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Search papers from 30+ top conferences, including NeurIPS, ICLR, ICML, CVPR, ACL and AAAI. Filter by keyword, then open any paper for its abstract, authors and links.

ECCV 2026

LlamaSeg: Image Segmentation via Autoregressive Mask Generation

Jiru Deng, Tengjin Weng, Tianyu Yang, Wenhan Luo, Zhiheng Li, Wenhao Jiang

We present LlamaSeg, a visual autoregressive framework that unifies multiple image segmentation tasks via natural language instructions. By reformulating segmentation as visual generation, LlamaSeg encodes masks as visual tokens and uses a LLaMA-style Transformer for direct next-token prediction, naturally fitting segmentation into autoregressive architectures. To support large-scale training, we introduce a data annotation pipeline and construct the SA-OVRS dataset, which contains 2M segmentation masks annotated with over 5,800 open vocabulary labels or diverse textual descriptions, spanning diverse realworld scenarios. This enables our model to localize objects in images based on text prompts and to generate fine-grained masks. We further introduce the composite metric average Hausdorff Distance (d ) AHD to evaluate mask contour fidelity for generative models better. Experiments show that LlamaSeg consistently outperforms existing generative approaches on multiple segmentation benchmarks and delivers finer, more accurate segmentation masks. Code and dataset are available at https://github.com/GML-FMGroup/llamaseg.

ECCV 2026

GUIDE: Resolving Domain Bias in GUI Agents through Real-Time Web Video Retrieval and Plug-and-Play Annotation

Rui Xie, Zhi Gao, Chenrui Shi, Zirui Shang, Lu Chen, Qing Li

Large vision-language models have endowed GUI agents with strong general capabilities for interface understanding and interaction. However, due to insufficient exposure to domain-specific software operation data during training, these agents exhibit significant domain bias— they lack familiarity with the specific operation workflows (planning) and UI element layouts (grounding) of particular applications, limiting their real-world task performance. In this paper, we present GUIDE (GUI Unbiasing via Instructional-Video Driven Expertise), a trainingfree, plug-and-play framework that resolves GUI agent domain bias by autonomously acquiring domain-specific expertise from web tutorial videos through a retrieval-augmented automated annotation pipeline. Guide introduces two key innovations. First, a subtitle-driven Video-RAG pipeline unlocks video semantics through subtitle analysis, performing progressive three-stage retrieval—domain classification, topic extraction, and relevance matching—to identify task-relevant tutorial videos. Second, a fully automated VLM pairwise video annotation pipeline feeds consecutive keyframes enhanced with UI element detection into VLMs, inferring the required planning and grounding knowledge that are injected into the agent’s corresponding modules to address both manifestations of domain bias. Extensive experiments on OSWorld demonstrate Guide’s generality as a plug-and-play component for both multi-agent systems and single-model agents. It yields +4.47–+7.48 percentage-point improvements and reduces execution steps—without modifying any model parameters or architecture—validating Guide as an architecture-agnostic enhancement to bridge GUI agent domain bias. Code, dataset, and additional results are available on our project page: https://sharryXR. github.io/GUIDE/.

ECCV 2026

Combating Textual Noise and Redundancy: Entropy-Aware Dense Visual Token Pruning

Xuehui Wang, Xuankun Yang, Wei Shen

Visual token pruning is a crucial strategy for accelerating VLMs by compressing redundant image patches, yet existing methods often fail to preserve critical cues under dense instructions and fine-grained queries. In this paper, we investigate this failure and identify two underlying bottlenecks: the widespread dispersion of textual noise that corrupts dense cross-modal scoring, and the feature fragmentation inherent to standard token selection. To address these issues, we propose Entropy-Aware Dense Pruning (EADP), a framework that reformulates pruning as a structured compression problem. EADP first leverages statistical entropy to quantify and filter out textual noise, yielding a robust, fine-grained instruction relevance score. Subsequently, instead of naive Top-K selection, EADP casts token selection as a submodular maximization problem with a spatial prior, explicitly ensuring a holistic and non-redundant visual representation. Extensive experiments demonstrate that EADP improves the accuracy-efficiency trade-off of VLMs, robustly preserving fine-grained visual cues under strict token budgets while achieving SoTA performance on challenging multimodal benchmarks.

ECCV 2026

Zoom-IQA: Image Quality Assessment with Reliable Region-Aware Reasoning

Guoqiang Liang, Jianyi Wang, Zhonghua Wu, Shangchen Zhou, Chen Change Loy

Image Quality Assessment (IQA) is a long-standing problem in computer vision. Previous methods typically focus on predicting numerical scores without explanation or provide low-level descriptions lacking precise scores. Recent reasoning-based vision language models (VLMs) have shown strong potential for IQA, enabling joint generation of quality descriptions and scores. However, we notice that existing VLM-based IQA methods tend to exhibit unreliable reasoning due to their limited capability of integrating visual and textual cues. In this work, we introduce Zoom-IQA, a VLM-based IQA model to explicitly emulate key cognitive behaviors: uncertainty awareness, region reasoning, and iterative refinement. Specifically, we present a two-stage training pipeline: 1) supervised fine-tuning (SFT) on our Grounded-RationaleIQA (GR-IQA) dataset to teach the model to ground its assessments in key regions; and 2) reinforcement learning (RL) for dynamic policy exploration, primarily stabilized by our KL-Coverage regularizer to prevent reasoning and scoring diversity collapse, and supported by a Progressive Re-sampling Strategy to mitigate annotation bias. Extensive experiments show that Zoom-IQA achieves improved robustness, explainability, and generalization. The application to downstream tasks, such as image restoration, further demonstrates the effectiveness of Zoom-IQA.

ECCV 2026

h-Flow: Flexible Flow-based Image Editing via Doob's h-Transform

Zehui Guo, Zhen Wang, Junwei Shu, Changbo Wang, Long Chen, Yang Li

Editing images with pre-trained text-to-image flow models typically requires carefully balancing target alignment with the desired prompt and source consistency with the original image. Existing approaches either rely on inversion-based pipelines or heuristic sourceto-target trajectory constructions, which often depend on architecturespecific designs or are sensitive to hyperparameters. In this paper, we propose h-flow, a training-free and theoretically grounded flow-based editing framework. Inspired by Doob’s h-Transform, we reformulate image editing as conditional generation under multiple terminal events corresponding to source consistency and target alignment. We first extend the classical h-Transform from SDE-based models to the deterministic RF framework by constructing an equivalent SDE with identical marginals. Within this formulation, we design dedicated h-functions for source consistency and target alignment, yielding closed-form reconstruction guidance and velocity-based semantic editing signals. We further introduce a velocity orthogonal decomposition to decouple reconstruction and editing directions, enabling a controllable trade-off between the two objectives. Extensive experiments demonstrate that h-flow achieves effective, robust, and flexible editing across diverse scenarios.

ECCV 2026

LoMa: Local Feature Matching Revisited

David Nordström, Johan Edstedt, Georg Bökman*, Jonathan Astermark, Anders Heyden, Viktor Larsson, Mårten Wadenbäck, Michael Felsberg, Fredrik Kahl

Local feature matching has long been a fundamental component of 3D vision systems such as Structure-from-Motion (SfM), yet progress has lagged behind the rapid advances of modern data-driven approaches. The newer approaches, such as feed-forward reconstruction models, have benefited extensively from scaling dataset sizes, whereas local feature matching models are still only trained on a few mid-sized datasets. In this paper, we revisit local feature matching from a datadriven perspective. In our approach, which we call LoMa, we combine large and diverse data mixtures, modern training recipes, scaled model capacity, and scaled compute, resulting in remarkable gains in performance. Since current standard benchmarks mainly rely on collecting sparse views from successful 3D reconstructions, the evaluation of progress in feature matching has been limited to relatively easy image pairs. To address the resulting saturation of benchmarks, we collect 1000 highly challenging image pairs from internet data into a new dataset called HardMatch. Ground truth correspondences for HardMatch are obtained via manual annotation by the authors. In our extensive benchmarking suite, we find that LoMa makes outstanding progress across the board, outperforming the state-of-the-art method ALIKED+LightGlue by +18.6 mAA on HardMatch, +29.5 mAA on WxBS, +21.4 (1m, 10→) on InLoc, +24.2 AUC on RUBIK, and +12.4 mAA on IMC 2022. We release our code and models publicly at https://github.com/davnords/LoMa.

ECCV 2026

Unified Removal of Raindrops and Reflections: A New Benchmark and A Novel Pipeline

Xingyu Liu, Zewei He, Yu Chen, Chunyu Zhu, Zixuan Chen, Xing Luo, Zheming Lu

When capturing images through glass surfaces or windshields on rainy days, raindrops and reflections frequently co-occur to significantly reduce the visibility of captured images. This practical problem lacks attention and needs to be resolved urgently. Prior de-raindrop, dereflection, and all-in-one models have failed to address this composite degradation. To this end, we first formally define the unified removal of raindrops and reflections (UR3) task for the first time and construct a real-shot dataset, namely RainDrop and ReFlection (RDRF), which provides a new benchmark with substantial, high-quality, diverse image pairs. Then, we propose an effective diffusion-based framework (i.e., DiffUR3) with several target designs to address this challenging task. By leveraging the powerful generative prior, DiffUR3 successfully removes both types of degradations. Extensive experiments demonstrate that our method achieves state-of-the-art performance on our benchmark and on challenging in-the-wild images.

ECCV 2026

DiNBV-Grasp: Real-Time Distance-Aware Two-Stage Next-Best-View for Robotic Grasping

Zilong Xie, Jingyu Gong, Xin Tan, Zhizhong Zhang, Yanyun Qu, Lizhuang Ma, Yuan Xie

Next-Best-View (NBV) planning is widely adopted for robotic grasping in cluttered and occluded environments. However, existing methods typically assume that a single observation at an arbitrary camera–object distance is sufficient for accurate grasp prediction. This overlooks a critical factor: grasp perception quality is highly sensitive to viewing distance, and the optimal distance varies significantly across object categories and scales. To address this limitation, we propose DiNBVGrasp, a real-time, distance-aware two-stage NBV framework. In Stage I, we distill a large vision-language model (VLM) into a lightweight planner for global semantic NBV exploration, resolving occlusions and generating an initial grasp hypothesis with low-latency inference. In Stage II, we introduce a local distance-aware refinement mechanism that explicitly models the relationship between viewing distance and grasp success probability. During manipulator approach, a lightweight scoring network evaluates intermediate observations and selects the most informative one for final grasp prediction. Extensive simulation and real-world experiments demonstrate that DiNBV-Grasp consistently improves grasp success rates across diverse objects, particularly for small and occluded targets, while preserving real-time performance, highlighting the importance of distance-aware perception in NBV-based grasping. Our code is available at https://github.com/XieZilongAI/DiNBV-Grasp.

ECCV 2026

Q-BridgeNet: A Quantization Network for Cross-Lingual Sign Language Translation

Liqian Feng, Lintao Wang, Xiaochen Liu, Anusha Withana, Ken-Tye Yong, Dehui Kong, Zhiyong Wang, Kun Hu

Most sign language translation (SLT) methods focus on isolated native sign–spoken pairs (e.g., American Sign Language–English). Extending language-specific SLT models to multilingual translation would improve accessibility by enabling communication across diverse sign and spoken language communities. However, existing multilingual SLT approaches still struggle to learn a unified model that minimizes crosslingual conflicts while capturing shared cross-lingual semantics and preserving language-specific variations across different sign languages. Therefore, we propose Q-BridgeNet, a unified framework for multilingual SLT that jointly mitigates cross-lingual conflicts across both the sign language and spoken language sides. On the sign language side, QBridgeNet learns discrete Q-units via adaptive segmentation and residual vector quantization: a shared base codebook provides language-agnostic semantic primitives, while language-specific residual codebooks refine heterogeneous signing semantics. On the spoken language side, a multilingual LLM is fine-tuned to operate in the Q-unit space, leveraging cross-lingual priors to enable a unified SLT model. Experiments on PHOENIX14T, How2Sign, and CSL-Daily show that Q-BridgeNet effectively mitigates cross-lingual conflicts, achieving state-of-the-art performance on native sign–spoken pairs while also demonstrating strong generalization to non-native pairs. Our source code is publicly available at: https://github.com/FengLiQ/Q-BridgeNet

ECCV 2026

PriorPose: Reference-Guided Joint Deformation and Alignment for Category-Level Object Pose Estimation

Yihan Chen, Huan Ren, Wenfei Yang, Hang Du, Tianzhu Zhang, Feng Wu

Category-level object pose estimation seeks to recover a similarity transform (R, t, s) for unseen instances without instance-specific CAD models. Most competitive methods are correspondence-based: prior-free variants regress canonical (NOCS) coordinates directly from local observations and implicitly memorize the canonical frame in the weights, which ties the parameters to category-typical orientations and hurts generalization under distribution shift; prior-based variants introduce a category prior but typically follow a serial deform-then-align pipeline, where underconstrained canonical completion can corrupt correspondences and induce error cascades in pose. We propose PriorPose, a reference-guided correspondence framework that keeps the category prior explicit and solves canonicalization and alignment jointly in a shared feature space. A reference-guided seeded transformer embeds the partial observation and the category prior as token sets and fuses them via geometry-aware seeds, from which the network jointly predicts a per-point NOCS field for visible points and a canonical deformation of the prior that reconstructs a full canonical instance, while a deep pose head regresses (R, t, s) from the induced correspondences. A two-part shape consistency objective, with canonical-space and camera-space consistency losses, couples correspondence, deformation, and pose, reducing reliance on memorized canonical orientations and avoiding deform-then-align error cascades. Experiments on standard and larger-category benchmarks demonstrate that PriorPose sets new state-of-the-art results on most evaluated metrics, especially under strict pose thresholds, while remaining competitive on relaxed pose and IoU metrics and showing improved robustness under shape variation and domain shift.

ECCV 2026

LUA: Latent Upscaling Adapter for Diffusion-Based Image Synthesis

Aleksandr Razin, Kazantsev Danil, Ilya Makarov

Generating high-resolution images with latent diffusion models is limited by the cost of high-resolution denoising, while post-hoc super-resolution introduces artifacts and additional latency by operating after decoding. We present the Latent Upscaling Adapter (LUA), a lightweight module that performs super-resolution directly on the generator’s latent code before the final VAE decoding step. LUA integrates as a drop-in component requiring no modifications to the base model or additional diffusion stages, enabling high-resolution synthesis through a single feed-forward pass in latent space. A shared Swin-style backbone with scale-specific pixel-shuffle heads supports ×2 and ×4 factors with nearly 3× lower decoding and upscaling time. Because LUA operates directly on latents, it transfers across diffusion systems–including SDXL, SD3, and FLUX–through architecture-level reuse, adapting only the input convolution with brief fine-tuning rather than retraining from scratch. Extensive experiments demonstrate that LUA closely matches the fidelity of native high-resolution generation at 2K and 4K while offering a practical and efficient path to scalable image synthesis.

ECCV 2026

Multi-Anchor Distillation with Text-Guided Analytic Classifier for Continual Learning

Qier Meng, De Cheng, Jiahao Li, Cheng Deng

Continual learning suffers from two forms of catastrophic forgetting: encoder drift, where visual representations shift as the model adapts to new tasks, and classifier overwriting, where gradient-based updates distort decision boundaries for previously learned classes. We present a unified exemplar-free framework that tackles both effects at the encoder and classifier levels. For the encoder, multi-anchor distillation maintains consistency with multiple historical encoder states and effectively bounds cumulative feature drift along the task sequence. For the classifier, a text-guided analytic classifier computes weights in closed form from incrementally accumulated statistics, completely bypassing gradient-based optimization. To further enhance the classifier, we incorporate a text prior that anchors classifier weights near CLIP’s text embeddings to inject cross-modal semantic knowledge, and propose text subspace regularization that encourages the classifier to learn directions orthogonal to the text embedding subspace, capturing discriminative visual patterns that text semantics alone cannot represent. Experiments on CIFAR-100, ImageNet-100, ImageNet-R, and CUB-200 show state-ofthe-art performance, including gains of +2.7% on CIFAR-100 and +3.5% on ImageNet-100 over prior methods. The code will be included in the supplementary material.

ECCV 2026

ORACLE-3D: Open-world Region-aligned Cross-modal Learning for Label-efficient 3D Scene Understanding

Yuru wang, Pei Liu, Songtao Wang, Zehan Zhang, Xinyan Lu, Changwei Cai, Hao Li, Haipeng Liu, Qingtian Ning, Jun Ma

The recent surge in vision-language models (VLMs) has revolutionized open-world perception, yet transferring this success to 3D scene understanding remains impeded by the scarcity of large-scale point-text pairs and the computational overhead of existing frozen-VLM approaches. To bridge this gap, we present ORACLE-3D (Open-world Region-Aligned Cross-modal LEarning for Label-efficient 3D Scene Understanding), a unified framework that effectively co-embeds point clouds, images, and text into a shared latent space without relying on labor-intensive 3Dtext pair construction. ORACLE-3D leverages the image modality as a semantic bridge, employing a novel cross-modal distillation strategy that comprises logit distillation and feature distillation. These components transfer the rich, open-vocabulary capabilities of pre-aligned 2D VLMs directly to the 3D domain. Furthermore, to mitigate the semantic noise arising from inevitable calibration errors and motion artifacts during 3D-2D projection, we propose a vision-point matching module that dynamically rectifies point-pixel misalignments. To ensure robust convergence amidst the gradient bias inherent in heterogeneous multimodal learning, we introduce a two-stage optimization strategy coupled with four task-specific weighted losses. Extensive evaluations on the nuScenes, Waymo, SemanticKITTI, and ScanNet datasets demonstrate the superiority of our paradigm. Specifically, ORACLE-3D outperforms state-of-the-art methods by an average of 11.6 and 14.8 in mIoU on base-annotated and annotation-free tasks, respectively. Project Page: https://ocean-luna.github.io/ORACLE.github.io/.

ECCV 2026

Disentangling Hallucinations: Orthogonal Semantic Projection for Robust Interpretability

Emirhan Bilgiç, Baptiste Caramiaux, Zhi Yan, Gianni Franchi

As Vision-Language Models are increasingly deployed in safetycritical applications, the trustworthiness of their explanations becomes crucial. Explainable AI (XAI) methods for Vision-Language Models often suffer from explanation hallucination, where attribution maps highlight prominent image regions even when prompted with incorrect text descriptions (e.g., highlighting a dog when prompted “cat”). Although this problem is widespread, a formal mathematical analysis of XAI methods and CLIP embeddings is largely missing in the literature. We demonstrate that this phenomenon is not specific to a single architecture but is a fundamental consequence of Linear Semantic Leakage in high-dimensional embedding spaces. We propose a unified theoretical framework, Linear Semantic Attribution (LSA), which generalizes across discriminative methods. We introduce Orthogonal Semantic Projection (OSP), a geometric intervention that utilizes the residual property of OMP to disentangle unique semantic signals from shared concepts. We prove theoretically and demonstrate empirically that OSP minimizes hallucination by orthogonalizing the query vector against distractor concepts, rendering the attribution model blind to shared features while preserving fidelity for correct prompts. The project website, including the code and animations, is available at: https://emirhanbilgic.github.io/OrthogonalSemantic-Projection

ECCV 2026

3D Scene-Adaptive Trajectory-Controllable Human Image Animation with Camera Movement

Deyin Liu, Jicheng Xu, Lin Yuanbo Wu, Xiaowei Zhao, Xiatian Zhu, Anjan Dutta, ZHE JIN

Human image animation, which aims to generate a video of a reference subject following a provided action sequence, has received increasing research interest. With the development of diffusion-based/flowbased video foundation models, existing animation works have began to upgrade the guidance information from 2D skeleton/pose to 3D modeling conditions. Despite achieving reasonable results, these approaches face challenges in synthesizing trajectory-controllable human motion within natural scene under changed camera views. In this work, we present a scene-adaptive human image animation framework that controls both human motion and camera trajectories within a reconstructed 3D environment for video generation. To achieve this, we first develop a groundadaptive 3D motion retargeting approach to enable user-friendly motion trajectory control adapting to the changes of elevations of ground and orientations automatically. Then we design a viewpoint-adaptive latent fusion mechanism to inject point-cloud geometric priors through scenevisibility masking into the generative process, providing precise guidance of viewpoint changes under camera control. Experiments on two standard human image animation benchmark datasets demonstrate remarkable improvements of our method over the state of the arts in related video generation metics. Project page: https://robinhood256100.github.io/webdisp

ECCV 2026

GeoMix: Descriptor-Free Visual Localization via Global Context and Multi-Detector Training

Yejun Zhang, Xinjue Wang, Zihan Wang, Esa Rahtu, Juho Kannala

Descriptor-free visual localization eliminates high-dimensional descriptor storage, preserves scene privacy, and simplifies map maintenance, yet its accuracy still lags far behind descriptor-based pipelines. We identify this gap to insufficient geometric discriminability in geometryonly matching. Without visual appearance, current methods underutilize local geometry cues, lack the global context among keypoints, and overfit to a single keypoint detector. We further observe that descriptorfree matching naturally enables multi-detector training, as heterogeneous keypoints can be optimized in a shared geometry-only space without aligning descriptor spaces. Building on these insights, we propose GeoMix, a descriptor-free 2D-3D matching framework that strengthens geometric discriminability at three levels. Locally, directional and distance-aware embeddings enrich neighborhood aggregation with finegrained spatial structure. Globally, learnable context nodes aggregate and redistribute scene-wide information via cross-attention to resolve ambiguities beyond local receptive fields. At the training level, MixTraining exploits this detector-agnostic geometry space to learn representations across multiple keypoint detectors. Extensive experiments on MegaDepth, Cambridge Landmarks, 7Scenes, and Aachen Day-Night show that GeoMix sets a new state of the art among descriptor-free methods, reducing 75th-percentile rotation error by 89% and translation error by up to 90% over the previous best, while generalizing zero-shot to unseen detectors and narrowing the gap to descriptor-based pipelines. Code is available at https://github.com/YejunZhang/Geomix.

ECCV 2026

Dense Dynamic Scene Reconstruction and Camera Pose Estimation from Multi-View Videos

Shuo Sun, Unal Artan, Malcolm Mielle, Achim Lilienthal, Martin Magnusson

We address the challenging problem of dense dynamic scenereconstruction and camera pose estimation from multiple freely movingcameras—a setting that arises naturally when multiple observers capturea shared event. Prior approaches either handle only single-camera input orrequire rigidly mounted, pre-calibrated camera rigs, limiting their practicalapplicability. We propose a two-stage optimization framework that decou-ples the task into robust camera tracking and dense depth refinement. Inthe first stage, we extend single-camera visual SLAM to the multi-camerasetting by constructing a spatiotemporal connection graph that exploitsboth intra-camera temporal continuity and inter-camera spatial overlap,enabling consistent scale and robust tracking. To ensure robustness underlimited overlap, we introduce a wide-baseline initialization strategy usingfeed-forward reconstruction models. In the second stage, we refine depthand camera poses by optimizing dense inter- and intra-camera consistencyusing wide-baseline optical flow. Additionally, we introduce MultiCam-Robolab, a new real-world dataset with ground-truth poses from a motioncapture system. Finally, we demonstrate that our method significantly out-performs state-of-the-art feed-forward models on both synthetic and real-world benchmarks, while requiring less memory. Our code is released at

ECCV 2026

Zero-Shot Inference-Time Rectification for Real-World Arbitrary-Scale Super-Resolution

Yifan Zuo, tianlin zhu, zhenlong xia, Jiebin Yan, Xiaoshui Huang, Sanqian Li, Yuming Fang, Qiang Wu

Arbitrary-scale single image super-resolution (ASSR) has achieved tremendous success under synthetic degradations, but still faces performance deterioration in real-world scenarios where complex distortions are highly entangled with continuous zooming scales. While constructing paired real-world datasets with fractional scales attempts to alleviate this gap, such physical data is fundamentally constrained by discrete scale coverage and hardware-related biases. To overcome these limitations, we propose the Continuous Degradation Rectifier (CDR), a plug-and-play generative proxy that achieves zero-shot domain adaptation at inference time without requiring any real-world paired training data. Designed for any off-the-shelf ASSR model pre-trained on synthetic data, CDR leverages a conditional diffusion process to dynamically project scale-entangled real-world inputs into a canonical bicubic latent space, rectifying the continuous degradation manifold. Specifically, CDR achieves this through a progressive orthogonal decoupling mechanism: it first purifies scale-invariant content from real-world low-resolution images via a discrete codebook bottleneck, and subsequently extracts scaleindependent degradation and continuous scale modulation from unpaired bicubically downsampled reference image, which are orthogonally trained by scale-equivariant contrastive learning. Finally, these orthogonal representations are seamlessly integrated via scale-adaptive bandwidth modulation to guide the reverse diffusion process. Extensive experiments on real-world benchmarks demonstrate that equipping frozen, off-the-shelf ASSR models with CDR not only bypasses the need for domain-specific retraining but also establishes a new state-of-the-art.

ECCV 2026

SCDL: Synergistic Confidence-Dispersion Learning for Semi-Supervised Video Polyp Segmentation

Yuanqin He, Yuhua Zhang, Huisi Wu, Jing Qin

Theearlydetectionandpreventionofcolorectalcancer(CRC)ishighly dependentontheaccuratepolypsegmentationfromendoscopicvideos.However, existing video polyp segmentation (VPS) methods are largely hampered by the scarcity of large-scale, high-quality pixel-level annotations. While some semisupervised VPS models have been proposed, their performance is limited by the unreliability of pseudo-labels produced in the training process. In addition, inherent challenges of endoscopic videos, such as low contrast, significant inter-frame variations, and consecutive low-quality frames, further exacerbate the above issues. To address these shortcomings, we propose a unified semi-supervised video polyp segmentation framework, dubbed synergistic confidence–dispersion learning (SCDL), which aims to simultaneously improve pseudo-label reliability, reduce regional supervision bias, and alleviate ambiguities caused by low-contrast features. Our framework has three innovative components: (1) a new spectral convex optimization separation (SCOS) algorithm, which employs convex optimization in a confidence–dispersion space for robust pseudo-label selection, (2) contextual consistency perturbation (CCP), a novel augmentation strategy that reduces regional supervision bias by forcing semantic reconstruction from unreliable areas; and (3) a prototype-guided adaptive refinement (PAR) module that leveragesprototype-drivenaggregationandobject-awarefusiontosharpenfeature discrimination, suppress background clutter, and strengthen temporal coherence. Extensive experiments demonstrate that our model consistently achieves SOTA performance on the SUN-SEG dataset across all labeled-data ratios, and exhibits zero-shot generalization capability on the unseen CVC-ClinicDB dataset. Code are available at https://github.com/Yuanqin-He/SCDL.

ECCV 2026

DiffUE: Enhancing Utility-Unlearnability Trade-off of Unlearnable Examples via Diffusion Autoencoders

Syed Irfan Ali Meerza, Oktay Ozturk, Amir Sadovnik, Jian Liu

AI models are increasingly trained on personal images scraped from social media and public platforms, often without consent, leading to serious privacy violations, such as unauthorized facial recognition and targeted advertising. To counter this, researchers have developed unlearnable examples (UEs), images modified with imperceptible noise to prevent AI models from extracting meaningful information. However, existing UE methods primarily rely on pixel-space noise, which can be bypassed by relearning strategies such as adversarial training, image transformation, and compression. While some techniques improve robustness, they often come at the expense of significant degradation in image utility and perceptual quality. In this paper, we introduce DiffUE to overcome these limitations by injecting noise into the semantic space of images instead of the pixel space. Instead of corrupting pixel values, DiffUE modifies high-level semantic features of images, ensuring robust unlearnability while preserving visual quality and utility. By leveraging a diffusion-based autoencoder framework to manipulate semantic features, DiffUE generates purposeful, natural-looking modifications that effectively resist advanced relearning strategies. Extensive experiments on four datasets, CIFAR-10, CIFAR-100, CelebA-HQ, and ImageNet, as well as a subjective user study, demonstrate that DiffUE significantly enhances the trade-off between image quality and unlearnability, offering a more robust and effective solution for safeguarding personal data in an increasingly exploitative AI landscape.

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