Computer Vision
Multi-AD: Cross-Domain Unsupervised Anomaly Detection for Medical and Industrial Applications
Wahyu Rahmaniar and Kenji Suzuki
Traditional deep learning models often lack annotated data, especially in cross-domain applications such as anomaly detection, which is critical for early disease diagnosis in medicine and defect detection in industry. To address this challenge, we propose Multi-AD, a convolutional neural network (CNN) model for robust unsupervised anomaly detection across medical and industrial images. Our approach employs the squeeze-and-excitation (SE) block to enhance feature extraction via channel-wise
attention, enabling the model to focus on the most relevant features and detect subtle anomalies. Knowledge
distillation (KD) transfers informative features from the teacher to the student model, enabling effective learning of the differences between normal and anomalous data. Then, the discriminator network further enhances the model's capacity to distinguish between normal and anomalous data. At the inference stage, by integrating multi-scale features, the student model can detect anomalies of varying sizes. The teacher-student (T-S) architecture ensures consistent representation of high-dimensional features while adapting them to enhance anomaly detection. Multi-AD was evaluated on several medical
datasets, including
brain MRI, liver CT, and retina OCT, as well as industrial
datasets, such as MVTec AD, demonstrating strong generalization across multiple domains. Experimental results demonstrated that our approach consistently outperformed state-of-the-art models, achieving the best average AUROC for both image-level (81.4% for medical and 99.6% for industrial) and pixel-level (97.0% for medical and 98.4% for industrial) tasks, making it effective for real-world applications.
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Computer Vision
NeVStereo: A NeRF-Driven NVS-Stereo Architecture for High-Fidelity 3D Tasks
Pengcheng Chen, Yue Hu, Wenhao Li et al. (+5 more)
In modern dense 3D reconstruction, feed-forward systems (e.g., VGGT, pi3) focus on end-to-end matching and geometry prediction but do not explicitly output the novel view synthesis (NVS). Neural rendering-based approaches offer high-fidelity NVS and detailed geometry from posed images, yet they typically assume fixed camera poses and can be sensitive to pose errors. As a result, it remains non-trivial to obtain a single framework that can offer accurate poses, reliable depth, high-quality rendering, and accurate 3D surfaces from casually captured views. We present NeVStereo, a NeRF-driven NVS-stereo architecture that aims to jointly deliver camera poses, multi-view depth, novel view synthesis, and surface reconstruction from multi-view RGB-only inputs. NeVStereo combines NeRF-based NVS for stereo-friendly renderings, confidence-guided multi-view depth estimation, NeRF-coupled bundle adjustment for pose refinement, and an iterative refinement stage that updates both depth and the radiance field to improve
geometric consistency. This design mitigated the common NeRF-based issues such as surface stacking, artifacts, and pose-depth coupling. Across indoor, outdoor, tabletop, and aerial
benchmarks, our experiments indicate that NeVStereo achieves consistently strong zero-shot performance, with up to 36% lower depth error, 10.4% improved pose accuracy, 4.5% higher NVS fidelity, and state-of-the-art mesh quality (F1 91.93%, Chamfer 4.35 mm) compared to existing prestigious methods.
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All authors Pengcheng Chen, Yue Hu, Wenhao Li, Nicole M Gunderson, Andrew Feng, Zhenglong Sun, Peter Beerel, Eric J Seibel
Computer Vision
Disco: Densely-overlapping Cell Instance Segmentation via Adjacency-aware Collaborative Coloring
Rui Sun, Yiwen Yang, Kaiyu Guo et al. (+8 more)
Accurate
cell instance
segmentation is foundational for digital pathology analysis. Existing methods based on contour detection and distance mapping still face significant challenges in processing complex and dense cellular regions. Graph coloring-based methods provide a new paradigm for this task, yet the effectiveness of this paradigm in real-world scenarios with dense overlaps and complex
topologies has not been verified. Addressing this issue, we release a large-scale
dataset GBC-FS 2025, which contains highly complex and dense sub-cellular
nuclear arrangements. We conduct the first systematic analysis of the chromatic properties of
cell adjacency graphs across four diverse
datasets and reveal an important discovery: most real-world
cell graphs are non-bipartite, with a high prevalence of odd-length cycles (predominantly triangles). This makes simple 2-coloring theory insufficient for handling complex tissues, while higher-chromaticity models would cause representational redundancy and optimization difficulties. Building on this observation of complex real-world contexts, we propose Disco (Densely-overlapping
Cell Instance
Segmentation via Adjacency-aware COllaborative Coloring), an adjacency-aware framework based on the "divide and conquer" principle. It uniquely combines a data-driven topological labeling strategy with a constrained deep learning system to resolve complex adjacency conflicts. First, "Explicit Marking" strategy transforms the topological challenge into a learnable classification task by recursively decomposing the
cell graph and isolating a "conflict set." Second, "Implicit Disambiguation" mechanism resolves ambiguities in conflict regions by enforcing feature dissimilarity between different instances, enabling the model to learn separable feature representations.
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All authors Rui Sun, Yiwen Yang, Kaiyu Guo, Chen Jiang, Dongli Xu, Zhaonan Liu, Tan Pan, Limei Han, Xue Jiang, Wu Wei, Yuan Cheng
Computer Vision
VMF-GOS: Geometry-guided virtual Outlier Synthesis for Long-Tailed OOD Detection
Ningkang Peng, Qianfeng Yu, Yuhao Zhang et al. (+6 more)
Out-of-Distribution (OOD) detection under long-tailed distributions is a highly challenging task because the scarcity of samples in tail classes leads to blurred decision boundaries in the feature space. Current state-of-the-art (sota) methods typically employ Outlier Exposure (OE) strategies, relying on large-scale real external
datasets (such as 80 Million Tiny Images) to regularize the feature space. However, this dependence on external data often becomes infeasible in practical deployment due to high data acquisition costs and
privacy sensitivity. To this end, we propose a novel data-free framework aimed at completely eliminating reliance on external
datasets while maintaining superior detection performance. We introduce a Geometry-guided virtual Outlier Synthesis (GOS) strategy that models statistical properties using the von Mises-Fisher (vMF) distribution on a hypersphere. Specifically, we locate a low-likelihood annulus in the feature space and perform directional sampling of virtual outliers in this region. Simultaneously, we introduce a new Dual-Granularity Semantic Loss (DGS) that utilizes contrastive learning to maximize the distinction between in-distribution (ID) features and these synthesized boundary outliers. Extensive experiments on
benchmarks such as CIFAR-LT demonstrate that our method outperforms sota approaches that utilize external real images.
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All authors Ningkang Peng, Qianfeng Yu, Yuhao Zhang, Yafei Liu, Xiaoqian Peng, Peirong Ma, Yi Chen, Peiheng Li, Yanhui Gu
Computer Vision
TSBOW – Traffic Surveillance Benchmark for Occluded Vehicles Under Various Weather Conditions
Ngoc Doan-Minh Huynh, Duong Nguyen-Ngoc Tran, Long Hoang Pham et al. (+10 more)
Global warming has intensified the frequency and severity of extreme weather events, which degrade CCTV signal and
video quality while disrupting traffic flow, thereby increasing traffic accident rates. Existing
datasets, often limited to light haze, rain, and snow, fail to capture extreme weather conditions. To address this gap, this study introduces the Traffic Surveillance
Benchmark for Occluded vehicles under various Weather conditions (TSBOW), a comprehensive
dataset designed to enhance occluded vehicle detection across diverse annual weather scenarios. Comprising over 32 hours of real-world traffic data from densely populated urban areas, TSBOW includes more than 48,000 manually annotated and 3.2 million semi-labeled frames; bounding boxes spanning eight traffic participant classes from large vehicles to micromobility devices and pedestrians. We establish an object detection
benchmark for TSBOW, highlighting challenges posed by occlusions and adverse weather. With its varied road types, scales, and viewpoints, TSBOW serves as a critical resource for advancing Intelligent Transportation Systems. Our findings underscore the potential of CCTV-based traffic monitoring, pave the way for new research and applications. The TSBOW
dataset is publicly available at: https://github.com/SKKUAutoLab/TSBOW.
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All authors Ngoc Doan-Minh Huynh, Duong Nguyen-Ngoc Tran, Long Hoang Pham, Tai Huu-Phuong Tran, Hyung-Joon Jeon, Huy-Hung Nguyen, Duong Khac Vu, Hyung-Min Jeon, Son Hong Phan, Quoc Pham-Nam Ho, Chi Dai Tran, Trinh Le Ba Khanh, Jae Wook Jeon
Computer Vision
Explainable Pathomics Feature Visualization via Correlation-aware Conditional Feature Editing
Yuechen Yang, Junlin Guo, Ruining Deng et al. (+9 more)
Pathomics is a recent approach that offers rich quantitative features beyond what black-box deep learning can provide, supporting more reproducible and explainable biomarkers in digital pathology. However, many derived features (e.g., "second-order moment") remain difficult to interpret, especially across different clinical contexts, which limits their practical adoption. Conditional
diffusion models show promise for explainability through feature editing, but they typically assume feature independence**–**an assumption violated by intrinsically correlated pathomics features. Consequently, editing one feature while fixing others can push the model off the biological
manifold and produce unrealistic artifacts. To address this, we propose a
Manifold-Aware
Diffusion (MAD) framework for controllable and biologically plausible
cell nuclei editing. Unlike existing approaches, our method regularizes feature trajectories within a disentangled latent space learned by a variational auto-encoder (VAE). This ensures that manipulating a target feature automatically adjusts correlated attributes to remain within the learned distribution of real
cells. These optimized features then guide a conditional
diffusion model to synthesize high-fidelity images. Experiments demonstrate that our approach is able to navigate the
manifold of pathomics features when editing those features. The proposed method outperforms baseline methods in conditional feature editing while preserving structural coherence.
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All authors Yuechen Yang, Junlin Guo, Ruining Deng, Junchao Zhu, Zhengyi Lu, Chongyu Qu, Yanfan Zhu, Xingyi Guo, Yu Wang, Shilin Zhao, Haichun Yang, Yuankai Huo
Computer Vision
Efficient Dataset Distillation for Pre-Trained Self-Supervised Models via Statistical Flow Matching
Qianxin Xia, Jiawei Du, Yuhan Zhang et al. (+6 more)
Dataset distillation seeks to synthesize a compact surrogate
dataset that enables performance comparable to training on the original
dataset for downstream tasks. For the scenario where pre-trained self-supervised models serve as priors, traditional Linear Gradient Matching optimizes synthetic images by encouraging them to mimic the gradient updates induced by real images on the linear probe. However, this batch-level formulation requires loading thousands of real images and applying multiple differentiable augmentations to synthetic images at each
distillation step, leading to substantial computational and
memory overheads. In this paper, we revisit the linear gradient and theoretically derive that it is essentially a local relative distribution directed from target class centers toward non-target class centers, which we term flow. This property causes suboptimality and instability, often necessitating expensive multiple augmentations to compensate. To address this, we introduce Statistical Flow Matching, an optimal, stable, and efficient supervised learning framework that optimizes synthetic images by aligning global statistical flows in the original data. Our approach loads raw statistics only once and performs a single augmentation pass on the synthetic data, achieving performance comparable to or better than the state-of-the-art method with 10x less GPU
memory usage and 4x faster
distillation time. Moreover, increasing the number of augmentations for our method yields further performance gains while incurring lower additional cost. Our code is publicly available at https://github.com/einsteinxia/SFM.
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All authors Qianxin Xia, Jiawei Du, Yuhan Zhang, Xin Zhang, Xuewan He, Wenbo Jiang, Jielei Wang, Tao Luo, Guoming Lu
Computer Vision
Parallel Swin Transformer-Enhanced 3D MRI-to-CT Synthesis for MRI-Only Radiotherapy Planning
Zolnamar Dorjsembe, Hung-Yi Chen, Furen Xiao et al. (+1 more)
MRI provides superior soft tissue contrast without ionizing radiation; however, the absence of electron density information limits its direct use for dose calculation. As a result, current radiotherapy workflows rely on combined MRI and CT acquisitions, increasing registration uncertainty and procedural complexity. Synthetic CT generation enables MRI only planning but remains challenging due to nonlinear MRI-CT relationships and anatomical variability. We propose Parallel Swin
Transformer-Enhanced Med2
Transformer, a 3D architecture that integrates convolutional encoding with dual Swin
Transformer branches to model both local anatomical detail and long-range contextual dependencies. Multi-scale shifted window
attention with hierarchical feature aggregation improves anatomical fidelity. Experiments on public and clinical
datasets demonstrate higher image similarity and improved
geometric accuracy compared with baseline methods. Dosimetric evaluation shows clinically acceptable performance, with a mean target dose error of 1.69%. Code is available at: https://github.com/mobaidoctor/med2
transformer.
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All authors Zolnamar Dorjsembe, Hung-Yi Chen, Furen Xiao, Hsing-Kuo Pao
Computer Vision
Dolphin-v2: Universal Document Parsing via Scalable Anchor Prompting
Hao Feng, Wei Shi, Ke Zhang et al. (+9 more)
Document parsing has garnered widespread
attention as
vision-
language models (VLMs) advance OCR capabilities. However, the field remains fragmented across dozens of specialized models with varying strengths, forcing users to navigate complex model selection and limiting system scalability. Moreover, existing two-stage approaches depend on axis-aligned bounding boxes for layout detection, failing to handle distorted or photographed documents effectively. To this end, we present Dolphin-v2, a two-stage document image parsing model that substantially improves upon the original Dolphin. In the first stage, Dolphin-v2 jointly performs document type classification (digital-born versus photographed) alongside layout analysis. For digital-born documents, it conducts finer-grained element detection with reading order prediction. In the second stage, we employ a hybrid parsing strategy: photographed documents are parsed holistically as complete pages to handle
geometric distortions, while digital-born documents undergo element-wise parallel parsing guided by the detected layout anchors, enabling efficient content extraction. Compared with the original Dolphin, Dolphin-v2 introduces several crucial enhancements: (1) robust parsing of photographed documents via holistic page-level understanding, (2) finer-grained element detection (21 categories) with semantic attribute extraction such as author information and document metadata, and (3) code block recognition with indentation preservation, which existing systems typically lack. Comprehensive evaluations are conducted on DocPTBench, OmniDocBench, and our self-constructed RealDoc-160
benchmark. The results demonstrate substantial improvements: +14.78 points overall on the challenging OmniDocBench and 91% error reduction on photographed documents, while maintaining efficient inference through parallel processing.
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All authors Hao Feng, Wei Shi, Ke Zhang, Xiang Fei, Lei Liao, Dingkang Yang, Yongkun Du, Xuecheng Wu, Jingqun Tang, Yang Liu, Hong Chen, Can Huang
Computer Vision
VRIQ: Benchmarking and Diagnosing the Visual-Reasoning IQ of Vision-Language Models
Tina Khezresmaeilzadeh, Jike Zhong and Konstantinos Psounis
Vision-
language model (VLM) errors on visual
reasoning may come from missing visual structure, from mistakes after that structure is extracted, or from both. We introduce VRIQ, a
benchmark of 1,391 human-verified IQ-style visual
reasoning puzzles spanning five psychometric categories, each instantiated in category-matched abstract and natural-image domains. To study perception and
reasoning more directly, we also introduce DiagVRIQ, a 209-item diagnostic subset paired with human-verified, rule-neutral textual descriptions. Across 21 VLMs, the 17 non-frontier models reach a 26.8% unweighted category mean on abstract puzzles, while three 2026 frontier models improve substantially but unevenly, with unweighted category means of 41.3–70.9% on abstract puzzles and at most 78.2% on natural-image puzzles; all remain below human performance. On DiagVRIQ, even when their descriptions are judged correct, the strongest models still answer approximately 9–12% of those items incorrectly. Two interventions, ground-truth description augmentation and tool-augmented inference, improve accuracy, with the largest tool gains on spatially demanding categories, but neither eliminates errors. VRIQ and DiagVRIQ provide a framework for studying visual
reasoning through complementary end-to-end, description-based, and intervention-based analyses rather than treating it as a single monolithic capability.
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Computer Vision
SAIL: Self-Amplified Iterative Learning for Diffusion Model Alignment with Minimal Human Feedback
Xiaoxuan He, Siming Fu, Wanli Li et al. (+5 more)
Aligning
diffusion models with human preferences remains challenging, particularly when reward models are unavailable or impractical to obtain, and collecting large-scale preference
datasets is prohibitively expensive. This raises a fundamental question: can we achieve effective
alignment using only minimal human feedback, without auxiliary reward models, by unlocking the latent capabilities within
diffusion models themselves? In this paper, we propose SAIL (Self-Amplified Iterative Learning), a novel framework that enables
diffusion models to act as their own teachers through iterative self-improvement. Starting from a minimal seed set of human-annotated preference pairs, SAIL operates in a closed-loop manner where the model progressively generates diverse samples, self-annotates preferences based on its evolving understanding, and refines itself using this self-augmented
dataset. To ensure robust learning and prevent catastrophic forgetting, we introduce a ranked preference mixup strategy that carefully balances exploration with adherence to initial human priors. Extensive experiments demonstrate that SAIL consistently outperforms state-of-the-art methods across multiple
benchmarks while using merely 6% of the preference data required by existing approaches, revealing that
diffusion models possess remarkable self-improvement capabilities that, when properly harnessed, can effectively replace both large-scale human annotation and external reward models.
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All authors Xiaoxuan He, Siming Fu, Wanli Li, Zhiyuan Li, Dacheng Yin, Kang Rong, Fengyun Rao, Bo Zhang
Computer Vision · Cross-listed from cs.LG
Erase at the Core: Representation Unlearning for Machine Unlearning
Jaewon Lee, Yongwoo Kim and Donghyun Kim
Many approximate machine unlearning methods demonstrate strong logit-level forgetting – such as near-zero accuracy on the forget set – yet continue to preserve substantial information within their internal feature representations. We refer to this discrepancy as superficial forgetting. Recent studies indicate that most existing unlearning approaches primarily alter the final classifier, leaving intermediate representations largely unchanged and highly similar to those of the original model. To address this limitation, we introduce the Erase at the Core (EC), a framework designed to enforce forgetting throughout the entire network hierarchy. EC integrates multi-layer contrastive unlearning on the forget set with retain set preservation through deeply supervised learning. Concretely, EC attaches auxiliary modules to intermediate layers and applies both contrastive unlearning and cross-
entropy losses at each supervision point, with layer-wise weighted losses. Experimental results show that EC not only achieves effective logit-level forgetting, but also substantially reduces representational similarity to the original model across intermediate layers. Furthermore, EC is model-agnostic and can be incorporated as a plug-in module into existing unlearning methods, improving representation-level forgetting while maintaining performance on the retain set.
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Computer Vision
Imagine a City: CityGenAgent for Procedural 3D City Generation
Zishan Liu, Zecong Tang, RuoCheng Wu et al. (+6 more)
The automated generation of interactive 3D cities is a critical challenge with broad applications in autonomous driving, virtual reality, and
embodied intelligence. While recent advances in generative models and procedural techniques have improved the realism of city generation, existing methods often struggle with high-fidelity asset creation, controllability, and manipulation. In this work, we introduce CityGenAgent, a natural
language-driven framework for hierarchical procedural generation of high-quality 3D cities. Our approach decomposes city generation into two interpretable components, Block Program and Building Program. To ensure structural correctness and semantic
alignment, we adopt a two-stage learning strategy: (1) Supervised Fine-Tuning (SFT). We train BlockGen and BuildingGen to generate valid programs that adhere to schema constraints, including non-self-intersecting polygons and complete fields; (2) Reinforcement Learning (RL). We design Spatial
Alignment Reward to enhance spatial
reasoning ability and Visual Consistency Reward to bridge the gap between textual descriptions and the visual modality. Benefiting from the programs and the models' generalization, CityGenAgent supports natural
language editing and manipulation. Comprehensive evaluations demonstrate superior semantic
alignment, visual quality, and controllability compared to existing methods, establishing a robust foundation for scalable 3D city generation.
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All authors Zishan Liu, Zecong Tang, RuoCheng Wu, Xinzhe Zheng, Jingyu Hu, Ka-Hei Hui, Haoran Xie, Bo Dai, Zhengzhe Liu
Computer Vision
Breaking Semantic Hegemony: Decoupling Principal and Residual Subspaces for Generalized OOD Detection
Ningkang Peng, Xiaoqian Peng, Yuhao Zhang et al. (+7 more)
While feature-based post-hoc methods have made significant strides in Out-of-Distribution (OOD) detection, we uncover a counter-intuitive Simplicity Paradox in existing state-of-the-art (SOTA) models: these models exhibit keen sensitivity in distinguishing semantically subtle OOD samples but suffer from severe
Geometric Blindness when confronting structurally distinct yet semantically simple samples or high-frequency sensor noise. We attribute this phenomenon to Semantic Hegemony within the deep feature space and reveal its mathematical essence through the lens of Neural Collapse. Theoretical analysis demonstrates that the spectral concentration bias, induced by the high variance of the principal subspace, numerically masks the structural distribution shift signals that should be significant in the residual subspace. To address this issue, we propose D-KNN, a training-free, plug-and-play
geometric decoupling framework. This method utilizes orthogonal decomposition to explicitly separate semantic components from structural residuals and introduces a dual-space calibration mechanism to reactivate the model's sensitivity to weak residual signals. Extensive experiments demonstrate that D-KNN effectively breaks Semantic Hegemony, establishing new SOTA performance on both CIFAR and ImageNet
benchmarks. Notably, in resolving the Simplicity Paradox, it reduces the FPR95 from 31.3% to 2.3%; when addressing sensor failures such as Gaussian noise, it boosts the detection performance (AUROC) from a baseline of 79.7% to 94.9%.
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All authors Ningkang Peng, Xiaoqian Peng, Yuhao Zhang, Qianfeng Yu, Feng Xing, Peirong Ma, Xichen Yang, Yi Chen, Tingyu Lu, Yanhui Gu
Computer Vision
Multimodal Latent Reasoning via Hierarchical Visual Cues Injection
Yiming Zhang, Qiangyu Yan, Borui Jiang et al. (+1 more)
The advancement of
multimodal large
language models (MLLMs) has enabled impressive perception capabilities. However, their
reasoning process often remains a "fast thinking" paradigm, reliant on end-to-end generation or explicit,
language-centric chains of thought (CoT), which can be inefficient, verbose, and prone to
hallucination. This work posits that robust
reasoning should evolve within a latent space, integrating
multimodal signals seamlessly. We propose
multimodal latent
reasoning via HIerarchical Visual cuEs injection (HIVE), a novel framework that instills deliberate, "slow thinking" without depending on superficial textual rationales. Our method recursively extends
transformer blocks, creating an internal loop for iterative
reasoning refinement. Crucially, it injectively grounds this process with hierarchical visual cues from global scene context to fine-grained regional details directly into the model's latent representations. This enables the model to perform grounded, multi-step inference entirely in the aligned latent space. Extensive evaluations demonstrate that test-time scaling is effective when incorporating
vision knowledge, and that integrating hierarchical information significantly enhances the model's understanding of complex scenes.
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All authors Yiming Zhang, Qiangyu Yan, Borui Jiang, Kai Han
Computer Vision
Learning with Adaptive Prototype Manifolds for Out-of-Distribution Detection
Ningkang Peng, JiuTao Zhou, Yuhao Zhang et al. (+6 more)
Out-of-distribution (OOD) detection is a critical task for the safe deployment of machine learning models in the real world. Existing prototype-based representation learning methods have demonstrated exceptional performance. Specifically, we identify two fundamental flaws that universally constrain these methods: the Static Homogeneity Assumption (fixed representational resources for all classes) and the Learning-Inference Disconnect (discarding rich prototype quality knowledge at inference). These flaws fundamentally limit the model's capacity and performance. To address these issues, we propose APEX (Adaptive Prototype for eXtensive OOD Detection), a novel OOD detection framework designed via a Two-Stage Repair process to optimize the learned feature
manifold. APEX introduces two key innovations to address these respective flaws: (1) an Adaptive Prototype
Manifold (APM), which leverages the Minimum Description Length (MDL) principle to automatically determine the optimal prototype complexity $K_c^*$ for each class, thereby fundamentally resolving prototype collision; and (2) a Posterior-Aware OOD Scoring (PAOS) mechanism, which quantifies prototype quality (cohesion and separation) to bridge the learning-inference disconnect. Comprehensive experiments on
benchmarks such as CIFAR-100 validate the superiority of our method, where APEX achieves new state-of-the-art performance.
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All authors Ningkang Peng, JiuTao Zhou, Yuhao Zhang, Xiaoqian Peng, Qianfeng Yu, Linjing Qian, Tingyu Lu, Yi Chen, Yanhui Gu
Computer Vision
Consistency-Preserving Concept Erasure via Unsafe-Safe Pairing and Directional Fisher-Weighted Adaptation
Yongwoo Kim, Sungmin Cha, Hyunsoo Kim et al. (+2 more)
With the increasing versatility of text-to-image
diffusion models, the ability to selectively erase undesirable concepts (e.g., harmful content) has become indispensable. However, existing concept erasure approaches primarily focus on removing unsafe concepts without providing guidance toward corresponding safe alternatives, which often leads to failure in preserving the structural and semantic consistency between the original and erased generations. In this paper, we propose a novel framework, PAIRed Erasing (PAIR), which reframes concept erasure from simple removal to consistency-preserving semantic realignment using unsafe-safe pairs. We first generate safe counterparts from unsafe inputs while preserving structural and semantic fidelity, forming paired unsafe-safe
multimodal data. Leveraging these pairs, we introduce two key components: (1) Paired Semantic Realignment, a guided objective that uses unsafe-safe pairs to explicitly map target concepts to semantically aligned safe anchors; and (2) Fisher-Weighted Initialization for DoRA, which initializes parameter-efficient low-rank adaptation
matrices using unsafe-safe pairs, encouraging the generation of safe alternatives while selectively suppressing unsafe concepts. Together, these components enable fine-grained erasure that removes only the targeted concepts while maintaining overall semantic consistency. Extensive experiments demonstrate that our approach achieves the best overall trade-off among baselines, providing effective concept erasure while preserving structural integrity, semantic coherence, and generation quality.
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All authors Yongwoo Kim, Sungmin Cha, Hyunsoo Kim, Jaewon Lee, Donghyun Kim
Computer Vision
MTPano: Multi-Task Panoramic Scene Understanding via Label-Free Integration of Dense Prediction Priors
Jingdong Zhang, Xiaohang Zhan, Lingzhi Zhang et al. (+5 more)
Comprehensive panoramic scene understanding is critical for immersive applications, yet it remains challenging due to the scarcity of high-resolution, multi-task annotations. While perspective foundation models have achieved success through data scaling, directly adapting them to the panoramic domain often fails due to severe
geometric distortions and coordinate system discrepancies. Furthermore, the underlying relations between diverse dense prediction tasks in spherical spaces are underexplored. To address these challenges, we propose MTPano, a robust multi-task panoramic foundation model established by a label-free training pipeline. First, to circumvent data scarcity, we leverage powerful perspective dense priors. We project panoramic images into perspective patches to generate accurate, domain-gap-free pseudo-labels using off-the-shelf foundation models, which are then re-projected to serve as patch-wise supervision. Second, to tackle the interference between task types, we categorize tasks into rotation-invariant (e.g., depth,
segmentation) and rotation-variant (e.g., surface normals) groups. We introduce the Panoramic Dual BridgeNet, which disentangles these feature streams via geometry-aware modulation layers that inject absolute position and ray direction priors. To handle the distortion from equirectangular projections (ERP), we incorporate ERP
token mixers followed by a dual-branch BridgeNet for interactions with gradient truncation, facilitating beneficial cross-task information sharing while blocking conflicting gradients from incompatible task attributes. Additionally, we introduce auxiliary tasks to fertilize the cross-task learning process. Extensive experiments demonstrate that MTPano achieves state-of-the-art performance on multiple
benchmarks and delivers competitive results against task-specific panoramic specialist foundation models.
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All authors Jingdong Zhang, Xiaohang Zhan, Lingzhi Zhang, Yizhou Wang, Zhengming Yu, Jionghao Wang, Wenping Wang, Xin Li
Computer Vision
Wid3R: Wide Field-of-View 3D Reconstruction via Camera Model Conditioning
Dongki Jung, Jaehoon Choi, Adil Qureshi et al. (+3 more)
We present Wid3R, a feed-forward neural network for multi-view visual geometry reconstruction that supports wide field-of-view camera models. Unlike existing methods that assume rectified or pinhole inputs, Wid3R directly models wide-angle imagery without explicit calibration or undistortion. Our approach leverages a ray-based representation with spherical harmonics and introduces a novel camera model
token to enable distortion-aware reconstruction. To the best of our knowledge, Wid3R is the first multi-frame feed-forward 3D reconstruction method that supports 360 imagery. Moreover, we show that conditioning on diverse camera types improves generalization to 360 scenes and alleviates data sparsity issues. Wid3R achieves significant performance gains, improving AUC@30 by up to +33.67 on Zip-NeRF (fisheye) and +77.33 on Stanford2D3D (360).
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All authors Dongki Jung, Jaehoon Choi, Adil Qureshi, Somi Jeong, Dinesh Manocha, Suyong Yeon
Computer Vision
FlashBlock: Attention Caching for Efficient Long-Context Block Diffusion
Zhuokun Chen, Jianfei Cai and Bohan Zhuang
Generating long-form content, such as minute-long
videos and extended texts, is increasingly important for modern generative models. Block
diffusion improves inference efficiency via KV caching and block-wise causal inference and has been widely adopted in
diffusion language models and
video generation. However, in long-context settings, block
diffusion still incurs substantial overhead from repeatedly computing
attention over a growing KV cache. We identify an underexplored property of block
diffusion: cross-step redundancy of
attention within a block. Our analysis shows that
attention outputs from
tokens outside the current block remain largely stable across
diffusion steps, while block-internal
attention varies significantly. Based on this observation, we propose FlashBlock, a cached block-external
attention mechanism that reuses stable
attention output, reducing
attention computation and KV cache access without modifying the
diffusion process. Moreover, FlashBlock is orthogonal to sparse
attention and can be combined as a complementary residual reuse strategy, substantially improving model accuracy under aggressive sparsification. Experiments on
diffusion language models and
video generation demonstrate up to 1.44$\times$ higher
token throughput and up to 1.6$\times$ reduction in
attention time, with negligible impact on generation quality. Project page: https://caesarhhh.github.io/FlashBlock/.
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Computer Vision
Fast-SAM3D: 3Dfy Anything in Images but Faster
Weilun Feng, Mingqiang Wu, Zhiliang Chen et al. (+10 more)
SAM3D enables scalable, open-world 3D reconstruction from complex scenes, yet its deployment is hindered by prohibitive inference latency. In this work, we conduct the first systematic investigation into its inference dynamics, revealing that generic acceleration strategies are brittle in this context. We demonstrate that these failures stem from neglecting the pipeline's inherent multi-level heterogeneity: the kinematic distinctiveness between shape and layout, the intrinsic sparsity of texture refinement, and the spectral variance across geometries. To address this, we present Fast-SAM3D, a training-free framework that dynamically aligns computation with instantaneous generation complexity. Our approach integrates three heterogeneity-aware mechanisms: (1) Modality-Aware Step Caching to decouple structural
evolution from sensitive layout updates; (2) Joint Spatiotemporal
Token Carving to concentrate refinement on high-
entropy regions; and (3) Spectral-Aware
Token Aggregation to adapt decoding resolution. Extensive experiments demonstrate that Fast-SAM3D delivers up to \textbf{2.67$\times$} end-to-end speedup with negligible fidelity loss, establishing a new Pareto frontier for efficient single-view 3D generation. Our code is released in https://github.com/wlfeng0509/Fast-SAM3D.
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All authors Weilun Feng, Mingqiang Wu, Zhiliang Chen, Chuanguang Yang, Haotong Qin, Yuqi Li, Xiaokun Liu, Guoxin Fan, Libo Huang, Yulun Zhang, Michele Magno, Yongjun Xu, Zhulin An
Computer Vision
Qi Li, Yanzhe Zhao, Yongxin Zhou et al. (+4 more)
Multimodal Large
Language Models (MLLMs) have shown immense promise in universal
multimodal retrieval, which aims to find relevant items of various modalities for a given query. However, their practical application is often hindered by the substantial computational cost incurred from processing a large number of
tokens from visual inputs. In this paper, we propose Magic-MM-
Embedding, a series of novel models that achieve both high efficiency and state-of-the-art performance in universal
multimodal embedding. Our approach is built on two synergistic pillars: (1) a highly efficient MLLM architecture incorporating visual
token compression to drastically reduce inference latency and training time, and (2) a multi-stage progressive training strategy designed to not only recover but significantly boost performance. This coarse-to-fine training paradigm begins with extensive continued training to restore
multimodal understanding and generation capabilities, progresses to large-scale contrastive pretraining and hard negative mining to enhance discriminative power, and culminates in a task-aware fine-tuning stage guided by an MLLM-as-a-Judge for precise data curation. Comprehensive experiments show that our model outperforms existing methods by a large margin while being more inference-efficient.
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All authors Qi Li, Yanzhe Zhao, Yongxin Zhou, Yameng Wang, Yandong Yang, Yuanjia Zhou, Jinxiang Liu
Computer Vision
Unlocking Prototype Potential: An Efficient Tuning Framework for Few-Shot Class-Incremental Learning
Shengqin Jiang, Xiaoran Feng, Yuankai Qi et al. (+6 more)
Few-shot class-incremental learning (FSCIL) seeks to continuously learn new classes from very limited samples while preserving previously acquired knowledge. Traditional methods often utilize a frozen pre-trained feature extractor to generate static class prototypes, which suffer from the inherent representation bias of the backbone. While recent
prompt-based tuning methods attempt to adapt the backbone via minimal parameter updates, given the constraint of extreme data scarcity, the model's capacity to assimilate novel information and substantively enhance its global discriminative power is inherently limited. In this paper, we propose a novel shift in perspective: freezing the feature extractor while fine-tuning the prototypes. We argue that the primary challenge in FSCIL is not feature acquisition, but rather the optimization of decision regions within a static, high-quality feature space. To this end, we introduce an efficient prototype fine-tuning framework that evolves static centroids into dynamic, learnable components. The framework employs a dual-calibration method consisting of class-specific and task-aware offsets. These components function synergistically to improve the discriminative capacity of prototypes for ongoing incremental classes. Extensive results demonstrate that our method attains superior performance across multiple
benchmarks while requiring minimal learnable parameters.
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All authors Shengqin Jiang, Xiaoran Feng, Yuankai Qi, Haokui Zhang, Renlong Hang, Qingshan Liu, Lina Yao, Quan Z. Sheng, Ming-Hsuan Yang
Computer Vision
ReGLA: Efficient Receptive-Field Modeling with Gated Linear Attention Network
Junzhou Li, Manqi Zhao, Yilin Gao et al. (+4 more)
Balancing accuracy and latency on high-resolution images is a critical challenge for lightweight models, particularly for
Transformer-based architectures that often suffer from excessive latency. To address this issue, we introduce ReGLA, a series of lightweight hybrid networks, which integrates efficient convolutions for local feature extraction with ReLU-based gated linear
attention for global modeling. The design incorporates three key innovations: the Efficient Large Receptive Field (ELRF) module for enhancing convolutional efficiency while preserving a large receptive field; the ReLU Gated Modulated
Attention (RGMA) module for maintaining linear complexity while enhancing local feature representation; and a multi-teacher
distillation strategy to boost performance on downstream tasks. Extensive experiments validate the superiority of ReGLA; particularly the ReGLA-M achieves 80.85% Top-1 accuracy on ImageNet-1K at $224px$, with only 4.98 ms latency at $512px$. Furthermore, ReGLA outperforms similarly scaled iFormer models in downstream tasks, achieving gains of 3.1% AP on COCO object detection and 3.6% mIoU on ADE20K semantic
segmentation, establishing it as a state-of-the-art solution for high-resolution visual applications.
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All authors Junzhou Li, Manqi Zhao, Yilin Gao, Zhiheng Yu, Yin Li, Dongsheng Jiang, Li Xiao
Computer Vision
RFM-Pose:Reinforcement-Guided Flow Matching for Fast Category-Level 6D Pose Estimation
Diya He, Qingchen Liu, Cong Zhang et al. (+1 more)
Object pose estimation is a fundamental problem in computer
vision and plays a critical role in virtual reality and
embodied intelligence, where
agents must understand and interact with objects in 3D space. Recently, score based generative models have to some extent solved the rotational
symmetry ambiguity problem in category level pose estimation, but their efficiency remains limited by the high sampling cost of score-based
diffusion. In this work, we propose a new framework, RFM-Pose, that accelerates category-level 6D object pose generation while actively evaluating sampled hypotheses. To improve sampling efficiency, we adopt a flow-matching generative model and generate pose candidates along an optimal transport path from a simple prior to the pose distribution. To further refine these candidates, we cast the flow-matching sampling process as a Markov decision process and apply proximal policy optimization to fine-tune the sampling policy. In particular, we interpret the flow field as a learnable policy and map an
estimator to a value network, enabling joint optimization of pose generation and hypothesis scoring within a reinforcement learning framework. Experiments on the REAL275
benchmark demonstrate that RFM-Pose achieves favorable performance while significantly reducing computational cost. Moreover, similar to prior work, our approach can be readily adapted to object pose tracking and attains competitive results in this setting.
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All authors Diya He, Qingchen Liu, Cong Zhang, Jiahu Qin
Computer Vision
Active Label Cleaning for Reliable Detection of Electron Dense Deposits in Transmission Electron Microscopy Images
Jieyun Tan, Shuo Liu, Guibin Zhang et al. (+4 more)
Automated detection of electron dense deposits (EDD) in glomerular disease is hindered by the scarcity of high-quality labeled data. While crowdsourcing reduces annotation cost, it introduces label noise. We propose an active label cleaning method to efficiently denoise crowdsourced
datasets. Our approach uses active learning to select the most valuable noisy samples for expert re-annotation, building high-accuracy cleaning models. A Label Selection Module leverages discrepancies between crowdsourced labels and model predictions for both sample selection and instance-level noise grading. Experiments show our method achieves 67.18% AP\textsubscript{50} on a private
dataset, an 18.83% improvement over training on noisy labels. This performance reaches 95.79% of that with full expert annotation while reducing annotation cost by 73.30%. The method provides a practical, cost-effective solution for developing reliable medical AI with limited expert resources.
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All authors Jieyun Tan, Shuo Liu, Guibin Zhang, Ziqi Li, Jian Geng, Lei Zhang, Lei Cao
Computer Vision · Cross-listed from cs.LG
CORP: Closed-Form One-shot Representation-Preserving Structured Pruning
Boxiang Zhang and Baijian Yang
Transformers achieve strong accuracy but incur high compute and
memory cost. Structured
pruning reduces inference cost, but most methods rely on retraining or multi-stage optimization, which limits post-training deployment. We propose CORP, a closed-form one-shot structured
pruning method that removes MLP dimensions and
attention substructures using only unlabeled calibration data without gradients or fine-tuning. CORP formulates structured
pruning as a representation recovery problem. It models removed components as affine functions of retained components and derives closed-form ridge regression solutions that fold compensation into model weights. This minimizes a layer-local affine/logit reconstruction objective under the calibration distribution. Experiments on ImageNet with DeiT reveal strong redundancy in both MLP and
attention representations. With CORP, models retain high accuracy under aggressive sparsity. On DeiT-Huge, CORP achieves 83.24% Top-1 accuracy after
pruning 50% of both MLP and
attention structures.
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Computer Vision
PatchFlow: Leveraging a Flow-Based Model with Patch Features
Boxiang Zhang, Baijian Yang, Xiaoming Wang et al. (+1 more)
Die casting plays a crucial role across various industries due to its ability to craft intricate shapes with high precision and smooth surfaces. However, surface defects remain a major issue that impedes die casting quality control. Recently, computer
vision techniques have been explored to automate and improve defect detection. In this work, we combine local neighbor-aware patch features with a normalizing flow model and bridge the gap between the generic pretrained feature extractor and industrial product images by introducing an adapter module to increase the efficiency and accuracy of automated anomaly detection. Compared to state-of-the-art methods, our approach reduces the error rate by 20% on the MVTec AD
dataset, achieving an image-level AUROC of 99.28%. Our approach has also enhanced performance on the VisA
dataset , achieving an image-level AUROC of 96.48%. Compared to the state-of-the-art models, this represents a 28.2% reduction in error. Additionally, experiments on a proprietary die casting
dataset yield an accuracy of 95.77% for anomaly detection, without requiring any anomalous samples for training. Our method illustrates the potential of leveraging computer
vision and deep learning techniques to advance inspection capabilities for the die casting industry
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All authors Boxiang Zhang, Baijian Yang, Xiaoming Wang, Corey Vian
Computer Vision
Boosting SAM for Cross-Domain Few-Shot Segmentation via Conditional Point Sparsification
Jiahao Nie, Yun Xing, Wenbin An et al. (+6 more)
Motivated by the success of the Segment Anything Model (SAM) in promptable
segmentation, recent studies leverage SAM to develop training-free solutions for few-shot
segmentation, which aims to predict object masks in the target image based on a few reference exemplars. These SAM-based methods typically rely on point matching between reference and target images and use the matched dense points as
prompts for mask prediction. However, we observe that dense points perform poorly in Cross-Domain Few-Shot
Segmentation (CD-FSS), where target images are from medical or satellite domains. We attribute this issue to large domain shifts that disrupt the point-image interactions learned by SAM, and find that point density plays a crucial role under such conditions. To address this challenge, we propose Conditional Point Sparsification (CPS), a training-free approach that adaptively guides SAM interactions for cross-domain images based on reference exemplars. Leveraging ground-truth masks, the reference images provide reliable guidance for adaptively sparsifying dense matched points, enabling more accurate
segmentation results. Extensive experiments demonstrate that CPS outperforms existing training-free SAM-based methods across diverse CD-FSS
datasets.
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All authors Jiahao Nie, Yun Xing, Wenbin An, Qingsong Zhao, Jiawei Shao, Yap-Peng Tan, Alex C. Kot, Shijian Lu, Xuelong Li
Computer Vision
Cross-Domain Few-Shot Segmentation via Multi-view Progressive Adaptation
Jiahao Nie, Guanqiao Fu, Wenbin An et al. (+3 more)
Cross-Domain Few-Shot
Segmentation aims to segment categories in data-scarce domains conditioned on a few exemplars. Typical methods first establish few-shot capability in a large-scale source domain and then adapt it to target domains. However, due to the limited quantity and diversity of target samples, existing methods still exhibit constrained performance. Moreover, the source-trained model's initially weak few-shot capability in target domains, coupled with substantial domain gaps, severely hinders the effective utilization of target samples and further impedes adaptation. To this end, we propose Multi-view Progressive Adaptation, which progressively adapts few-shot capability to target domains from both data and strategy perspectives. (i) From the data perspective, we introduce Hybrid Progressive Augmentation, which progressively generates more diverse and complex views through cumulative strong augmentations, thereby creating increasingly challenging learning scenarios. (ii) From the strategy perspective, we design Dual-chain Multi-view Prediction, which fully leverages these progressively complex views through sequential and parallel learning paths under extensive supervision. By jointly enforcing prediction consistency across diverse and complex views, MPA achieves both robust and accurate adaptation to target domains. Extensive experiments demonstrate that MPA effectively adapts few-shot capability to target domains, outperforming state-of-the-art methods by a large margin (+7.0%).
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All authors Jiahao Nie, Guanqiao Fu, Wenbin An, Yap-Peng Tan, Alex C. Kot, Shijian Lu