Computer Vision · Cross-listed from cs.RO
Self-Correcting VLA: Online Action Refinement via Sparse World Imagination
Chenyv Liu, Wentao Tan, Lei Zhu et al. (+4 more)
Standard
vision-
language-action (VLA) models rely on fitting statistical data priors, limiting their robust understanding of underlying physical dynamics. Reinforcement learning enhances physical grounding through exploration yet typically relies on external reward signals that remain isolated from the
agent's internal states. World action models have emerged as a promising paradigm that integrates imagination and control to enable predictive planning. However, they rely on implicit context modeling, lacking explicit mechanisms for self-improvement. To solve these problems, we propose Self-Correcting VLA (SC-VLA), which achieve self-improvement by intrinsically guiding action refinement through sparse imagination. We first design sparse world imagination by integrating auxiliary predictive heads to forecast current task progress and future trajectory trends, thereby constraining the policy to encode short-term physical
evolution. Then we introduce the online action refinement module to reshape progress-dependent dense rewards, adjusting trajectory orientation based on the predicted sparse future states. Evaluations on challenging
robot manipulation tasks from simulation
benchmarks and real-world settings demonstrate that SC-VLA achieve state-of-the-art performance, yielding the highest task throughput with 16% fewer steps and a 9% higher success rate than the best-performing baselines, alongside a 14% gain in real-world experiments. Code is available at https://github.com/Kisaragi0/SC-VLA.
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All authors Chenyv Liu, Wentao Tan, Lei Zhu, Fengling Li, Jingjing Li, Guoli Yang, Heng Tao Shen
Computer Vision
UniHand: A Unified Model for Diverse Controlled 4D Hand Motion Modeling
Zhihao Sun, Tong Wu, Ruirui Tu et al. (+2 more)
Hand motion plays a central role in human interaction, yet modeling realistic 4D hand motion (i.e., 3D hand pose sequences over time) remains challenging. Research in this area is typically divided into two tasks: (1) Estimation approaches reconstruct precise motion from visual observations, but often fail under hand occlusion or absence; (2) Generation approaches focus on synthesizing hand poses by exploiting generative priors under multi-modal structured inputs and infilling motion from incomplete sequences. However, this separation not only limits the effective use of heterogeneous condition signals that frequently arise in practice, but also prevents knowledge transfer between the two tasks. We present UniHand, a unified
diffusion-based framework that formulates both estimation and generation as conditional motion synthesis. UniHand integrates heterogeneous inputs by
embedding structured signals into a shared latent space through a joint variational autoencoder, which aligns conditions such as MANO parameters and 2D skeletons. Visual observations are encoded with a frozen
vision backbone, while a dedicated hand perceptron extracts hand-specific cues directly from image features, removing the need for complex detection and cropping pipelines. A latent
diffusion model then synthesizes consistent motion sequences from these diverse conditions. Extensive experiments across multiple
benchmarks demonstrate that UniHand delivers robust and accurate hand motion modeling, maintaining performance under severe occlusions and temporally incomplete inputs.
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All authors Zhihao Sun, Tong Wu, Ruirui Tu, Daoguo Dong, Zuxuan Wu
Computer Vision
Tokenizing Semantic Segmentation with Run Length Encoding
Abhineet Singh, Justin Rozeboom and Nilanjan Ray
This paper presents a new unified approach to semantic
segmentation in both images and
videos by using
language modeling to output the masks as sequences of discrete
tokens. We use run length encoding (RLE) to discretize the
segmentation masks, and adapt the Pix2Seq framework to learn autoregressive models to output these
tokens. We propose novel tokenization strategies to compress the lengths of the
token sequences to make it practicable to extend this approach to
videos. We also show how instance information can be incorporated into the tokenization process to perform panoptic
segmentation. We evaluate our models on two domain-specific
datasets to demonstrate their competitiveness with the state of the art in certain scenarios, in spite of being severely bottlenecked by our limited computational resources. We supplement these analyses by proposing several promising approaches to foster future competitiveness in general-purpose applications, and facilitate this by making our code and models publicly available.
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Computer Vision
Virtual Biopsy for Intracranial Tumors Diagnosis on MRI
Xinzhe Luo, Shuai Shao, Yan Wang et al. (+3 more)
Deep intracranial tumors situated in eloquent
brain regions controlling vital functions present critical diagnostic challenges. Clinical practice has shifted toward stereotactic biopsy for pathological confirmation before treatment. Yet biopsy carries inherent risks of hemorrhage and neurological deficits and struggles with sampling bias due to tumor spatial heterogeneity, because pathological changes are typically region-selective rather than tumor-wide. Therefore, advancing non-invasive MRI-based pathology prediction is essential for holistic tumor assessment and modern clinical decision-making. The primary challenge lies in data scarcity: low tumor incidence requires long collection cycles, and annotation demands biopsy-verified pathology from neurosurgical experts. Additionally, tiny lesion volumes lacking
segmentation masks cause critical features to be overwhelmed by background noise. To address these challenges, we construct the ICT-MRI
dataset - the first public biopsy-verified
benchmark with 249 cases across four categories. We propose a Virtual Biopsy framework comprising: MRI-Processor for standardization; Tumor-Localizer employing
vision-
language models for coarse-to-fine localization via weak supervision; and Adaptive-Diagnoser with a Masked Channel
Attention mechanism fusing local discriminative features with global contexts. Experiments demonstrate over 90% accuracy, outperforming baselines by more than 20%.
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All authors Xinzhe Luo, Shuai Shao, Yan Wang, Jiangtao Wang, Yutong Bai, Jianguo Zhang
Computer Vision · Cross-listed from cs.RO
Iterative Closed-Loop Motion Synthesis for Scaling the Capabilities of Humanoid Control
Weisheng Xu, Qiwei Wu, Jiaxi Zhang et al. (+7 more)
Physics-based
humanoid control relies on training with motion
datasets that have diverse data distributions. However, the fixed difficulty distribution of
datasets limits the performance ceiling of the trained control policies. Additionally, the method of acquiring high-quality data through professional motion capture systems is constrained by costs, making it difficult to achieve large-scale scalability. To address these issues, we propose a closed-loop automated motion data generation and iterative framework. It can generate high-quality motion data with rich action semantics, including martial arts, dance, combat, sports, gymnastics, and more. Furthermore, our framework enables difficulty iteration of policies and data through physical metrics and objective evaluations, allowing the trained tracker to break through its original difficulty limits. On the PHC single-primitive tracker, using only approximately 1/10 of the AMASS
dataset size, the average failure rate on the test set (2201 clips) is reduced by 45% compared to the baseline. Finally, we conduct comprehensive ablation and comparative experiments to highlight the rationality and advantages of our framework.
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All authors Weisheng Xu, Qiwei Wu, Jiaxi Zhang, Tan Jing, Yangfan Li, Yuetong Fang, Jiaqi Xiong, Kai Wu, Rong Ou, Renjing Xu
Computer Vision
Trung X. Pham, Kang Zhang, Ji Woo Hong et al. (+1 more)
Diffusion Transformers have achieved state-of-the-art performance in class-conditional and
multimodal generation, yet the structure of their learned conditional
embeddings remains poorly understood. In this work, we present the first systematic study of these
embeddings and uncover a notable redundancy: class-conditioned
embeddings exhibit extreme angular similarity, exceeding 99% on ImageNet-1K, while continuous-condition tasks such as pose-guided image generation and
video-to-audio generation reach over 99.9%. We further find that semantic information is concentrated in a small subset of dimensions, with head dimensions carrying the dominant signal and tail dimensions contributing minimally. By
pruning low-magnitude dimensions–removing up to two-thirds of the
embedding space–we show that generation quality and fidelity remain largely unaffected, and in some cases improve. These results reveal a semantic bottleneck in
Transformer-based
diffusion models, providing new insights into how semantics are encoded and suggesting opportunities for more efficient conditioning mechanisms.
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All authors Trung X. Pham, Kang Zhang, Ji Woo Hong, Chang D. Yoo
Computer Vision · Cross-listed from cs.LG
Breaking Semantic-Aware Watermarks via LLM-Guided Coherence-Preserving Semantic Injection
Zheng Gao, Xiaoyu Li, Zhicheng Bao et al. (+2 more)
Generative images have proliferated on Web platforms in social media and online copyright distribution scenarios, and semantic watermarking has increasingly been integrated into
diffusion models to support reliable provenance tracking and forgery prevention for web content. Traditional noise-layer-based watermarking, however, remains vulnerable to inversion attacks that can recover embedded signals. To mitigate this, recent content-aware semantic watermarking schemes bind watermark signals to high-level image semantics, constraining local edits that would otherwise disrupt global coherence. Yet, large
language models (LLMs) possess structured
reasoning capabilities that enable targeted exploration of semantic spaces, allowing locally fine-grained but globally coherent semantic alterations that invalidate such bindings. To expose this overlooked vulnerability, we introduce a Coherence-Preserving Semantic Injection (CSI) attack that leverages LLM-guided semantic manipulation under
embedding-space similarity constraints. This
alignment enforces visual-semantic consistency while selectively perturbing watermark-relevant semantics, ultimately inducing detector misclassification. Extensive empirical results show that CSI consistently outperforms prevailing attack baselines against content-aware semantic watermarking, revealing a fundamental security weakness of current semantic watermark designs when confronted with LLM-driven semantic perturbations.
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All authors Zheng Gao, Xiaoyu Li, Zhicheng Bao, Xiaoyan Feng, Jiaojiao Jiang
Computer Vision
CADC: Content Adaptive Diffusion-Based Generative Image Compression
Xihua Sheng, Lingyu Zhu, Tianyu Zhang et al. (+3 more)
Diffusion-based generative image compression has demonstrated remarkable potential for achieving realistic reconstruction at ultra-low bitrates. The key to unlocking this potential lies in making the entire compression process content-adaptive, ensuring that the encoder's representation and the decoder's generative prior are dynamically aligned with the semantic and structural characteristics of the input image. However, existing methods suffer from three critical limitations that prevent effective content adaptation. First, isotropic quantization applies a uniform quantization step, failing to adapt to the spatially varying complexity of image content and creating a misalignment with the
diffusion model's noise-dependent prior. Second, the information concentration bottleneck – arising from the dimensional mismatch between the high-dimensional noisy latent and the
diffusion decoder's fixed input – prevents the model from adaptively preserving essential semantic information in the primary channels. Third, existing textual conditioning strategies either need significant textual bitrate overhead or rely on generic, content-agnostic textual
prompts, thereby failing to provide adaptive semantic guidance efficiently. To overcome these limitations, we propose a content-adaptive
diffusion-based image codec with three technical innovations: 1) an Uncertainty-Guided Adaptive Quantization method that learns spatial uncertainty maps to adaptively align quantization distortion with content characteristics; 2) an Auxiliary Decoder-Guided Information Concentration method that uses a lightweight auxiliary decoder to enforce content-aware information preservation in the primary latent channels; and 3) a Bitrate-Free Adaptive Textual Conditioning method that derives content-aware textual descriptions from the auxiliary reconstructed image, enabling semantic guidance without bitrate cost.
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All authors Xihua Sheng, Lingyu Zhu, Tianyu Zhang, Dong Liu, Shiqi Wang, Jing Wang
Computer Vision
SEF-MAP: Subspace-Decomposed Expert Fusion for Robust Multimodal HD Map Prediction
Haoxiang Fu, Lingfeng Zhang, Hao Li et al. (+7 more)
High-definition (HD) maps are essential for autonomous driving, yet multi-modal fusion often suffers from inconsistency between camera and
LiDAR modalities, leading to performance degradation under low-light conditions, occlusions, or sparse point clouds. To address this, we propose SEFMAP, a Subspace-Expert Fusion framework for robust
multimodal HD map prediction. The key idea is to explicitly disentangle BEV features into four semantic subspaces:
LiDAR-private, Image-private, Shared, and Interaction. Each subspace is assigned a dedicated expert, thereby preserving modality-specific cues while capturing cross-modal consensus. To adaptively combine expert outputs, we introduce an uncertainty-aware gating mechanism at the BEV-
cell level, where unreliable experts are down-weighted based on predictive variance, complemented by a usage balance regularizer to prevent expert collapse. To enhance robustness in degraded conditions and promote role specialization, we further propose distribution-aware masking: during training, modality-drop scenarios are simulated using EMA-statistical surrogate features, and a specialization loss enforces distinct behaviors of private, shared, and interaction experts across complete and masked inputs. Experiments on nuScenes and Argoverse2
benchmarks demonstrate that SEFMAP achieves state-of-the-art performance, surpassing prior methods by +4.2% and +4.8% in mAP, respectively. SEF-MAPprovides a robust and effective solution for multi-modal HD map prediction under diverse and degraded conditions.
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All authors Haoxiang Fu, Lingfeng Zhang, Hao Li, Ruibing Hu, Zhengrong Li, Guanjing Liu, Zimu Tan, Long Chen, Hangjun Ye, Xiaoshuai Hao
Computer Vision
MultiAnimate: Pose-Guided Image Animation Made Extensible
Yingcheng Hu, Haowen Gong, Chuanguang Yang et al. (+3 more)
Pose-guided human image animation aims to synthesize realistic
videos of a reference character driven by a sequence of poses. While
diffusion-based methods have achieved remarkable success, most existing approaches are limited to single-character animation. We observe that naively extending these methods to multi-character scenarios often leads to identity confusion and implausible occlusions between characters. To address these challenges, in this paper, we propose an extensible multi-character image animation framework built upon modern
Diffusion Transformers (DiTs) for
video generation. At its core, our framework introduces two novel components-Identifier Assigner and Identifier Adapter - which collaboratively capture per-person positional cues and inter-person spatial relationships. This mask-driven scheme, along with a scalable training strategy, not only enhances flexibility but also enables generalization to scenarios with more characters than those seen during training. Remarkably, trained on only a two-character
dataset, our model generalizes to multi-character animation while maintaining compatibility with single-character cases. Extensive experiments demonstrate that our approach achieves state-of-the-art performance in multi-character image animation, surpassing existing
diffusion-based baselines.
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All authors Yingcheng Hu, Haowen Gong, Chuanguang Yang, Zhulin An, Yongjun Xu, Songhua Liu
Computer Vision
Generalizing Visual Geometry Priors to Sparse Gaussian Occupancy Prediction
Changqing Zhou, Yueru Luo and Changhao Chen
Accurate 3D scene understanding is essential for
embodied intelligence, with occupancy prediction emerging as a key task for
reasoning about both objects and free space. Existing approaches largely rely on depth priors (e.g., DepthAnything) but make only limited use of 3D cues, restricting performance and generalization. Recently, visual geometry models such as VGGT have shown strong capability in providing rich 3D priors, but similar to
monocular depth foundation models, they still operate at the level of visible surfaces rather than volumetric interiors, motivating us to explore how to more effectively leverage these increasingly powerful geometry priors for 3D occupancy prediction. We present GPOcc, a framework that leverages generalizable visual geometry priors (GPs) for
monocular occupancy prediction. Our method extends surface points inward along camera rays to generate volumetric samples, which are represented as Gaussian primitives for probabilistic occupancy inference. To handle streaming input, we further design a training-free incremental update strategy that fuses per-frame Gaussians into a unified global representation. Experiments on Occ-ScanNet and EmbodiedOcc-ScanNet demonstrate significant gains: GPOcc improves mIoU by +9.99 in the
monocular setting and +11.79 in the streaming setting over prior state of the art. Under the same depth prior, it achieves +6.73 mIoU while running 2.65$\times$ faster. These results highlight that GPOcc leverages geometry priors more effectively and efficiently. Code will be released at https://github.com/JuIvyy/GPOcc.
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Computer Vision
VasGuideNet: Vascular Topology-Guided Couinaud Liver Segmentation with Structural Contrastive Loss
Chaojie Shen, Jingjun Gu, Zihao Zhao et al. (+4 more)
Accurate Couinaud liver
segmentation is critical for preoperative
surgical planning and tumor localization.However, existing methods primarily rely on image intensity and spatial location cues, without explicitly modeling vascular
topology. As a result, they often produce indistinct boundaries near vessels and show limited generalization under anatomical variability.We propose VasGuideNet, the first Couinaud
segmentation framework explicitly guided by vascular
topology. Specifically, skeletonized vessels, Euclidean distance transform (EDT)–derived geometry, and k-nearest neighbor (kNN) connectivity are encoded into
topology features using Graph Convolutional Networks (GCNs). These features are then injected into a 3D encoder–decoder backbone via a cross-
attention fusion module. To further improve inter-class separability and anatomical consistency, we introduce a Structural Contrastive Loss (SCL) with a global
memory bank.On Task08_HepaticVessel and our private LASSD
dataset, VasGuideNet achieves Dice scores of 83.68% and 76.65% with RVDs of 1.68 and 7.08, respectively. It consistently outperforms representative baselines including UNETR, Swin UNETR, and G-UNETR++, delivering higher Dice/mIoU and lower RVD across
datasets, demonstrating its effectiveness for anatomically consistent
segmentation. Code is available at https://github.com/Qacket/VasGuideNet.git.
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All authors Chaojie Shen, Jingjun Gu, Zihao Zhao, Ruocheng Li, Cunyuan Yang, Jiajun Bu, Lei Wu
Computer Vision
IHF-Harmony: Multi-Modality Magnetic Resonance Images Harmonization using Invertible Hierarchy Flow Model
Pengli Zhu, Yitao Zhu, Haowen Pang et al. (+1 more)
Retrospective MRI harmonization is limited by poor scalability across modalities and reliance on traveling subject
datasets. To address these challenges, we introduce IHF-Harmony, a unified invertible hierarchy flow framework for multi-modality harmonization using unpaired data. By decomposing the translation process into reversible feature transformations, IHF-Harmony guarantees bijective mapping and lossless reconstruction to prevent anatomical distortion. Specifically, an invertible hierarchy flow (IHF) performs hierarchical subtractive coupling to progressively remove artefact-related features, while an artefact-aware normalization (AAN) employs anatomy-fixed feature modulation to accurately transfer target characteristics. Combined with anatomy and artefact consistency loss objectives, IHF-Harmony achieves high-fidelity harmonization that retains source anatomy. Experiments across multiple MRI modalities demonstrate that IHF-Harmony outperforms existing methods in both anatomical fidelity and downstream task performance, facilitating robust harmonization for large-scale multi-site imaging studies. Code is available at https://github.com/Idea89560041/IHF-Harmony.
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All authors Pengli Zhu, Yitao Zhu, Haowen Pang, Anqi Qiu
Computer Vision
AdaGS: Confidence-Gated Diffusion Adaptation for Unposed Sparse-View 3D Gaussian Reconstruction
Beizhen Zhao, Sicheng Yu, Guanzhi Ding et al. (+2 more)
Sparse-view outdoor 3D reconstruction remains challenging for Gaussian splatting. Although
diffusion restorers can provide additional pseudo-view supervision, we identify a failure pattern in
diffusion-guided reconstruction: an unguided
diffusion model may misinterpret floating artifacts and complete them into plausible but non-existent scene structures. To mitigate this challenge, we propose a confidence-gated
diffusion adaptation framework for unposed sparse-view reconstruction. We introduce an artifact-aware adapter that is trained in two stages on our constructed Gaussian artifact
dataset. The adapter suppresses floater-induced visual cues before
diffusion restoration, while bidirectional confidence fusion accepts generated regions only when they are supported by cross-view
geometric evidence. Furthermore, we introduce a Gaussian stabilization strategy to attenuate Gaussians that exhibit weak multi-view inconsistency, preventing them from repeatedly triggering
hallucinations in later optimization. Extensive experiments on three outdoor
datasets demonstrate that our method improves rendering fidelity and pose accuracy over existing approaches.
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All authors Beizhen Zhao, Sicheng Yu, Guanzhi Ding, Yu Hu, Hao Wang
Computer Vision · Cross-listed from cs.RO
LiLo-VLA: Compositional Long-Horizon Manipulation via Linked Object-Centric Policies
Yue Yang, Shuo Cheng, Yu Fang et al. (+4 more)
General-purpose
robots must master long-horizon manipulation, defined as tasks involving multiple kinematic structure changes (e.g., attaching or detaching objects) in unstructured environments. While
Vision-
Language-Action (VLA) models offer the potential to master diverse atomic skills, they struggle with the combinatorial complexity of sequencing them and are prone to cascading failures due to environmental sensitivity. To address these challenges, we propose LiLo-VLA (Linked Local VLA), a modular framework capable of zero-shot generalization to novel long-horizon tasks without ever being trained on them. Our approach decouples transport from interaction: a Reaching Module handles global motion, while an Interaction Module employs an object-centric VLA to process isolated objects of interest, ensuring robustness against irrelevant visual features and invariance to spatial configurations. Crucially, this modularity facilitates robust failure recovery through dynamic replanning and skill reuse, effectively mitigating the cascading errors common in end-to-end approaches. We introduce a 21-task simulation
benchmark consisting of two challenging suites: LIBERO-Long++ and Ultra-Long. In these simulations, LiLo-VLA achieves a 69% average success rate, outperforming Pi0.5 by 41% and OpenVLA-OFT by 67%. Furthermore, real-world evaluations across 8 long-horizon tasks demonstrate an average success rate of 85%. Project page: https://yy-gx.github.io/LiLo-VLA/.
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All authors Yue Yang, Shuo Cheng, Yu Fang, Homanga Bharadhwaj, Mingyu Ding, Gedas Bertasius, Daniel Szafir
Computer Vision
Which Tool Response Should I Trust? Tool-Expertise-Aware Chest X-ray Agent with Multimodal Agentic Learning
Zheang Huai, Honglong Yang and Xiaomeng Li
AI
agents with tool-use capabilities show promise for integrating the domain expertise of various tools. In the medical field, however, tools are usually AI models that are inherently error-prone and can produce contradictory responses. Existing research on medical
agents lacks sufficient understanding of the tools' realistic reliability and thus cannot effectively resolve tool conflicts. To address this gap, this paper introduces a framework that enables an
agent to interact with tools and empirically learn their practical trustworthiness across different types of
multimodal queries via agentic learning. As a concrete instantiation, we focus on chest X-ray analysis and present a tool-expertise-aware chest X-ray
agent (TEA-CXA). When tool outputs disagree, the
agent experimentally accepts or rejects
multimodal tool results, receives rewards, and learns which tool to trust for each query type. Importantly, TEA-CXA extends existing codebases for reinforcement learning with multi-turn tool-calling that focus on textual inputs, to support
multimodal contexts effectively. In addition, we enhance the codebase for medical use scenarios by supporting multiple tool calls in one turn, parallel tool inference, and multi-image accommodation within a single user query. Our code framework is applicable to general medical research on multi-turn tool-calling reinforcement learning in
multimodal settings. Experiments show that TEA-CXA outperforms the state-of-the-art methods and a comprehensive set of baselines. Code will be released.
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Computer Vision · Cross-listed from cs.LG
WaterVIB: Learning Minimal Sufficient Watermark Representations via Variational Information Bottleneck
Haoyuan He, Yu Zheng, Jie Zhou et al. (+1 more)
Robust watermarking is critical for intellectual property protection, whereas existing methods face a severe vulnerability against regeneration-based AIGC attacks. We identify that existing methods fail because they entangle the watermark with high-frequency cover texture, which is susceptible to being rewritten during generative purification. To address this, we propose WaterVIB, a theoretically grounded framework that reformulates the encoder as an information sieve via the Variational Information Bottleneck. Instead of overfitting to fragile cover details, our approach forces the model to learn a Minimal Sufficient Statistic of the message. This effectively filters out redundant cover nuances prone to generative shifts, retaining only the essential signal invariant to regeneration. We theoretically prove that optimizing this bottleneck is a necessary condition for robustness against distribution-shifting attacks. Extensive experiments demonstrate that WaterVIB significantly outperforms state-of-the-art methods, achieving superior zero-shot resilience against unknown
diffusion-based editing.
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All authors Haoyuan He, Yu Zheng, Jie Zhou, Jiwen Lu
Computer Vision
AHAN: Asymmetric Hierarchical Attention Network for Identical Twin Face Verification
Hoang-Nhat Nguyen
Identical twin face verification represents an extreme fine-grained recognition challenge where even state-of-the-art systems fail due to overwhelming genetic similarity. Current face recognition methods achieve over 99.8% accuracy on standard
benchmarks but drop dramatically to 88.9% when distinguishing identical twins, exposing critical vulnerabilities in biometric security systems. The difficulty lies in learning features that capture subtle, non-genetic variations that uniquely identify individuals. We propose the Asymmetric Hierarchical
Attention Network (AHAN), a novel architecture specifically designed for this challenge through multi-granularity facial analysis. AHAN introduces a Hierarchical Cross-
Attention (HCA) module that performs multi-scale analysis on semantic facial regions, enabling specialized processing at optimal resolutions. We further propose a Facial Asymmetry
Attention Module (FAAM) that learns unique biometric signatures by computing cross-
attention between left and right facial halves, capturing subtle asymmetric patterns that differ even between twins. To ensure the network learns truly individuating features, we introduce Twin-Aware Pair-Wise Cross-
Attention (TA-PWCA), a training-only regularization strategy that uses each subject's own twin as the hardest possible distractor. Extensive experiments on the ND_TWIN
dataset demonstrate that AHAN achieves 92.3% twin verification accuracy, representing a 3.4% improvement over state-of-the-art methods.
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Computer Vision
Easy3E: Feed-Forward 3D Asset Editing via Rectified Voxel Flow
Shimin Hu, Yuanyi Wei, Fei Zha et al. (+2 more)
Existing 3D editing methods rely on computationally intensive scene-by-scene iterative optimization and suffer from multi-view inconsistency. We propose an effective and feed-forward 3D editing framework based on the TRELLIS generative backbone, capable of modifying 3D models from a single editing view. Our framework addresses two key issues: adapting training-free 2D editing to structured 3D representations, and overcoming the bottleneck of appearance fidelity in compressed 3D features. To ensure
geometric consistency, we introduce Voxel FlowEdit, an edit-driven flow in the sparse voxel latent space that achieves globally consistent 3D deformation in a single pass. To restore high-fidelity details, we develop a normal-guided single to multi-view generation module as an external appearance prior, successfully recovering high-frequency textures. Experiments demonstrate that our method enables fast, globally consistent, and high-fidelity 3D model editing.
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All authors Shimin Hu, Yuanyi Wei, Fei Zha, Yudong Guo, Juyong Zhang
Computer Vision
See It, Say It, Sorted: An Iterative Training-Free Framework for Visually-Grounded Multimodal Reasoning in LVLMs
Yongchang Zhang, Oliver Ma, Tianyi Liu et al. (+2 more)
Recent large
vision-
language models (LVLMs) have demonstrated impressive
reasoning ability by generating long chain-of-thought (CoT) responses. However, CoT
reasoning in
multimodal contexts is highly vulnerable to visual
hallucination propagation: once an intermediate
reasoning step becomes inconsistent with the visual evidence, subsequent steps-even if logically valid-can still lead to incorrect final answers. Existing solutions attempt to mitigate this issue by training models to "think with images" via reinforcement learning (RL). While effective, these methods are costly, model-specific, and difficult to generalize across architectures. Differently, we present a lightweight method that bypasses RL training and provides an iterative, training-free, plug-and-play framework for visually-grounded
multimodal reasoning. Our key idea is to supervise each
reasoning step at test time with visual evidence, ensuring that every decoded
token is justified by corresponding visual cues. Concretely, we construct a textual visual-evidence pool that guides the model's
reasoning generation. When existing evidence is insufficient, a visual decider module dynamically extracts additional relevant evidence from the image based on the ongoing
reasoning context, expanding the pool until the model achieves sufficient visual certainty to terminate
reasoning and produce the final answer. Extensive experiments on multiple LVLM backbones and
benchmarks demonstrate the effectiveness of our approach. Our method achieves 16.5%-29.5% improvements on TreeBench and 13.7% RH-AUC gains on RH-Bench, substantially reducing
hallucination rates while improving
reasoning accuracy without additional training.
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All authors Yongchang Zhang, Oliver Ma, Tianyi Liu, Guangquan Zhou, Yang Chen
Computer Vision
Unified Unsupervised and Sparsely-Supervised 3D Object Detection by Semantic Pseudo-Labeling and Prototype Learning
Yushen He, Lei Zhao and Weidong Chen
3D object detection is essential for autonomous driving and robotic perception, yet its reliance on large-scale manually annotated data limits scalability and adaptability. To reduce annotation dependency, unsupervised and sparsely-supervised paradigms have emerged. However, they face intertwined challenges: low-quality pseudo-labels, unstable feature mining, and a lack of a unified training framework. This paper proposes SPL, a unified training framework for both unsupervised and sparsely-supervised 3D object detection via \underline{S}emantic \underline{P}seudo-labeling and prototype \underline{L}earning. SPL first generates high-quality pseudo-labels by integrating image semantics, point cloud geometry, and temporal cues, producing both 3D bounding boxes for dense objects and 3D point labels for sparse ones. These pseudo-labels are not used directly but as probabilistic priors within a novel, multi-stage prototype learning strategy. This strategy stabilizes feature representation learning through
memory-based initialization and momentum-based prototype updating, effectively mining features from both labeled and unlabeled data. Extensive experiments on KITTI and nuScenes
datasets demonstrate that SPL significantly outperforms state-of-the-art methods in both settings. Our work provides a robust and generalizable solution for learning 3D object detectors with minimal or no manual annotations. Our code is available at https://github.com/TossherO/SPL.
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Computer Vision · Cross-listed from eess.IV
Perceptual Quality Optimization of Image Super-Resolution
Wei Zhou, Yixiao Li, Hadi Amirpour et al. (+4 more)
Single-image super-resolution (SR) has achieved remarkable progress with deep learning, yet most approaches rely on distortion-oriented losses or heuristic perceptual priors, which often lead to a trade-off between fidelity and visual quality. To address this issue, we propose an Efficient Perceptual Bi-directional
Attention Network (Efficient-PBAN) that explicitly optimizes SR towards human-preferred quality. Unlike patch-based quality models, Efficient-PBAN avoids extensive patch sampling and enables efficient image-level perception. The proposed framework is trained on our self-constructed SR quality
dataset that covers a wide range of state-of-the-art SR methods with corresponding human opinion scores. Using this
dataset, Efficient-PBAN learns to predict perceptual quality in a way that correlates strongly with subjective judgments. The learned metric is further integrated into SR training as a differentiable perceptual loss, enabling closed-loop
alignment between reconstruction and perceptual assessment. Extensive experiments demonstrate that our approach delivers superior perceptual quality. Code is publicly available at https://github.com/Lighting-YXLI/Efficient-PBAN.
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All authors Wei Zhou, Yixiao Li, Hadi Amirpour, Xiaoshuai Hao, Jiang Liu, Peng Wang, Hantao Liu
Computer Vision
Automatic Map Density Selection for Locally-Performant Visual Place Recognition
Somayeh Hussaini, Tobias Fischer and Michael Milford
A key challenge in translating Visual Place Recognition (VPR) from the lab to long-term deployment is ensuring a priori that a system can meet user-specified performance requirements across different parts of an environment, rather than just on average globally. One critical mechanism for controlling this local performance is the density of the reference mapping database, yet this factor is largely neglected in existing work, where fixed, engineering-driven sampling densities based on sensors, storage, or GPS frequency are typically used. In this paper, we propose a VPR mapping approach that uses two reference traverses from the operating environment to automatically select an appropriate map density satisfying two user-defined requirements: (1) a target Local Recall@1 level, and (2) the proportion of the operational environment over which it must be met or exceeded, which we term the Recall Achievement Rate (RAR). Our approach uses spatial consistency and coherence features from reference-to-reference matches at multiple map densities to estimate the density needed to meet these targets on unseen deployment data. Through experiments across two VPR methods and the Nordland and Oxford RobotCar
benchmarks, we show that our system consistently meets or exceeds the target Local Recall@1 over at least the user-specified proportion of the environment. Comparisons with alternative baselines show that it reliably selects an appropriate operating point in map density, avoiding unnecessarily dense maps. Finally, ablations evaluate sensitivity to reference traversal choice and segment length, and our analysis reveals that conventional global Recall@1 is a poor predictor of the often more operationally meaningful RAR metric.
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Computer Vision
Adversarial Robustness of Deep Learning-Based Thyroid Nodule Segmentation in Ultrasound
Nicholas Dietrich and David McShannon
Introduction: Deep learning-based
segmentation models are increasingly integrated into clinical imaging workflows, yet their robustness to
adversarial perturbations remains incompletely characterized, particularly for ultrasound images. We evaluated
adversarial attacks and inference-time defenses for thyroid nodule
segmentation in B-mode ultrasound. Methods: Two black-box
adversarial attacks were developed: (1) Structured Speckle Amplification Attack (SSAA), which injects boundary-targeted noise, and (2) Frequency-Domain Ultrasound Attack (FDUA), which applies bandpass-filtered phase perturbations in the
Fourier domain. Three inference-time mitigations were evaluated on
adversarial images: randomized preprocessing with test-time augmentation, deterministic input denoising, and
stochastic ensemble inference with consistency-aware aggregation. Experiments were conducted on a U-Net
segmentation model trained on cine-clips from a database of 192 thyroid nodules. Results: The baseline model achieved a mean Dice similarity coefficient (DSC) of 0.76 (SD 0.20) on unperturbed images. SSAA reduced DSC by 0.29 (SD 0.20) while maintaining high visual similarity (SSIM = 0.94). FDUA resulted in a smaller DSC reduction of 0.11 (SD 0.09) with lower visual fidelity (SSIM = 0.82). Against SSAA, all three defenses significantly improved DSC after correction, with deterministic denoising showing the largest recovery (+0.10, p < 0.001), followed by randomized preprocessing (+0.09, p < 0.001), and
stochastic ensemble inference (+0.08, p = 0.002). No defense achieved statistically significant improvement against FDUA. Conclusion: Spatial-domain
adversarial perturbations in ultrasound
segmentation showed partial mitigation with input preprocessing, whereas frequency-domain perturbations were not mitigated by the defenses, highlighting modality-specific challenges in
adversarial robustness evaluation.
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