulate corresponding processing stages. We propose FeedbackTrack, a visual-cortex-inspired framework that introduces sparse, group-level layer-aligned cross-frame feedback into pretrained
Transformer trackers. Previous-frame intermediate states are detached, cached, and returned to corresponding
Transformer groups in the current frame through two lightweight pathways: Query Feedback for
token-level query modulation and Gate Feedback for context-dependent feature modulation. FeedbackTrack preserves the original tracking pipeline with only a fixed-size one-frame cache. Across SPMTrack and ARTrackV2, FeedbackTrack consistently improves five backbone configurations on LaSOT and GOT-10k, achieving 83.4 AO and 79.1 AUC with SPMTrack-G while adding less than 1% parameters. Controlled comparisons show that cross-frame feedback outperforms same-frame modulation by 1.8–3.2 AO points, demonstrating that the gains mainly come from recurrent historical information. Further analysis reveals a non-uniform depth-dependent organization of learned feedback strengths, highlighting the effectiveness of recurrent feedback for
Transformer tracking.
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All authors Yueyang Cang, Xiaoteng Zhang, Zhiyuan Ning, Yuchen He, Li Shi
Computer Vision
Deep Learning based Detection of Fishing Vessels and Fishing Monitoring using Nightlight Images
Shantakar Mohanty, Prasun Kumar Gupta and Raian Vargas Maretto
The demand for maritime surveillance has given rise to the need for monitoring fishing vessel activities, particularly in addressing the challenge of "dark vessels" that operate without Automatic Identification System (AIS) transmission. This study presents a novel approach for detecting small-scale fishing vessels using nighttime light (NTL) imagery from the SDGSAT-1 satellite, combined with deep learning techniques to enhance fishing monitoring awareness along the western coast of India. A dual-branch YOLO11 architecture was developed to exploit both the 10-meter panchromatic and 40-meter RGB imagery from SDGSAT-1. The custom model architecture was specifically optimized for small object detection in NTL imagery, featuring parallel convolutional backbones that process both modalities before concatenation for enhanced feature extraction. The dual-branch YOLO11 model demonstrated optimal performance with a precision of 0.99, recall of 0.93, F1-score of 0.96, and mAP@50 of 0.96, significantly outperforming single-branch implementations of YOLOv5s, YOLOv8s, and standard YOLO11s architectures. When applied to the western coast of India, the model detected 31525 vessel instances across the temporal
dataset spanning 2022-23. Cross-matching analysis with AIS data revealed that only 7146 (22.7%) of detected vessels had corresponding AIS transmissions, while 24379 (77.3%) were identified as potential dark vessels. Spatio-temporal analysis showed peak fishing activity during January-April, with a primary activity corridor parallel to the coastline within 50-100 km, corresponding to productive continental shelf areas. This research contributes to maritime surveillance capabilities by highlighting the effectiveness of nighttime lights satellite imagery for fishing vessel detection and provides valuable insights into fishing patterns and potential regulatory compliance issues in Indian waters.
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Computer Vision
ControlRadio: Prompt-Driven Controllable Diffusion for Cross-Modal Radio Map Generation
Kangjun Liu, Xiying Pan, Shuhang Zhang et al. (+3 more)
Radio maps describe how wireless signals propagate across space and are essential for wireless communication, sensing, and network planning. However, constructing accurate
radio maps traditionally requires either dense measurements or computationally expensive physical simulations, which limits scalability and real-time deployment. Recent advances in generative artificial intelligence offer a promising alternative, but existing approaches lack fine-grained control and physical consistency when applied to real-world wireless environments. Here we present ControlRadio, a controllable generative framework that produces
radio maps from natural-
language descriptions and environmental layouts, including building structures and transmitter locations. Joint semantic and spatial conditioning enables interpretable, propagation-plausible generation, while a controlled latent prior and layout-aware conditioning improve stability and structural consistency. Extensive experiments demonstrate that ControlRadio achieves state-of-the-art accuracy and strong generalization across diverse urban scenarios, while reducing computation time by more than four orders of magnitude compared with conventional simulation-based methods. Such results suggest a new paradigm for scalable and controllable wireless environment modeling, with broad implications for next-generation communication systems and data-driven
radio sensing.
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All authors Kangjun Liu, Xiying Pan, Shuhang Zhang, Xiang Xiang, Ke Chen, Yaowei Wang
Computer Vision
Alpha as an Efficiency Signal: Visibility-Routed RGBA Image-to-Video Generation
Zhe Li, Honghao Qiao, Zhixin Xu et al. (+3 more)
RGBA
videos combine RGB appearance with an alpha channel, enabling animated assets to be applied across arbitrary backgrounds, which are heavily used in gaming industry. However, generating high-quality RGBA animations for games remains challenging for two reasons. First, most existing RGBA
video datasets are dominated by photorealistic content, with limited coverage of game assets. Second, the traditional generate-then-matte pipelines estimate alpha only after RGB synthesis, so semi-transparent regions are often blurred by background, resulting in unstable matting outputs. More recently, many methods have begun to model RGB and alpha jointly, but existing approaches are mostly text-conditioned, and still have unresolved issues in efficiency and quality. To address these challenges, we introduce GameAlpha-2.4K, a 2.4K-clip game-style RGBA
video dataset built with matte-friendly synthesis, multi-hypothesis alpha recovery, and compositing-based quality gates. Using this
dataset, we train a reference-conditioned RGBA
video generator that jointly produces RGB frames and alpha mattes in a single pass. To improve efficiency, we propose a visibility router that identifies transparent
tokens in an early stage and bypasses their later DiT updates, while x_0-lock guides them along the original flow-matching schedule toward self-predicted endpoints. Our model obtains lower FVD than traditional two-stage pipelines, and the visibility router skips 35% of
token evaluations in the final two DiT denoising steps, providing a 1.2x backbone speedup with negligible quality degradation compared to dense inference.
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All authors Zhe Li, Honghao Qiao, Zhixin Xu, Qijie Wang, Bo Peng, Dawei Li
Computer Vision
One-Time Training for All Grains: Open-Set Grain Recognition and Quantitative Analysis
Qihe Su, Mengyu Sun, Yuxi Ke et al. (+4 more)
Advances in crop breeding have introduced an increasing number of grain varieties, creating a growing demand for efficient variety recognition and quantitative analysis. However, existing methods are typically trained on a fixed variety set, and incorporating newly introduced varieties requires additional data collection and model retraining. To address this limitation, we propose GROW, a framework for Grain Recognition and quantitative analysis in Open sets Without retraining. GROW first performs class-agnostic grain localization, converting mixed-grain images into individual instances for variety-wise counting and phenotypic measurement. It then combines visual
embeddings and morphological descriptors into fused grain descriptors stored in an extensible GrainBank. Query grains are recognized through rank-similarity weighted top-k
retrieval, and newly introduced varieties are incorporated by appending their descriptors without updating the deployed models. Extensive experiments under progressive variety expansion, varying grain densities, and background domain shifts demonstrate the scalability, robustness, and adaptability of GROW. Compared with joint retraining, GROW reduced the average category-registration time from 4153 s to only 39 s while maintaining competitive recognition performance. These results demonstrate that GROW provides an efficient and maintainable solution for extensible grain recognition, counting, and phenotypic analysis without repeated model retraining.
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All authors Qihe Su, Mengyu Sun, Yuxi Ke, Zhuoyan Jiang, Wanneng Yang, Chenglong Huang, Ziyuan Yang
Computer Vision
Beyond Global Editing: Per-Instance Disentangled Subspaces for Training-Free Hallucination Mitigation in LVLMs
Ali Cheraghian, Hamidreza Dastmalchi, Hamed Barzamini et al. (+4 more)
Recent advances in large
vision-
language models (LVLMs) have enabled powerful
multimodal reasoning by integrating visual encoders with large
language models (LLMs). However, their reliability is frequently undermined by
hallucinations, where generated text inaccurately describes the visual input. Although fine-tuning can mitigate this problem, it is computationally expensive and requires large, curated
datasets, making training-free alternatives attractive. Among these, model editing is more promising than decoding-based approaches: decoding methods adapt outputs per input but introduce computational overhead and instability, whereas model editing modifies internal representations offline, providing a more efficient and stable solution. However, existing model-editing techniques typically rely on a single global subspace to correct
hallucinations, treating all test samples identically and failing to capture diverse
hallucination modes across inputs. To address this limitation, we propose a training-free
hallucination mitigation framework for dynamic, per-instance suppression at test time. Our method first constructs a set of Disentangled
Hallucination Subspaces, each isolating a distinct
hallucination mode. During inference, the model adaptively calculates weights reflecting each input's relationship to these subspaces, guiding a dynamically combined projection that selectively suppresses the most probable
hallucination directions while preserving image-grounded semantics. Extensive experiments across multiple
vision-
language benchmarks and LVLM families demonstrate consistent improvements, highlighting the robustness, generalizability, and efficiency of our approach.
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All authors Ali Cheraghian, Hamidreza Dastmalchi, Hamed Barzamini, Morteza Saberi, Mojtaba Golzan, Shafin Rahman, Hossein Rahmani
Computer Vision
Revisiting the Current Frame: Physical-Trace-Guided Network Output Correction for Video Restoration
Yifeng Lin, Liuxiang Qiu, Guangming Ren et al. (+1 more)
Video restoration methods exploit temporal information to recover information missing from degraded observations. However, reference frames within the sequence may introduce inconsistent degradation, content discrepancy, or reconstruction errors due to physical image-formation variations, occlusion, and imperfect temporal aggregation. Existing approaches mainly focus on improving restoration networks, while the reliability of the generated outputs at different spatial locations remains largely unexplored. In this work, we propose ANCHOR, a model-agnostic framework that revisits the low-quality current frame as a temporally aligned anchor for
video restoration correction. Specifically, ANCHOR estimates a spatial trust field from heterogeneous physical-trace evidence and adaptively balances the restoration proposal with the original observation. Experiments on High Dynamic Range
video reconstruction and
video deraining demonstrate consistent improvements across various state-of-the-art restoration models, validating the effectiveness of reliability-aware output correction for
video restoration.
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All authors Yifeng Lin, Liuxiang Qiu, Guangming Ren, Tiesong Zhao
Computer Vision · Cross-listed from cs.AI
GeoPhysAdapter: Scale-Matched Geophysical Adaptation for Cross-Domain Landslide Mapping with Vision Foundation Models
Zhihang Liu, Mei-Po Kwan, Jinlin Wu et al. (+1 more)
Newly triggered landslides rarely carry immediate annotations, so cross-domain transferability determines the value of landslide mapping for emergency response and regional risk assessment.
Vision foundation models have strengthened representational transfer, yet on unseen regions, events, and data sources they still generate high-confidence false alarms. Terrain, material, and rainfall triggering can constrain such errors, but their supports are local, regional, and event-scale, so that resampling onto a 10~m grid misaligns them with the
segmentation decision unit and compounds the uncertain geographic context problem (UGCoP). We propose GeoPhysAdapter, which anchors on a frozen
vision foundation model, restricts terrain, material, and triggering to dense spatial guidance, regional modulation, and event-timing forcing, and applies bounded adaptation at two decision units, the pixel and the candidate landslide body, reverting exactly to the visual prediction where support is insufficient. On an event-isolated PILD
dataset of four public sources, 55 global landslide events, and 7,890 test samples, 70.3% of cross-domain false-positive mass lies in near-pure spurious bodies of median equivalent diameter 207m, matching coarse-prior support rather than the pixel. Pixel-level adaptation removes a net 507,817 erroneous pixels and reduces error by 7.76%, whereas raising the decision unit to the candidate body, under identical samples, anchor, and baseline, increases error reduction to 23.99%, approximately 3.1 times the pixel-level effect, improves IoU by 0.031 (14.2% relative), and corrects 9.92 pixels per pixel harmed. The data and code are publicly available at: https://github.com/Liu-Zhihang/geophysadapter.
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All authors Zhihang Liu, Mei-Po Kwan, Jinlin Wu, Hao Li
Computer Vision
Diffusion Image Editing via Asynchronous Token Decoding
Yang Shi, Liangsi Lu, Minzhe Guo et al. (+4 more)
Text-guided
diffusion image editing aims to modify semantic attributes of an image while preserving its identity, layout, and background. However, naïvely switching the text condition during sampling often causes global drift, as denoising dynamics propagate changes across
tokens and can disrupt unedited regions. To address this issue, we propose Asynchronous
Token Decoding Edit (ATDEdit), an inference-time framework that views each sampler step as a parallel update of a globally coupled
token matrix and enables
token-indexed condition switching with differentiated update policies. Instead of applying synchronous target-conditioned updates to all
tokens, ATDEdit estimates editable locations using
token-wise conditional surprisal and applies target-conditioned corrections to the selected
token set. It supplies source key/value
memory at keep-
token positions and projects selected keep-
token latent rows back to their source values; these operations promote background preservation but do not constitute a pixel-level invariance guarantee. This approach combines local editing and background preservation without external or user-provided spatial masks and without model fine-tuning. On PIE-Bench, ATDEdit achieves the strongest reported preservation metrics, including 27.44~dB PSNR and 0.055 LPIPS, while retaining competitive semantic
alignment.
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All authors Yang Shi, Liangsi Lu, Minzhe Guo, Yifeng Xie, Yanhui Chen, Jingchao Wang, Xuhang Chen
Computer Vision
Warp-free Cross-view Geo-localization via Feature-space Consensus Mining
Zhuo Song, Lian Xu, Runqing Jiang et al. (+4 more)
Cross-view geo-localization is challenging due to drastic viewpoint changes and large appearance discrepancies between street-level and satellite imagery. Although existing methods often use
geometric warping to expose co-visible cues, such transformations rely on restrictive spatial assumptions and inevitably introduce severe visual distortions under view-dependent visibility, yielding noisy supervision and fragile correspondences. To overcome this, we propose a novel joint-view consensus-guided learning framework that entirely bypasses explicit
geometric warping. Instead of forcing rigid spatial
alignment, we dynamically mine and adaptively strengthen a semantic consensus directly within the feature space. Specifically, an auxiliary joint-view pathway during training enables direct cross-view interaction, allowing each view to selectively aggregate corroborative evidence into a unified consensus representation. To resolve feature heterogeneity among the single- and joint-view streams, we introduce global pattern probes acting as a semantic dictionary to project divergent modalities into a strictly aligned metric space. Guided by a consensus-mediated contrastive objective, single-view
embeddings are explicitly pulled toward the joint-view anchor during training, distilling this consensus-mining capability into the single-view encoders for robust
retrieval at inference. Extensive experiments demonstrate that our method achieves state-of-the-art performance across four standard
benchmarks, underscoring the importance of discovering cross-view semantic consensus for reliable geo-localization.
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All authors Zhuo Song, Lian Xu, Runqing Jiang, Yongjian Zhang, Kunhong Li, Ye Zhang, Yulan Guo
Computer Vision
MemeMind: Reference-Guided Trace Construction for Offline Context Optimization
Run Yang, Weihang Wang, Boheng Sheng et al. (+7 more)
Offline context optimization improves an
agent by revising its instructions and examples while keeping the model frozen. This approach learns from rollouts on an adaptation set, but some queries produce only failed rollouts. In these cases, the optimizer sees no successful example of how the available tools can reach the correct answer. We introduce MemeMind, which uses an offline reference answer to recover this missing experience. TraceBuilder identifies the evidence required by the reference, executes text search, image
retrieval, and visual grounding, and verifies the resulting tool trace before adding it to the adaptation buffer. ToolGuide then summarizes the collected traces into a shared guide and separate instructions for each tool. The reference answers and constructed traces are used only during adaptation, while inference uses the learned guides with a frozen model. We study this problem through Anime, Comic, and Game meme interpretation. These memes combine edited and ambiguous visual content, overlaid text, long tail franchise knowledge, and culture specific references. Their interpretation can require coordinated visual grounding, image
retrieval, and text search, making them a demanding setting in which native rollout groups may fail together. We evaluate MemeMind on MemeX, a
benchmark of 1,000 such memes annotated by experts. Across two Qwen3-VL models, two
language partitions, and two independent judges, MemeMind improves over the strongest context optimization baseline by 22.0% and 21.1% on Qwen3-VL-30B-A3B, and by 8.1% and 8.0% on Qwen3-VL-235B-A22B under GPT-5 judging. Ablations and held out traces show that constructing successful tool use for failed groups provides the largest component gain and produces more effective evidence acquisition at inference time.
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All authors Run Yang, Weihang Wang, Boheng Sheng, Yuchen He, Jielei Zhang, Pengyu Chen, Zhiyu Wu, Qiang Sun, Huyang Sun, Longwen Gao
Computer Vision
Degraded Infrared Small Object Detection via Degradation-Adapted Physics-Guided Restoration
Xinkai Lu, Wenjun Chen, Yi Li et al. (+2 more)
Infrared small object detection has made significant progress in recent years. However, degradations such as fog and nonuniformity can suppress target-background contrast, substantially increasing detection difficulty. Existing methods mainly rely on image restoration as preprocessing, but they are typically designed for specific degradation types and fail to generalize to varying degradations. To alleviate this, we propose DAISOD, a degradation-adapted infrared small object detection framework for robust detection under different degradations. DAISOD first identifies the type and severity of degradations, then adapts the processing via dedicated branches, and finally fuses the results for subsequent detection. Moreover, a physics-guided restoration mechanism is incorporated to explicitly estimate degradation parameters and remove degradation effects through physical models, avoiding excessive restoration that may erase small targets. Moreover, we construct a degraded infrared small object detection
dataset covering diverse degradation types and levels. Extensive experiments show that DAISOD outperforms state-of-the-art methods under various degradation conditions.
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All authors Xinkai Lu, Wenjun Chen, Yi Li, Yi Chang, Luxin Yan
Computer Vision
Jun Huang, Meiyi Chen, Zijie Yue et al. (+6 more)
Hysteroscopic
surgical scene
segmentation plays a pivotal role in understanding the hysteroscopic intraoperative environment as well as computer-assisted intervention. However, this task presents unique challenges due to the high morphological similarity among different lesions and the presence of artifacts such as specular reflections, motion blur, and fluid occlusions in
surgical videos. In this work, we propose the first
vision-
language model (VLM)-based hysteroscopic
surgical scene
segmentation method, which performs pixel-wise localization for fifteen representative categories in hysteroscopic
surgical scenes. Our VLM-hyster has a
segmentation backbone that utilizes the pretrained image encoder for robust visual feature extraction, coupled with a
transformer-based decoder for dense prediction. Moreover, we design category-specific text
prompts and incorporate a masked
distillation branch to filter out visual features with low correlation to the text
prompts, enabling the model to focus more effectively on category-specific image regions and thereby enhancing
segmentation performance. We collect a large multicentric hysteroscopic
surgical scene
dataset, containing 4,020 high-resolution images with detailed mask annotations, for model training and evaluation. Experimental results demonstrate that VLM-hyster substantially outperforms state-of-the-art AI models. Furthermore, extensive assessments by gynecologists, as well as multicentre and prospective validations, demonstrate VLM-hyster's robustness and generalizability. The results suggest that VLM-hyster earns considerable potential in enabling AI-assisted localization of
surgical instruments and lesions in hysteroscopic surgeries. Code is available at https://github.com/viscom-tongji/VLM-hyster.
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All authors Jun Huang, Meiyi Chen, Zijie Yue, Yuhang Xiao, Fang Li, Hanli Wang, Xiaowen Tong, Yi Guo, Miaojing Shi
Computer Vision · Cross-listed from cs.AI
CADEngBench: It Looks Like CAD, but Does It Work? Evaluating Parametric Design, Assembly Reasoning, and Physics Simulation
Harmanjot Singh, Abhra Dubey and Jorge Alejandro Amador Herrera
A CAD model is not engineering-grade merely because it looks correct. It must satisfy design requirements, respond predictably to parameter changes, support controlled edits, match a reference structural response under a declared analysis, and connect to other parts through valid joints. We present CADEngBench, a two-track
benchmark for these capabilities. CADEngBench-P evaluates 300 parametric parts, each used for one zero-to-CAD task and one functional-editing task (600 tasks in total), through boundary-representation (B-Rep) validity, engineering and DFM checks, parameter-family perturbations, functional editing, and matched linear-static FEA in CalculiX. CADEngBench-A evaluates 150 body pairs through ranked joint
retrieval, exact face-and-edge grounding, joint-frame prediction, and kinematic verification. Across eight
multimodal, code-capable models, editing supplied CAD is substantially easier than generating it, while complex edits and matched FEA remain difficult. Assembly predictions often locate the relevant region but fail to recover the recorded joint or mating entities. These results show that CAD evaluation must test engineering behavior rather than appearance alone.
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Computer Vision
UniDFKD: A Unified Semantic Prior Framework for Architecture-Agnostic Data-Free Knowledge Distillation
Xuewan He, Tong Chu, Zihan Cheng et al. (+5 more)
Data-Free Knowledge
Distillation (DFKD) transfers knowledge from a pretrained teacher model to a compact student model by synthesizing semantically informative data, eliminating the need for access to the original training
dataset. Existing DFKD methods rely heavily on architecture-specific statistical priors (e.g., Batch Normalization statistics) to guide data synthesis, however, such architecture-dependent priors are often absent in modern architectures such as
Vision Transformers (ViTs), resulting in degraded semantic quality of the synthesized data and consequently catastrophic performance degradation. In this paper, we propose UniDFKD, a unified data-free knowledge
distillation framework that replaces architecture-specific statistics with explicit, architecture-agnostic semantic priors. UniDFKD governs the entire synthesis-
distillation pipeline along three dimensions: (1) Categorical Semantic Conditioning (CSC) defines what to synthesize by persistently modulating the generator with
language-derived
embeddings to capture semantic diversity; (2) Spatial Semantic Anchoring (SSA) dictates where evidence belongs by anchoring the teacher's spatial attributions to a Gaussian prior; and (3) Spatial Semantic
Distillation (SSD) controls how knowledge is transferred by explicitly aligning teacher-student spatial evidence alongside predictions. Extensive experiments across CNNs and ViTs demonstrate that UniDFKD establishes a new state-of-the-art, outperforming existing methods by an average absolute margin of over 20% in both homogeneous and heterogeneous settings.
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All authors Xuewan He, Tong Chu, Zihan Cheng, Yuchen Su, Qianxin Xia, Guoming Lu, Jielei Wang, Wen Li
Computer Vision
GRASP: Granularity-Aware Region Alignment and Semantic Prototype Learning for Fine-Grained Cross-Modal Understanding in Drone Views
Jiahui Cui, Yan Zhao, Kan Wei et al. (+4 more)
Fine-grained cross-modal understanding in drone views is essential for aerial
vision-
language navigation. However, the inherent wide field of view and overhead perspective of drone scenarios impose dual challenges on
vision-
language understanding. At the macro level, overwhelming background clutter in visual representations leads to Cross-Modal Focus Misalignment, where the model prioritizes global environmental similarities over specific object details. At the micro level, Visual Isomorphism creates ambiguity, where candidates share similar
geometric structures yet differ only in subtle attributes. To address these challenges, we propose the Granularity-Aware Region
Alignment and Semantic Prototype (GRASP) learning framework, enhancing discriminative capability through two synergistic strategies. Specifically, we introduce Region-Focused
Alignment (RFA) to promote object-centric cross-modal
alignment while suppressing background interference. Concurrently, to tackle visual isomorphism, we propose Semantic Perturbation Enhanced Matching (SPEM), which leverages a foreground-purified Semantic Prototype Codebook (SPC) to construct semantically perturbed negatives for fine-grained semantic discrimination. Extensive experiments on the GeoText-1652
benchmark and the unseen ERA
dataset demonstrate that GRASP achieves competitive performance in drone-view fine-grained image-text
retrieval, validating its effectiveness for cross-modal understanding in aerial scenarios. Our code implementation is available at https://github.com/UCAS-JC/GRASP.
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All authors Jiahui Cui, Yan Zhao, Kan Wei, Enze Zhu, Peirong Zhang, Lei Wang, Yiru Wang
Computer Vision
Did the Grid Erase the Event? EndoClock for Auditing Medical World-Model Pipelines
Yarin Udi, Tom Sharon-Shahak, Roee Masad et al. (+1 more)
Medical world models commonly learn from
multimodal recordings synchronized onto a fixed-rate grid. This preprocessing resamples each native stream onto a shared time axis. Each stream has an observation clock that governs when observations are emitted or updated. When this clock depends on the latent or acquisition state, it is endogenous. In such settings, synchronization may not be neutral and can erase task-relevant evidence before the model sees the data. We introduce a four-regime taxonomy that characterizes where the evidence needed to distinguish a target event or state survives. The relevant witness may remain in the sampled values, in grid-
cell update patterns, in native timing, or only in an external acquisition channel. EndoClock operationalizes this taxonomy as a conservative pretraining audit. It reports the lowest witness-bearing representation supported by the available evidence, or unresolved when no regime can be established. We illustrate this failure in echocardiography, where B-mode
video write-outs cease during pulsed-wave Doppler acquisition while the corresponding measurement events remain recorded only in an external acquisition log. This work is a preliminary failure alert and executable audit. Its practical message is to preserve the native observation process long enough to determine whether synchronization has erased information required by the intended task.
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All authors Yarin Udi, Tom Sharon-Shahak, Roee Masad, Dan Pri-Tal
Computer Vision
Task-Adaptive 3D Cross-Field MRI Translation via Field-Conditioned Content-Style Pretraining
Haowen Pang, Yingqi Hao and Pengli Zhu
Magnetic field strength is a major source of domain shift in
magnetic resonance imaging (MRI), affecting signal-to-noise ratio, tissue contrast, spatial detail, and the visibility of anatomical boundaries. The MRIxFields 2026 challenge investigates this problem through cross-field MRI translation across acquisitions at 0.1T, 1.5T, 3T, 5T, and 7T. Its three tasks, Any-to-7T, 0.1T-to-High, and Any-to-Any synthesis, require the generation of target-field image characteristics while preserving subject-specific anatomy. This problem is particularly challenging because paired acquisitions of the same subject across multiple field strengths are rarely available for training. We propose a 3D unpaired cross-field MRI translation framework based on field-conditioned content-style pretraining. The proposed framework first learns controllable field-to-field translation across all available field strengths by disentangling anatomical content from field-dependent contrast characteristics. The pretrained backbone is then adapted to task-specific target domains. Our model comprises a 3D content encoder, a 3D style encoder, a field-conditioned style generator, an AdaIN-modulated decoder, and a multi-field discriminator.
Adversarial learning encourages realistic target-field appearance, while cycle-consistency, identity, content, style, and diversity constraints promote anatomical fidelity and controllable translation. We evaluate the proposed method on MRIxFields data spanning five field strengths and three MRI modalities. Experiments on paired test data demonstrate that the framework can adapt to the three challenge settings while preserving three-dimensional anatomical structure in the synthesized volumes. The implementation code is publicly available at https://github.com/Idea89560041/3D-MRI-Field-Translation.
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Computer Vision
In-Loop Model Adaptation with Coupled Latent-Noise Guidance for High-Fidelity Subject-Driven Text-to-Image Generation
Yushun Tang, Weiming Chen, Siyi Liu et al. (+3 more)
Text-to-image
diffusion models have achieved remarkable success in generating high-quality images from a given text
prompt. Subject-driven generation aims to synthesize customized images to mimic the appearance of subjects in given reference images within different visual contexts specified by the text
prompts. The central challenge here is that, when the reference image changes, the
diffusion model cannot efficiently adapt to different visual contexts while consistently maintaining the subject identity. Existing methods either train the model with a large domain-specific
dataset or fine-tune the model using the reference image for hundreds of iterations before actual image generation. In this work, we explore a new approach, called In-Loop Model Adaptation (IMA), which adapts the core
diffusion model at each generation step during the actual process of image generation, without being trained on the reference image before the generation process. To this end, we establish a DDIM inversion chain that maps the reference image to a sequence of latent, as well as a text-to-image generation chain which generates the image from the text
prompt only. We then introduce a masked latent consistency loss and a noise regularization loss to characterize the latent-noise difference between the
diffusion model and these two chains at each generation step. This coupled latent-noise loss is used to guide the in-loop model adaptation to preserve the subject identity specified by the reference image while maintaining accurate
alignment with the text
prompt, resulting in high-fidelity text-to-image generation. Our extensive experiments demonstrate that our proposed IMA method significantly improves the performance of subject-driven text-to-image generation.
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All authors Yushun Tang, Weiming Chen, Siyi Liu, Yi Zhang, Feng Wu, Zhihai He