Computer Vision
Dual Co-Train: Cross-Dataset Ultrasound Tongue Segmentation Under Extreme Data Scarcity
Alisher Myrgyyassov, Zhen Song, Bruce Xiao Wang et al. (+4 more)
Ultrasound tongue contour
segmentation remains challenging under cross-
dataset domain shift, where limited annotations, probe variability, and acquisition noise often degrade model generalization. We present a source-free domain adaptation framework for robust ultrasound tongue
segmentation built on a lightweight UltraUNet backbone. Starting from a checkpoint pretrained on only five labeled source images, simulating an underfitted constrained source model, the proposed method adapts to a fully-unlabeled target domain by iteratively refining pseudo-labels, filtering unreliable masks with a contour-based quality-control module, and generating target-style synthetic image-mask pairs through a
segmentation-guided conditional GAN. The student model is then trained on a mixture of clean pseudo-labeled target images, noisy pseudo-labels with consistency regularization, and synthetic samples, enabling closed-loop adaptation without access to source data. We evaluate the method on 12 source-target transfer pairs across eight ultrasound tongue imaging
datasets, and conduct source-size scaling experiments and ablation studies. Across all comparisons, the proposed framework improves
segmentation overlap and contour accuracy over the baselines, including supervised ones. These results suggest that task-specific pseudo-label refinement and synthetic target-style augmentation can substantially improve source-free adaptation for ultrasound tongue imaging.
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All authors Alisher Myrgyyassov, Zhen Song, Bruce Xiao Wang, Yu Sun, Min Ney Wong, Yihao Zhou, Yongping Zheng
Computer Vision · Cross-listed from cs.GR
aDSL: Agentic 3D Creation via Joint Agent-Program Design
Rui-Huan Wang, Si-Tong Wei, Jia-Qi He et al. (+3 more)
Programmatic representations provide a compelling paradigm for 3D content creation, enabling fine-grained edits, interpretability, and explicit structural control. Yet, agentic workflows that rely on large
language models (LLMs) to author 3D programs remain brittle, often failing to translate high-level intent into consistent low-level geometry. We attribute this fragility to a mismatch between existing programmatic interfaces and the
reasoning strengths of LLMs, which favor semantic structure and spatial relations over fragile numeric choices. In this paper, we jointly design an
Agent-centric Domain-Specific
Language (aDSL) and a role-specialized multi-
agent system to close this gap. aDSL bridges semantic logic and
geometric constraints by emphasizing composability and spatial
reasoning; it enables
agents to manipulate geometry through relational operators instead of brittle absolute coordinates. Building on aDSL, our training-free multi-
agent system follows a Plan-Execute-Critic loop to decompose requests, synthesize code, and iteratively repair errors and constraint violations using execution feedback. Experiments show that this co-design improves robustness, controllability, and faithfulness to user intent. Our method outperforms prior LLM-based baselines on text-to-shape and image-to-shape tasks while preserving explicit structure, editability, and interpretability. It also enables downstream applications such as articulated object creation and structured scene composition. Our code is available at https://github.com/sig-pku/aDSL.
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All authors Rui-Huan Wang, Si-Tong Wei, Jia-Qi He, Heng-Yi Wei, Baoquan Chen, Peng-Shuai Wang
Computer Vision
LinCa: Accelerating Diffusion Models via Learnable Decomposed Feature Caching
Jinshan Liu, Haoran Qin, Xiaobing Tu et al. (+9 more)
Diffusion models have achieved remarkable success in image and
video generation, yet the high computational cost of iterative sampling remains a critical bottleneck for practical deployment. Feature caching has emerged as a promising acceleration paradigm by reusing or predicting intermediate features across timesteps. However, existing training-free methods apply uniform prediction strategies that cannot adapt to the heterogeneous feature dynamics, causing significant quality degradation under high acceleration ratios. We propose LinCa, a feature caching framework based on learnable invertible networks. LinCa decomposes cached features into sub-components with distinct continuity properties via a lightweight invertible network and applies differentiated prediction orders matched to each component. The strict invertibility guarantees lossless reconstruction back to the original feature space, forming a unified Decompose-Predict-Reconstruct pipeline. By training separate predictors for different models and timestep segments, LinCa adapts to heterogeneous feature dynamics. Experiments on FLUX, Qwen-Image, and HunyuanVideo demonstrate that LinCa, with less than 0.2% additional parameters, significantly outperforms existing methods and maintains near-lossless quality at 5-7x speedup. Code: https://github.com/QHR69/LinCa
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All authors Jinshan Liu, Haoran Qin, Xiaobing Tu, Jiacheng Liu, Jiahui Hu, Zhengan Yan, Yukun Xie, Kerui Shen, Jinkui Ren, Yuqi Lin, Xiantao Zhang, Linfeng Zhang
Computer Vision
SFMformer: A Spatial-Frequency Modulation Transformer for Lightweight Image Super-Resolution
Chih-Hsiang Yang, Chia-Min Lin, Ching-Yu Tsai et al. (+2 more)
Sparse
attention mechanisms, which score all
token pairs but propagate only the strongest, now underpin the most efficient
Transformers for lightweight image super-resolution. This paper observes that sparsification changes what it means to improve such a network. A dense
attention layer has one place where representation quality matters: the aggregation of attended features. A sparse layer has two, because the top-k operator first decides which
tokens survive and only then decides what to do with them, and a
token discarded at the selection stage cannot be recovered downstream. Selection quality and aggregation quality are therefore separable targets, addressed by modules placed before and after the
attention respectively. We test this by pairing a dual-branch spatial enhancement on the input of a progressive focused
attention with a wavelet-domain modulation on its output, forming SFMformer. Measuring each module alone and jointly over all fifteen
benchmark-scale pairs, we find their gains are not additive: the joint gain exceeds the sum of the individual gains on nine pairs, and the sign of the discrepancy is predicted by how much the weaker module contributes on its own (r = -0.72), so the two compound when they relieve different constraints and overlap when they relieve the same one. Enabling spectral modulation once per block rather than once per layer retains the effect at roughly one-sixth of its cost, keeping the model below one million parameters at every scale. SFMformer ranks first on 28 of 30 PSNR/SSIM entries across five
benchmarks and three upscaling factors. We report the cases where the pairing does not help, and deploy the model on a Raspberry Pi 5 to confirm the design is practical under tight resource budgets.
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All authors Chih-Hsiang Yang, Chia-Min Lin, Ching-Yu Tsai, Yung-Che Wang, Jen-Shiun Chiang
Computer Vision
Cross-Domain Generalization in Machine Unlearning via Label-Conditioned Energy Magnitude Regularization
Syed Ali Ahmed, Syed Bilal Ahsan and Muhammad Zaigham Zaheer
Machine unlearning removes the influence of specific data from a trained model. However, most methods treat the forgotten concept as isolated. In this paper, we study what happens to the rest of the model when a class is forgotten, using a label-conditioned energy-based model (EBM) that assigns per-class energies, making the effect directly observable. We forget a class by raising the energy of its image-label pairs, training with a forget term, a retain anchor to the pretrained model, a global margin, and an energy regularizer that stops the energy magnitudes from growing without limit. A propagation term applies the same forget signal to retain samples, weighted by each sample's DINOv2 similarity to the forget class, so forgetting reaches images that resemble it and leaves the rest untouched. We evaluate on two
benchmark datasets: 1) On a subset of DomainNet across four visual domains, we forget tiger, lion, and scissors one at a time. Forgetting a class in the sketch domain also erases it from real, clipart, and painting, with forgetting error reaching 98% and 99% for lion and scissors, and the effect carrying over to the most similar class. 2) On CIFAR-10, we turn off the propagation term and forget each of the ten classes on its own. Forgetting is complete (100%), while the other nine classes retain 98.5% of their pre-unlearning accuracy on average.
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Computer Vision
Beyond Instrument Motion: Recognizing Tissue Tension Toward Surgical Skill Assessment
Marko Haralović, Zhiqi Miao, Alexander Machiel Bont et al. (+3 more)
Surgical performance assessment in minimally invasive surgery largely relies on manual expert review, making it time-consuming, subjective, and difficult to scale. While existing
surgical video understanding methods address tasks such as instrument
segmentation,
surgical phase recognition, and action recognition, they do not explicitly capture fine-grained tissue handling, a key indicator of
surgical quality. To address this gap, we introduce tissue tension recognition, a new clinically motivated
video understanding task for laparoscopic and
robot-assisted rectal cancer surgery. To support this task, we construct SurgTension, the first expert-annotated tissue tension
dataset, providing a
benchmark for objective tissue tension recognition. We further propose TensionTRAC, a lightweight trajectory-based framework that models tissue tension from sparse point trajectories. Using a compact trajectory encoder, TensionTRAC achieves competitive performance against strong pretrained
video backbones.
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All authors Marko Haralović, Zhiqi Miao, Alexander Machiel Bont, Jiapan Guo, Frans van Workum, Estefanía Talavera
Computer Vision
PerFact: Perception-Derived Fact Prompting for 3D Brain MRI Report Generation
Jianyu Sun, Zhenxuan Zhang, Guang Yang et al. (+1 more)
Radiology report generation has matured almost entirely on 2D chest radiographs, where the default route to better reports is a larger backbone or a pre-training one on medical data. We revisit that assumption on 3D multi-sequence
brain MRI, a volumetric multi-disease regime, and find that the model is not the lever. Zero-shot medical and radiology
vision-
language models transfer poorly to
brain MRI, with chest radiograph specialists failing most conspicuously, and five backbones fine-tuned identically across three model families and an order of magnitude in scale differ only marginally. What determines the quality of the report is the information injected into the
prompt. We delegate perception to upstream 3D
segmentation and classification, serialize their outputs into a structured fact sentence, and
prompt a LoRA-adapted
vision-
language model with it; we call this PerFact. In a controlled study that fixes the backbone, data split, target reports, and adaptation while varying only the injected grounding, perception-derived facts outperform retrieved prior reports,
retrieval becomes redundant once facts are present, and end-to-end predicted facts remain effective without any ground-truth annotation at inference. The residual gap between predicted and oracle facts is explained by the granularity of the facts rather than by the generator. Closed-ended visual question answering comes at no measurable cost to report quality, though the grounding source has little effect on it. On 3D
brain MRI, grounding information, not model choice, is the dominant controllable factor in report quality.
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All authors Jianyu Sun, Zhenxuan Zhang, Guang Yang, Peter J. Lally
Computer Vision
AppendiGrade: An XAI-Enhanced Deep Learning Framework for Grading Appendicitis in Ultrasound with Gaussian Blur and Grad-CAM
Fahad Ahammed, Omar Faruq Shikdar, Navid Zaman et al. (+3 more)
Appendicitis is one of the most common abdominal emergencies worldwide and requires
prompt diagnosis and treatment to prevent life-threatening conditions. However, accurately differentiating complicated cases, such as perforation or abscess formation, from uncomplicated appendicitis remains a significant clinical challenge. Among other methods, ultrasound is a safer and more cost-efficient diagnostic technique because of the lack of radiation exposure. In this research, an advanced system capable of automatically detecting complicated appendicitis from ultrasound images was developed. A
dataset consisting of 4679 ultrasound images with 5 classes, namely perforated, abscess, acute, appendicolith, and normal, was used for the proposed model training and testing. Four pretrained deep learning models, DenseNet201, InceptionV3, ConvNextTiny, and VGG19, have been employed for detecting and classifying complicated appendicitis. In the initial configuration, InceptionV3 achieved the second highest accuracy, with a value of 69.21%. Owing to suboptimal performance with raw images, further optimization techniques, including image preprocessing, hyperparameter tuning, model fine-tuning, and image sharpening, were applied. These enhancements significantly improved the model's performance, with an accuracy of 95.58% for InceptionV3. The model performance is then explained with gradient-weighted class activation mapping (Grad-CAM), which creates a heatmap of the regions responsible for the model's prediction of the infected areas. This could make crosschecking with experts much easier.
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All authors Fahad Ahammed, Omar Faruq Shikdar, Navid Zaman, Md Tahsin, Md. Nawab Yousuf Ali, Golam Sorwar
Computer Vision
Comparative Study of Out-of-the-Box Technology for Automatic Target Detection and Recognition
Alma M. Liezenga, Lotte Nijskens, Henrik R. Baumann et al. (+12 more)
Automatic Target Detection and Recognition (ATD/R) is critical for military decision support and (semi-)autonomous operations. Recent advances in object detection and artificial intelligence (AI) significantly boosted the potential performance of ATD/R. However, the scarcity of publicly available military
datasets limits the application of these systems. As a solution, this paper explores the use of publicly available models and civilian
datasets to achieve reasonable performance in military contexts. We
benchmark several state-of-the-art models, including six iterations of the YOLO series and two variations on the DETR framework, on a newly acquired military relevant
dataset. This
dataset features military vehicles and challenging circumstances, including various degrees of occlusions and small targets. The out-of-the-box version of each model is validated alongside a version finetuned on the VisDrone
dataset. This
dataset features small objects, an Air-to-Ground (A2G) perspective and relevant classes, potentially generalizing to our military ATD/R task. We compare the performance of the models using mAP@0.5 and mAP@0.5:0.95, across A2G and Ground-to-Ground (G2G) perspective, target size and model size, giving insight into the real-time capabilities of models. Our main findings are: (1) bigger models outperform smaller models, (2) DETR-based models show promising results compared to the YOLO series,(3) fine-tuning models on an out-of-domain A2G
dataset, improves their A2G performance and slightly improves their performance on small objects, but (4) all models still struggle with detecting small objects in an A2G scenario. We conclude that, despite recent advances in object detection, in-domain training is still crucial for creating capable ATD/R systems.
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All authors Alma M. Liezenga, Lotte Nijskens, Henrik R. Baumann, Stefan Becker, Simon Bensberg, Niccolò Camarlinghi, Håvard R. Eiring, Alexander W. Johnsgaard, Tanel Liiv, Giuseppe Martino, Matteo Marturini, Matthias Rapp, Jan Erik van Woerden, Alexander Wolpert, Hugo J. Kuijf
Computer Vision · Cross-listed from cs.IR
VisDocAgentBench: Benchmarking Agents for Visually Rich Document Retrieval
Lexiang Hu, Yanzhao Zhang, Mingxin Li et al. (+5 more)
Visually rich documents encode relevance through
language, layout, structured visual elements, and corpus context, yet
retrieval is typically evaluated by one-shot query–page matching. Agentic-search
benchmarks usually score downstream question answering or report generation, leaving document ranking under iterative evidence acquisition underexplored. We introduce VisDocAgentBench, a closed-corpus
benchmark comparing static and agentic
retrieval under a shared ranked-output contract. It contains 2,375 pages from 100 documents and 120 unique-target queries balanced across direct, one-bridge, and two-bridge evidence structures. Relation-preserving construction yields semantic, relational, and visual queries, followed by full-document review and hard-negative validation. A strong late-interaction visual retriever reaches 97.50% Recall@1 on direct items but 2.50% on two-bridge items, exposing the limits of query–target matching when relevance depends on corpus context.
Agents recover much of this loss, but planner choice and
retrieval representation remain decisive. Every planner performs better with visual
retrieval, whose best R@1 reaches 67.50% versus 37.50% for OCR-text. Ablations identify iterative search and page inspection as consequential capabilities, and providing the complete support context improves ranking on both routes. Trace analysis localizes the remaining losses to target discovery, candidate examination, and evidence-role integration. These findings motivate
retrieval agents that combine modality-preserving discovery with evidence-directed verification.
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All authors Lexiang Hu, Yanzhao Zhang, Mingxin Li, Dingkun Long, Yikang Li, Fuwei Zhang, Yisen Wang, Zhouchen Lin
Computer Vision
CFB-GBM v2.0: An Augmented Longitudinal Dataset for Multi-Modal Glioblastoma Segmentation, Radiomics, and RANO Progression Tracking
Alexandre G. Leclercq, Noémie N. Moreau, Hugo Audebert et al. (+21 more)
Glioblastoma (GBM) is the most aggressive primary
brain tumor in adults, with a median overall survival of 15 months. Longitudinal, multi-modal imaging
datasets with comprehensive clinical and treatment data are essential to support the development of reproducible computational methods for treatment response prediction, disease progression modelling, and personalized medicine. We present CFB-GBM v2.0, an extension of our previously released CFB-GBM
dataset comprising 264 GBM patients treated according to the standard Stupp protocol. The primary contribution of this release is the completion of Gross Tumour Volume (GTV) delineations across all available timepoints ($t_0$, $t_1$ and $t_2$), increasing the overall GTV completion rate from 35% to 97%. This was achieved using a nnU-Net model pre-trained on BraTS 2021 and fine-tuned on CFB-GBM ground-truth contours, with the generated
segmentations validated by five radiation oncologists. From these longitudinal GTV annotations, volumetric RANO 2.0 response category labels were derived for all available temporality pairs ($t_0 \rightarrow t_1$, $t_0 \rightarrow t_2$ and $t_1 \rightarrow t_2$). To further ease
dataset usability and reproducibility,
brain masks computed with HD-BET and pre-computed radiomic features extracted with PyRadiomics are provided for each patient timepoint and MRI modality. Additionally, the WHO classification guideline (2016 vs. 2021) applicable to each patient's diagnosis is now explicitly documented. CFB-GBM v2.0 is publicly available on The Cancer Imaging Archive (TCIA) at https://www.cancerimagingarchive.net/collection/cfb-gbm .
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All authors Alexandre G. Leclercq, Noémie N. Moreau, Hugo Audebert, Andros Nassar, Thomas Cochin, Thomas Leleu, Loïc Le Henaff, Alexis Desmonts, Yoann Poirier, Aurélie Dubru, Laura Guillemette, Pascal Lecoeur, Kévin Lemasson, Cyril Jaudet, Sébastien Bougleux, Romain Hérault, Carole Brunaud, Samuel Valable, Dinu Stefan, Charlotte Raboutet, Alain Batalla, Joëlle Lacroix, Roman Rouzier, Aurélien Corroyer-Dulmont
Computer Vision
Improving Complex Moiré Removal with Generative Supervision
Xinyang Gu, Zhilu Zhang, Honglei Xu et al. (+3 more)
The availability of high-quality paired data is essential for training learning-based image demoiréing models. However, it remains challenging for existing
datasets to encompass the complex moiré patterns captured in uncontrolled real-world scenarios. Such degradations typically manifest as large-scale, multicolored moiré patterns. Moreover, these patterns frequently occur in images for which clean counterparts are difficult to obtain, such as photographs acquired from public displays or existing online resources. In this work, we propose a novel data engine designed to improve the removal of complex moiré patterns by generating training supervision. Specifically, we initially collect real-world images containing complex moiré patterns and localize the corresponding screen regions. Multiple image-conditioned generative foundation models are subsequently deployed to produce candidate references. To establish reliable supervision, these candidates are subjected to patch-level quality control to filter and select the optimal results. Based on this systematic paradigm, we construct the WildMoiré
dataset, which contains 6.8K moiré-GT training pairs. For evaluation, we additionally build an independent test set comprising $\sim$250 pairs with captured clean ground truth. Extensive experiments on ESDNet, SDXL, and Qwen-Image-Edit demonstrate that the proposed generative supervision consistently improves the performance of complex moiré removal.
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All authors Xinyang Gu, Zhilu Zhang, Honglei Xu, Yanting Mei, Yukang Ding, Wangmeng Zuo
Computer Vision
DistillPath: An Efficient 22M Distilled Pathology Encoder Approaching Large Foundation Model Performance
Ramon Kaspar, Andrey Ignatov and Valentina Boeva
Many high-performing pathology tile encoders are now foundation models with hundreds of millions to over a billion parameters. Encoding and storing the thousands of tiles in each whole-slide image with such models is costly on commodity hardware, so compact encoders that retain useful downstream performance are a valuable alternative. We present DistillPath-KS16, which starts from the existing 22M kaiko ViT-S/16 encoder and improves it by distilling from released pathology encoders used as frozen teachers. The recipe reads only the teachers' final class and patch
tokens and trains on 6,000 public slides, needing neither their DINO nor iBOT pretraining heads nor a billion-tile corpus, so it applies to any released encoder that exposes backbone
tokens. We distill four teachers spanning 86M to 1.1B parameters into the same student. Every variant improves the kaiko baseline on all three
benchmarks we use, EVA, HEST, and PLISM, and the strongest teacher is task-dependent. On the seven-task EVA mean, DistillPath-KS16-Virchow2 reaches $0.795$, within $0.015$ points of Virchow2, the top-scoring model in our evaluation, at about $29\times$ fewer parameters; it also scores above H0-mini and GPFM on this aggregate metric, though that advantage is task-concentrated rather than uniform. Because it remains a 22M ViT-S/16 with 384-dimensional features, DistillPath-KS16 runs more than $25\times$ faster than Virchow2. Code is available at https://github.com/RamonKaspar/DistillPath, and released model weights are available at https://huggingface.co/collections/RamonK/distillpath.
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Computer Vision
GenRec: Knowing Where to Reconstruct and Where to Generate
Ata Çelen, Jaewoo Jung, Federico Tombari et al. (+4 more)
Generative novel view synthesis from sparse input images is rarely all reconstruction or all generation: pixels visible in some source view have a unique correct value modulated only by view-dependent shading, while pixels in disocclusions or beyond the captured volume admit a distribution of plausible completions. Existing generative novel-view-synthesis methods conflate these regimes under a single uniform loss, blurring the line between
geometric fidelity and creative
hallucinations even when scene geometry is injected through warped point clouds or projected depth. We introduce GenRec, a multi-view flow matching model that builds the reconstruction–generation split directly into its architecture, supervision, and gradient flow. Guided by an observation mask derived from the source cameras and a
monocular depth
estimator, a flow matching backbone jointly denoises RGB and scene-coordinate maps across all target views, while a pixel-space refinement stage restores high-frequency detail on observed pixels; the same mask gates supervision so regression signals do not contaminate the generative prior. Across RealEstate10K, DL3DV-10K, and Mip-NeRF~360, in both single-view extrapolation and two-view interpolation, GenRec attains the best reconstruction fidelity in observed regions while also surpassing purely generative baselines on perceptual quality in unobserved ones, showing the effectiveness of our approach.
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All authors Ata Çelen, Jaewoo Jung, Federico Tombari, Marc Pollefeys, Sunghwan Hong, Michael Niemeyer, Daniel Barath
Computer Vision
Scale Matters: Adaptive Granularity Selection for Cross-Species 3D Plant Organ Segmentation
Carla Salazar and Lazaros Nalpantidis
Recent 3D foundation models provide powerful feature representations for point cloud learning by controlling spatial granularity. However, relying on a fixed spatial granularity severely limits generalization in applications like plant phenotyping, where organ morphology and size vary substantially across species and growth stages. To address this, we propose AGS-PlantSeg, a few-shot 3D plant organ
segmentation method that leverages the frozen Utonia (arXiv:2603.03283) foundation model combined with Adaptive Granularity Selection. By dynamically selecting the best granularity levels for each specific plant model, our method extracts optimized
geometric features for a lightweight MLP
segmentation head. Extensive experiments across PLANesT-3D (arXiv:2407.21150), Pheno4D , and Crops3D demonstrate that AGS-PlantSeg significantly improves cross-species generalization, achieving 88.9% average mIoU performance and outperforming fixed-granularity baselines by 2.5 mIoU points. Despite requiring minimal annotated data, our approach is highly competitive with fully supervised, plant-specific architectures.
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Computer Vision
Training with synthetic data for drone detection in thermal imagery
Tanel Liiv, Sander Soodla, Nzamba Bignoumba et al. (+2 more)
Ground-to-Air (G2A) drone detection in medium- and long-wave infrared (MWIR/LWIR) imagery is challenging due to reduced texture information, sensor noise, weak thermal contrast, and the scarcity of annotated data. This work investigates a synthetic-first training strategy that combines synthetic scene generation with fine-tuning on real data. We show that synthetic data provides an effective basis for learning initial object representations, while real in-domain thermal imagery is still essential for reliable deployment. Even small amounts of real IR data substantially reduce domain gaps. Our experiments indicate that
dataset alignment has a stronger impact on performance than model scale. Finally, our analysis of the
dataset suggests that semantic
alignment in feature space is the strongest predictor of model performance, while radiometric properties such as
entropy and dynamic range also contribute to detection robustness. This work provides a foundation for combining synthetic and real IR data for effective G2A drone detection.
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All authors Tanel Liiv, Sander Soodla, Nzamba Bignoumba, Alma M. Liezenga, Toomas Pruuden
Computer Vision · Cross-listed from cs.AR
ETHEREAL: A 25.6-$\mu$s/inf. Low-latency Event-driven Graph-neural-network Processor for High-resolution Vision at the Edge
Adrian Kneip, Martin Lefebvre, Daniel Gehrig et al. (+4 more)
Dynamic
vision sensors (DVS) are enticing candidates to reach the low-latency, sub-ms target of edge-
vision applications, as they generate events with a $\mu$s-level time resolution. However, using DVS front ends also calls for novel
algorithm/hardware back ends capable of efficiently handling streams of sparse spatiotemporal events. While event-driven graph neural networks (EV-GNNs) have emerged as a solution on the algorithmic side that is both accurate and efficient, there is no dedicated hardware to date capable of efficiently supporting their mixed requirements of dense-regular compute operations and sparse-irregular
memory accesses. We therefore introduce ETHEREAL, the first EV-GNN processor chip, capable of bridging this gap by means of a neighbor-parallel spline-convolution engine combined with a split-2D/3D
memory hierarchy that introduces a novel spatiotemporal event-caching mechanism. Measurement results demonstrate a 25.6$\mu$s latency and a 1.6$\mu$J energy per end-to-end event-wise inference on the state-of-the art DAGr-GNN workload and VGA-resolution (640x480 pixels) DSEC
dataset.
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All authors Adrian Kneip, Martin Lefebvre, Daniel Gehrig, Victoria Catalán Pastor, Davide Scaramuzza, Marian Verhelst, Charlotte Frenkel
Computer Vision
TINA+: Probing Residual Visual Knowledge in Unlearned Diffusion Models via Diffusion-Consistent Text-Free Inversion
Qianlong Xiang, Miao Zhang, Kun Wang et al. (+3 more)
Although text-to-image
diffusion models exhibit remarkable generative power, concept erasure techniques are essential for preventing harmful content. Existing
adversarial probes evaluate these methods by testing whether erased concepts can still be recovered. However, existing erasure and probe methods remain largely text-centric, focusing on whether the text-to-image mapping is severed while overlooking whether the corresponding visual knowledge remains. To investigate this question from a visual perspective, we leverage
diffusion inversion to probe whether a generative trajectory can reconstruct visual instances of an erased concept. Under a null-text condition, standard inversion avoids the textual pathway but amplifies approximation errors, hindering faithful trajectory recovery. To address this challenge, we introduce TINA+, a
diffusion-consistent Text-free INversion Attack equipped with optimization-based inversion. We also find that unconstrained
diffusion inversion may discover spurious trajectories, even allowing a randomly initialized
diffusion model to reconstruct the target concept. Such trajectories may falsely indicate residual visual knowledge. TINA+ therefore introduces
Diffusion-Consistent Trajectory Regularization to suppress this failure mode. By penalizing trajectories that fall far below the expected marginal energy
evolution of
diffusion, TINA+ suppresses spurious inversion paths while preserving its ability to recover erased concepts. Experiments across twelve erasure methods, four concept-erasure tasks, and different model architectures demonstrate that TINA+ reliably probes residual visual knowledge through
diffusion-consistent visual trajectories. These results provide stronger evidence that current methods often obscure concepts by severing text-image links rather than eliminating the underlying visual knowledge.
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All authors Qianlong Xiang, Miao Zhang, Kun Wang, Haoyu Zhang, Junhui Hou, Liqiang Nie
Computer Vision
Evaluation of AI-based Visual Crack Detection in Steel Bridges Using Probability of Detection
Andrii Kompanets, Finn Michael Sherry, Remco Duits et al. (+2 more)
Bridge structures are regularly inspected for structural damage such as cracks and corrosion in order to ensure public safety and reduce maintenance costs. Much research has been done on automating this process using computer
vision methods, which are often evaluated and compared using metrics such as intersection over union, mean average precision, etc. However, predicting the actual effectiveness of an inspection method within the field of structural engineering from these metrics remains challenging. To enable the systematic use of these increasingly popular methods in engineering practice, evaluating the performance of these methods in a way that is compatible with standard engineering approaches is therefore an urgent necessity. We present a new statistical evaluation framework to allow the comparison of computer
vision methods with conventional visual inspection for crack detection in steel bridges. The framework is based on probability of detection curves and can account for the influence of image resolution. We apply this evaluation method to the real-world “Cracks in Steel Bridges”
dataset, which contains annotated images of cracks in bridge structures. The quantification of the probability of detection and its uncertainty enables a practical assessment of the effect of automated methods for damage detection in structural reliability analyses. In turn, this enables the wide-spread use of automated (AI-based) damage detection in safety critical applications. This evaluation method provides evidence that the proposed computer
vision approach approach is robust for the crack detection task and can have a high added value as an addition to conventional visual inspection methods.
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All authors Andrii Kompanets, Finn Michael Sherry, Remco Duits, Davide Leonetti, H.H. Snijder
Computer Vision
Vision-Language Models for Analog Gauge Reading: An Empirical Study of Specialization, Transfer and Reliability
Abdul Mueez, Aaditya Baranwal, Junior Chaj-Mejia et al. (+3 more)
Analog gauges remain common in industrial environments where manual inspection is costly or hazardous. The engineering application addressed here is direct numerical reading of single-target analog-gauge images, while the artificial-intelligence contribution is a systematic evaluation of specialization, transfer, robustness and reliability for a general-purpose
vision-
language model (VLM) without an explicit pointer-
segmentation and
geometric-reading pipeline. The Qwen2.5-VL-7B-Instruct model is evaluated using zero-shot prompting, in-context learning (ICL) and parameter-efficient fine-tuning with Quantized Low-Rank Adaptation (QLoRA) on a public synthetic
dataset, a
video-derived Pressure Gauge
dataset and a proprietary industrial
dataset. All fine-tuning experiments use a fixed 20-epoch protocol with the final epoch used for analysis; separate models with and without supplied gauge ranges remove
prompt-setting confounds. The primary metric is range-normalized mean percentage error (MPE). The best fine-tuned MPE values are 2.39% on the synthetic
dataset, with a 95% bootstrap confidence interval (CI) of 1.43-3.90%; 2.61% on the Pressure Gauge
dataset, with a CI of 1.66-3.80%; and 4.43% on the proprietary industrial
dataset, with a CI of 2.31-7.14%. Leave-one-
dataset-out experiments reveal substantial transfer degradation on held-out synthetic and proprietary data, while robustness tests identify Gaussian blur as the strongest tested corruption. Reliability analysis shows that high-confidence errors remain possible, motivating abstention and independent validation in safety-critical use. These results support QLoRA-specialized VLMs for direct single-gauge reading but not yet a deployment-ready plant-monitoring pipeline.
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All authors Abdul Mueez, Aaditya Baranwal, Junior Chaj-Mejia, Guneet Bhatia, Jason T. Voelker, Shruti Vyas
Computer Vision
DynaForcing: Overcoming Dynamic Collapse in Self-Forcing Distillation for Streaming Avatar Generation
Yubo Huang, Sirui Zhao, Xinchen Yao et al. (+5 more)
Audio-driven avatar generation requires realistic lip-sync, expressive motion, and real-time streaming. Recent work achieves the latter via self-forcing with Distribution Matching
Distillation (DMD), but this paradigm suffers from a critical failure that has not been systematically characterized: dynamic collapse, where the student model converges to a near-static optimum with high perceptual quality but severely suppressed temporal dynamics. We trace this to two causes: the reverse KL objective in DMD, which biases toward low-motion modes, and unanchored self-conditioning, which creates a feedback loop that amplifies collapse. This is especially harmful for avatars, where even subtle motion loss breaks lip-sync and expression. To address this, we propose DynaForcing, a training framework with three complementary strategies applied at different levels. Specifically, Hybrid Forcing anchors rollouts to ground-truth dynamics at the data level to break the feedback loop. Dynamics-Aware Reward Regularization introduces explicit motion rewards via the RL interpretation of DMD to counteract the reverse KL bias at the loss level. Reference Perturbation perturbs reference images to decouple identity from static details, forcing the model to rely on audio for motion at the conditioning level. We further introduce computation graph
pruning and gradient replay, reducing the GPU footprint of self-forcing by over an order of magnitude. Experiments show that DynaForcing recovers dynamics to teacher-comparable levels (Dyn-Deg: 0.31 -> 0.73, Sync-C: 7.03 -> 7.68) while improving visual quality, resolving the quality-dynamics trade-off throughout training without early stopping.
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All authors Yubo Huang, Sirui Zhao, Xinchen Yao, Zhengye Zhang, Jinyang Huang, Fengqi Cui, Shiwei Wu, Enhong Chen
Computer Vision
Monitoring Pasture Restoration from Satellite Image Time Series: Caveats and Opportunities
Linnea Sartorius, Isak Randahl, Delia Fano Yela et al. (+3 more)
Monitoring nature restoration at scale is an important but difficult ecological problem. Deep learning methods to analyze satellite image time series (SITS) have been widely used for land surface monitoring. In semi-natural grasslands - the habitat type in focus in this work - restoration outcomes develop gradually, yet satellite observations are influenced by weather, acquisition conditions, and processing artefacts, making it difficult to distinguish genuine restoration signals from unrelated temporal variation. In this work, we examine - to the best of our knowledge, for the first time - whether restoration status can be detected directly from satellite image time series by formulating pasture restoration as a binary deep learning classification problem. We evaluate two common SITS deep learning architectures on different Sentinel-2 image combinations, across 1,397 restored Swedish pastures and find that explicitly modeling intra-year variability and per-pasture normalization increases separability, reaching 0.88 accuracy for the best model. We further investigate our results and perform a targeted bias analysis finding that reliable deployment requires temporally balanced labels and evaluation protocols that explicitly test for year-related confounding. We therefore frame our contribution not as a solved restoration-monitoring system, but as a realistic case study of what works, what fails, and what future studies should control for. Code and models are available at https://github.com/aleksispi/ml-nature-resto.
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All authors Linnea Sartorius, Isak Randahl, Delia Fano Yela, Georg Andersson, Sadegh Jamali, Aleksis Pirinen
Computer Vision
Environment-Invariant Subspace Learning for Generalizable Deepfake Detection
Shenghao Chen, Hao Jia, Chen Li et al. (+3 more)
Cross-distribution generalization remains a critical bottleneck in deepfake detection. While recent efforts leverage the semantic priors of large-scale visual foundation models (VFMs), a noteworthy yet underexplored challenge remains: the susceptibility of these semantic priors to environmental interference from factors such as lighting and style. Crucially, this interference establishes spurious correlations between forgery cues and environmental patterns that severely limit generalization. To address this fundamental challenge, we propose an innovative Environment-Invariant Subspace Learning (EISL) framework. The core contribution of EISL is that it aims to disentangle features into orthogonal forgery-relevant invariant factors and environment-related residual factors via a learnable low-rank projection. To facilitate robust feature disentanglement, we also design an Environmental Intervention module that generates diverse and challenging intervention pairs, simulating out-of-distribution environmental shifts to guide the model toward discovering truly invariant forgery representations. Experiments across cross-
dataset, cross-generator, whole-face synthesis, and corruption settings show consistent gains and competitive or leading performance against strong detectors, demonstrating improved robustness to unseen forgery types and environmental variations. This work provides a new perspective and a valuable exploration for understanding and tackling the generalization barriers of VFMs in deepfake detection.
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All authors Shenghao Chen, Hao Jia, Chen Li, Chunjie Ma, Zan Gao, Shengyong Chen
Computer Vision
Magnitude-Direction Decoupling for Fast Video Generation with Flow Matching Models
Haonan Xu, Feiyang Chen, Songkui Chen et al. (+5 more)
Flow matching models for
video generation achieve impressive performance but suffer from high computational overhead due to iterative denoising. In fact, the original model is not necessary for all denoising steps, allowing some steps to use lightweight alternatives for faster sampling. However, directly using caching or lightweight models can deviate from the original denoising trajectory, resulting in suboptimal performance. Through empirical analysis, we find that lightweight models can robustly capture the magnitude components of the original model's output, while caching provides reliable directional guidance. Building on this insight, we propose the Magnitude-Direction Decoupling (MDD) method, which adaptively employs a direction-calibrated lightweight model as a substitute for the original model to accelerate inference and effectively correct deviations in the denoising trajectory. Moreover, MDD further reduces inference costs by reusing magnitude information under classifier-free guidance (CFG). As a result, MDD offers a more reliable and lightweight solution to accelerate sampling. Experiments show that MDD outperforms existing acceleration methods, delivering promising speedups (e.g., up to 2.95x on Wan2.1) while preserving high visual fidelity and content richness.
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All authors Haonan Xu, Feiyang Chen, Songkui Chen, Hongpeng Pan, Zhefeng Wang, Xinyu Duan, Baoxing Huai, Yang Yang
Computer Vision
Differentiable Voronoi Ray Tracing Beyond Rasterization Speeds
Bernardo Taveira, Carl Lindström, Joakim Johnander et al. (+1 more)
Real-time novel view synthesis is dominated by rasterized explicit primitives. These projection-based pipelines provide high throughput but require specialized extensions for non-pinhole effects such as distortion, rolling shutter, and depth of field. Ray-based rendering expresses these effects naturally but is generally assumed too slow for competitive real-time rendering. We analyze the factors governing throughput in differentiable Voronoi ray tracing and identify traversal length, per-
cell work, and
memory locality as principal determinants. Guided by this, we introduce VoroTracing, which co-designs the scene representation, optimization, and GPU execution to reduce these costs. Compact octahedral appearance textures reduce
memory traffic, while surface-concentrated opacity promotes early termination. The fixed-budget representation is optimized without
pruning or densification and rendered with a GPU implementation designed for coherent traversal. On Mip-NeRF 360, VoroTracing renders at 623 FPS on an RTX 5090, providing $3.2\times$ the throughput of the fastest prior ray-based method and $2.8\times$ that of 3D Gaussian Splatting, while maintaining competitive reconstruction quality. Our renderer supports fisheye, rolling-shutter, motion-blur, and depth-of-field effects through ray generation and sampling, requiring no specialized rasterization. These results show that real-time throughput can be achieved with the flexibility of ray-based rendering. We release our source code, see https://research.zenseact.com/publications/vorotracing
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All authors Bernardo Taveira, Carl Lindström, Joakim Johnander, Fredrik Kahl
Computer Vision
Is Haar Enough? Exploring Symlets and Coiflets for Wavelet Convolution Layers
Md Rifat Ur Rahman
Wavelet convolution layers have recently emerged as an efficient mechanism for enlarging receptive fields through multiresolution analysis, but prior work has fixed the wavelet basis to Haar or Daubechies at a chosen decomposition depth, leaving open whether a different basis can shift the underlying efficiency frontier. We identify and characterize a previously unexplored trade-off in this setting: bases with stronger approximation properties (longer filters) can reduce the decomposition depth required for competitive accuracy, yielding a net reduction in parameters and FLOPs despite higher perlevel transform cost. We formalize this as an F-vs.-L tradeoff (filter length vs. decomposition levels) and study it systematically across Haar, Daubechies, Symlets, and Coiflets under controlled architectures and budgets. On image classification (CIFAR-10, ImageNet-1K) and semantic
segmentation (Cityscapes), Coiflet-based wavelet convolutions match Haar at deeper levels with approximately 32% fewer additional parameters and 33% fewer additional FLOPs, providing a concrete and actionable design choice for practitioners building wavelet-based architectures.
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Computer Vision
Denoised Variance-Based Pruning with Optimal Brain Bias Compensation
Geon Tack Lee, Jaegul Choo and Kang Eun Jeon
Vision Transformers (ViTs) achieve state-of-the-art performance but carry massive computational overhead that restricts edge deployment. Although structural
pruning has emerged as a key strategy to reduce these costs, existing methods often suffer from severe accuracy degradation or require expensive retraining. Recently, Variance-Based
Pruning (VBP) introduced a promising paradigm by selecting
neurons based on activation variance; however, it remains limited by statistical noise in finite-sample activation covariance and reliance on bias-only updates that cannot fully account for structural reconstruction error. To address these limitations, we introduce Denoised Variance-Based
Pruning with Optimal
Brain Bias Compensation (DVBP + OB$^2$C). We leverage random
matrix theory to filter noise from the activation covariance spectrum for robust
neuron selection and mathematically prove that integrating mean-shift compensation into the Optimal
Brain Compression objective reduces the layer-wise Hessian exactly to the activation covariance
matrix. This enables an optimal, closed-form update of the remaining weights using the same statistics gathered for selection. Extensive experiments on DeiT, Swin, and ConvNeXt architectures demonstrate that DVBP + OB$^2$C achieves state-of-the-art training-free performance; at 50% MLP
pruning, it retains over 90% of the original Top-1 accuracy on Small and Base variants, outperforming VBP by up to 29.46% (ConvNeXt-T) and 7.33% (Swin-S). The code is available at: https://github.com/geontackee/DVBP_OB2C.
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Computer Vision
MaLViL: Multi-axis Low-rank Vision-LSTM for Medical Image Segmentation
Afshin Bozorgpour, Sina Ghorbani Kolahi, Moein Heidari et al. (+2 more)
Vision-LSTM (ViL) enables efficient global modeling, but its cost still scales with the number of spatial
tokens, so existing segmenters confine ViL to a coarse bottleneck and lose fine anatomical detail. Rasterizing 2D features into a 1D sequence further breaks adjacency across the orthogonal scan axis. We propose MaLViL, a Multi-axis Low-rank
Vision-LSTM network that extends ViL across decoder resolutions. Bidirectional low-rank ViL (Bi-LRViL) reasons on a compact orthonormal subspace and preserves detail through an orthogonal residual; scale-aware SaLViL restores cross-axis neighbors before serialization; and a Cross-Directional Mixer (CDM) fuses orthogonal horizontal and vertical traversal paths. Statistics-Guided Skip Modulation (SGSM) further retains boundary cues in encoder skips. On skin-lesion, ultrasound, and multi-organ CT
benchmarks, MaLViL achieves competitive or state-of-the-art
segmentation accuracy, while reducing ViL operator
memory by up to $83\times$ at fine decoder resolutions. Code is available at: https://github.com/xmindflow/malvil.
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All authors Afshin Bozorgpour, Sina Ghorbani Kolahi, Moein Heidari, Ilker Hacihaliloglu, Dorit Merhof
Computer Vision
RetiWave-Mamba: A Dual-Stream Network for Retinal Disease Detection based on Multi-scale Context and Feature-Adaptive Mamba Projection
Cheng Cheng and Jin Hong
Retinal diseases are a leading cause of irreversible
vision impairment, making early and accurate diagnosis essential for effective treatment. Optical Coherence
Tomography (OCT) serves as a critical imaging modality for this purpose, yet its automated analysis is hindered by inherent speckle noise, varying lesion scales, and subtle inter-class similarities. To address these challenges, we propose a novel framework, RetiWave-Mamba, which integrates spatial-frequency domain learning with state-of-the-art state space models. The framework utilizes Discrete Wavelet Transform (DWT) to decompose OCT images into low- and high-frequency streams, enabling decoupled processing of structural context and fine-grained details. For the low-frequency branch, we design a Multi-scale Contextual Localization Module (MCLM), which synergizes multi-scale dilation with spatial
attention to expand the global receptive field and precisely localize lesion regions. For the high-frequency branch, we introduce an
Attention-Guided High-Resolution Network (AG-HRNet) equipped with an intelligent gating mechanism to suppress noise propagation during multi-scale interactions. Furthermore, a Feature-Adaptive Mamba Projector (FAMP) is incorporated to form complementary channel-wise feature paths and adaptively reweight them using Mamba-generated gates. Extensive experiments on the OCT-C8
dataset demonstrate that our approach achieves a state-of-the-art (SOTA) classification accuracy of 98.38, surpassing existing methods. These results highlight the effectiveness of RetiWave-Mamba in identifying retinal pathologies and support its potential for computer-aided OCT image analysis.
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Computer Vision
PathoArgus: Advancing Evidence-Grounded Long-Context Visual Reasoning across Gigapixel Whole-Slide and Multi-Slide Case Contexts
Bowen Liu, Qixiang Zhang and Xiaomeng Li
Whole-slide pathology
reasoning requires models to integrate gigapixel-scale visual evidence across complete case-linked slides, yet current question-answering
benchmarks primarily measure final answer accuracy–a metric vulnerable to linguistic priors and
benchmark regularities, and insufficient to establish that predictions are grounded in the supplied tissue. We introduce PathoArgus-Bench, a
benchmark and evaluation protocol that explicitly tests the full evidence chain: availability, accessibility, use, and responsiveness. PathoArgus-Bench comprises 22,078 four-choice questions from 4,913 patients across 15 TCGA projects, covering six pathology capabilities across three levels of evidence demand, and operates under a fixed reader budget that retains only a small fraction of the gigapixel context. To further isolate evidence-grounded
reasoning, we contribute ESG (Evidence State Quartets), a controlled set of 483 quartets where the question text is fixed while the target WSI set is moved, replaced, or removed, requiring consistent predictions across all states. Evaluating 20 general-purpose, medical, and pathology-specific systems reveals a stark gap: while GPT-5.6 achieves 57.09% overall accuracy and 57.04% on ESG, it correctly completes only 19 of 483 quartets (3.93% QExact), exposing that row-level accuracy does not translate into reliable evidence grounding. We also introduce PathoArgus, a fixed-budget reader that allocates context via question relevance and spatial coverage, attaining 50.39% overall accuracy yet only 1.86% QExact–demonstrating that improved context access alone does not ensure consistent evidence-based prediction. Our
benchmark and diagnostics establish that acquiring useful whole-slide context is necessary but far from sufficient, and call for a shift from answer-centric to evidence-grounded evaluation in computational pathology.
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