Egocentric point tracking aims to localize points on object surfaces from a first-person perspective and serves as a critical step toward embodied intelligence. Recent methods rely on video input, tracking query points through feature matching across consecutive frames. However, these methods struggle in highly dynamic settings—a common challenge in first-person perspectives, where the head-mounted camera undergoes frequent and abrupt rotations, resulting in high angular velocities, motion blur, and large inter-frame displacements. In contrast, event cameras capture motion at microsecond temporal resolution, naturally avoiding blur and delivering low-latency, high-fidelity cues crucial for egocentric point tracking. Moreover, rapid egocentric motion disrupts local smoothness, breaking the assumption that spatially adjacent regions share similar motion. Event dynamics expose global motion trends, guiding coherent modeling and consistent feature flow. Therefore, this paper proposes a mamba-based tracking framework that constructs feature modeling paths aligned with the dominant motion trend extracted from events, and modulates feature propagation along these paths based on local motion intensity, enhancing stability by suppressing unreliable signals and emphasizing consistent cues. Additionally, a motion-adaptive suppression module enhances temporal robustness by adaptively suppressing correlation features based on motion intensity variations, mitigating the effects of intensity fluctuations and partial observability. To facilitate research in this domain, a multimodal dataset named DVS-EgoPoints with both events and videos for egocentric point tracking is collected. Experiments on the DVS-EgoPoints dataset and a simulation benchmark demonstrate superior performance over state-of-the-art methods, especially under challenging motion and occlusion conditions.
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With the advancement of face recognition (FR) systems, privacy-preserving face recognition (PPFR) systems have gained popularity for their accurate recognition, enhanced facial privacy protection, and robustness to various attacks. However, there are limited studies to further verify privacy risks by reconstructing realistic high-resolution face images from embeddings of these systems, especially for PPFR. In this work, we propose the face embedding mapping (FEM), a general framework that explores Kolmogorov-Arnold Network (KAN) for conducting the embedding-to-face attack by leveraging pre-trained Identity-Preserving diffusion model against state-of-the-art (SOTA) FR and PPFR systems. Based on extensive experiments, we verify that reconstructed faces can be used for accessing other real-word FR systems. Besides, the proposed method shows the robustness in reconstructing faces from the partial and protected face embeddings. Moreover, FEM can be utilized as a tool for evaluating safety of FR and PPFR systems in terms of privacy leakage. All images used in this work are from public datasets.
Universal Compressed Image Restoration via Codec-Aware Conditioning with Reinforcement Learning
PDF ↗We address the task of universal compressed image restoration, which involves recovering high-quality images degraded by a wide range of codecs and compression levels. While prior methods have made significant progress, they typically target specific degradation types and struggle to generalize across both traditional and learning-based codecs. To overcome this limitation, we propose a unified framework that leverages codec-aware conditioning and reinforcement learning-based fine-tuning. Specifically, we introduce a conditioning module that encodes both codec type and compression level, enabling the restoration network to adapt its behavior to diverse degradation settings. To further improve generalization, we incorporate reward-based objectives during fine-tuning, providing complementary signals that enhance training across both conventional and learned compression schemes. Experimental results demonstrate the effectiveness of our method in restoring images across a wide range of compression artifacts and scenarios.
Implicit Neural Representations (INRs) have revolutionized signal processing and computer vision by modeling signals as continuous, differentiable functions parameterized by neural networks. However, INRs are prone to the spectral bias problem, limiting their ability to retain high-frequency information, and often struggle with noise robustness. Motivated by recent trends in iterative refinement processes, we propose Iterative Implicit Neural Representations (I-INRs). This novel plug-and-play framework iteratively refines signal reconstructions to restore high-frequency details, improve noise robustness, and enhance generalization, ultimately delivering superior reconstruction quality. I-INRs integrate seamlessly into existing INR architectures with only a 0.5–2% increase in parameters. During reconstruction, the iterative refinement adds just 0.8–1.6% additional FLOPs over the baseline while delivering a substantial performance boost of up to +2.0 PSNR. Extensive experiments demonstrate that I-INRs consistently outperform WIRE, SIREN, and Gauss across various computer vision tasks, including image fitting, image denoising, and object occupancy prediction.
While Large Vision Language Models (LVLMs) are increasingly deployed in real-world applications, their ability to interpret abstract visual inputs remains limited. Specifically, they struggle to comprehend hand-drawn sketches, a modality that offers an intuitive means of expressing concepts that are difficult to describe textually. We identify the primary bottleneck as the absence of a large-scale dataset that jointly models sketches, photorealistic images, and corresponding natural language instructions. To address this, we present two key contributions: (1) a new, large-scale dataset of image-sketch-instruction triplets designed to facilitate both pretraining and instruction tuning, and (2) O3SLM, an LVLM trained on this dataset. Comprehensive evaluations on multiple sketch-based tasks: (a) object localization, (b) counting, (c) image retrieval i.e., (SBIR and fine-grained SBIR), and (d) visual question answering (VQA); while incorporating the three existing sketch datasets, namely QuickDraw!, Sketchy, and Tu-Berlin, along with our generated SketchVCL dataset, show that O3SLM achieves state-of-the-art performance, substantially outperforming existing LVLMs in sketch comprehension and reasoning.
3D Gaussian Splatting has emerged as a transformative technique in novel view synthesis, primarily due to its high rendering speed and photorealistic fidelity. However, its memory footprint scales rapidly with scene complexity, often reaching several gigabytes. Existing methods address this issue by introducing compression strategies that exploit primitive level redundancy through similarity detection and quantization. We aim to surpass the compression limits of such methods by incorporating symmetry aware techniques, specifically targeting mirror symmetries to eliminate redundant primitives. We propose a novel compression framework, SymGS, introducing learnable mirrors into the scene, thereby eliminating local and global reflective redundancies for compression. Our framework functions as a plug and play enhancement to state of the art compression methods, (e.g. HAC) to achieve further compression. Compared to HAC, we achieve 1.66x compression across benchmark datasets (upto 3x on large scale scenes). On an average, SymGS enables 108x compression over 3DGS, while preserving rendering quality.
RouterNet: Hierarchical Point Routing Network for Robust Vertebral Landmark Localization on AP X-ray Images
PDF ↗Locating vertebral landmarks on anteroposterior (AP) X-ray images is challenging due to the tissue overlap. Despite the great progress of heatmap-based methods, they often predict missing/false points, which are intolerable in the downstream applications like scoliosis assessment. In this paper, we instead modernize the classic point-regression scheme, and propose a novel model termed RouterNet to locate the 68 vertebral landmarks completely and accurately. RouterNet starts from an initial root point, and then gradually routes it onto more and more points with finer and finer semantics. RouterNet naturally couples such point routing process with its hierarchical and multi-scale feature learning. That is, lower-scale feature maps are utilized to regress points with coarser semantics, and the regressed points pilot a more focused local feature extraction on the next higher-scale map to route onto their subsequent positions with finer semantics. With this divide-and-conquer, RouterNet alleviates the task difficulty, and can robustly localize by routing from the whole spinal center to 17 vertebral centers, and further to their 68 corner points. Extensive and comprehensive experiments on both public and private datasets demonstrate our superior performance over other state-of-the-arts, by decreasing NMSE by 73.8% for landmark localization, and SMAPE by 14.8% for the downstream scoliosis assessment.
3D Gaussian splatting (3DGS) has demonstrated impressive 3D reconstruction performance with explicit scene representations. Given the widespread application of 3DGS in 3D reconstruction and generation tasks, there is an urgent need to protect the copyright of 3DGS assets. However, existing copyright protection techniques for 3DGS overlook the usability of 3D assets, posing challenges for practical deployment. Here we describe splats in splats, the first 3DGS steganography framework that embeds 3D content in 3DGS itself without modifying any attributes. To achieve this, we take a deep insight into spherical harmonics (SH) and devise an importance-graded SH coefficient encryption strategy to embed the hidden SH coefficients. Furthermore, we employ a convolutional autoencoder to establish a mapping between the original Gaussian primitives' opacity and the hidden Gaussian primitives' opacity. Extensive experiments indicate that our method significantly outperforms existing 3D steganography techniques, with 5.31% higher scene fidelity and 3x faster rendering speed, while ensuring security, robustness, and user experience.
Continual Semantic Segmentation (CSS) requires learning new classes without forgetting previously acquired knowledge, addressing the fundamental challenge of catastrophic forgetting in dense prediction tasks. However, existing CSS methods typically employ single-stage encoder-decoder architectures where segmentation masks and class labels are tightly coupled, leading to interference between old and new class learning and suboptimal retention-plasticity balance. We introduce DecoupleCSS, a novel two-stage framework for CSS. By decoupling class-aware detection from class-agnostic segmentation, DecoupleCSS enables more effective continual learning, preserving past knowledge while learning new classes. The first stage leverages pre-trained text and image encoders, adapted using LoRA, to encode class-specific information and generate location-aware prompts. In the second stage, the Segment Anything Model (SAM) is employed to produce precise segmentation masks, ensuring that segmentation knowledge is shared across both new and previous classes. This approach improves the balance between retention and adaptability in CSS, achieving state-of-the-art performance across a variety of challenging tasks.
OWL: Unsupervised 3D Object Detection by Occupancy Guided Warm-up and Large Model Priors Reasoning
PDF ↗Unsupervised 3D object detection leverages heuristic algorithms to discover potential objects, offering a promising route to reduce annotation costs in autonomous driving. Existing approaches mainly generate pseudo labels and refine them through self-training iterations. However, these pseudo-labels are often incorrect at the beginning of training, resulting in misleading the optimization process. Moreover, effectively filtering and refining them remains a critical challenge. In this paper, we propose $\textbf{OWL}$ for unsupervised 3D object detection by occupancy guided warm-up and large-model priors reasoning. OWL first employs an Occupancy Guided Warm-up (OGW) strategy to initialize the backbone weight with spatial perception capabilities, mitigating the interference of incorrect pseudo-labels on network convergence. Furthermore, OWL introduces an Instance-Cued Reasoning (ICR) module that leverages the prior knowledge of large models to assess pseudo-label quality, enabling precise filtering and refinement. Finally, we design a WAS (Weight-adapted Self-training) strategy to dynamically re-weight pseudo-labels, improving the performance through self-training. Extensive experiments on Waymo Open Dataset (WOD) and KITTI demonstrate that OWL outperforms state-of-the-art unsupervised methods by over 15.0\% mAP, revealing the effectiveness of our method.
In this paper, we present Ev-iCRF, a novel self-supervised pipeline for high dynamic range (HDR) image reconstruction from a single-exposure low dynamic range (LDR) image, guided by asynchronous event streams generated by a bio-inspired event camera. The highlight of Ev-iCRF lies in its formulation of the inverse camera response function (iCRF) based on Event-LDR Correspondence. By leveraging the HDR properties of event data, the method enables direct iCRF estimation, offering a new perspective for event-guided HDR imaging. The pipeline is trained in a self-supervised manner using formulation-driven iCRF estimation loss and refinement loss, without the need for synchronized HDR supervision. Ev-iCRF adopts a two-stage coarse-to-fine reconstruction pipeline, allowing effective fusion of features from both LDR image and event data. The event information is used to optimize the iCRF, enabling accurate HDR reconstruction from LDR inputs. We evaluate Ev-iCRF on real-world datasets, and results show that it outperforms state-of-the-art methods in HDR reconstruction accuracy. Moreover, the reconstructed images demonstrate improved texture fidelity and structural detail.
Person re-identification (Re-ID) under extremely low-light conditions suffers from severe image degradation, which significantly impairs the extraction of identity-discriminative features. Existing methods struggle to recover semantic information that is obscured under poor illumination. To better understand this problem, we conduct a comprehensive analysis of the semantic modeling behavior of Re-ID models in low-light settings. For the first time, we investigate the norm distributions of Query (Q), Key (K), and Value (V) vectors within the attention module and observe that, as illumination decreases, the norm of Query vectors in pedestrian regions drops significantly. This leads to dispersed attention and degraded feature representations. To address this issue, we propose a novel framework named Norm-Ratio Attention and Semantic Recovery Distillation Network (NRSRD), which consists of two key components: a Norm-Ratio Attention Module (NRA) and a Semantic Recovery Distillation Module (SRD). The former dynamically adjusts attention responses based on the ratio of K/Q vector norms, enhancing structural region perception while suppressing background interference. The latter transfers discriminative semantic knowledge from high-illumination auxiliary data to the low-light model, compensating for the semantic degradation caused by poor lighting. Extensive experiments on multiple publicly available low-light Re-ID benchmarks demonstrate the effectiveness and superiority of the proposed method.
Deep unrolling models (DUMs) have shown great poten-tial in sparse-view CT reconstruction by combining itera-tive optimization and deep learning. However, most DUMsinsufficiently account for physical degradation from sparse-view imaging, leading to slow convergence and persistentartifacts. To address this, we propose PAUM, a Physics-Aware Accelerated Unrolling Model explicitly incorporatingCT imaging physics into the iterative reconstruction. PAUMfirst introduces a Dual-Domain Physics-Aware Extrapolation(DDPE) module. By modeling dual-domain degradations, itperforms row-wise extrapolation in the sinogram domain toimprove missing view recovery, and pixel-wise extrapolationin the image domain to address spatially variant degradationfrom incomplete backprojection. This physics-aware extrap-olation aligns optimization dynamics with underlying physi-cal imaging degradation, significantly enhances structural up-dates, thereby accelerating convergence. Subsequently, wedevelop a lightweight Block-Attention Deformable Regu-larization Network (BDRN), leveraging deformable convo-lutions and block-wise attention to model spatially variantand structured artifact physical characteristics. This enablesspatially adaptive regularization on extrapolated results, ef-fectively improving reconstruction quality. Extensive exper-iments demonstrate PAUM achieves over 1dB improvementcompared to SOTA methods, while reducing iteration countby 50%.
3D semantic occupancy prediction offers a nuanced representation of the surrounding environment, which is crucial for ensuring the safety of autonomous driving. However, fine-grained scene representations inevitably result in cubic growth in data scale, which imposes substantial demands on model architecture and computational complexity, especially in high-resolution scenarios. Existing approaches for handling high-resolution scenes typically obtain fine-grained features by grid sampling on low-resolution feature map, resulting in limited sparsity and insufficient feature interaction. This paper presents a framework leveraging SParse representation and SCalable feature interaction to address the aforementioned challenges, called SPSC. Specifically, we maintain sparsity by progressively pruning unoccupied queries during the coarse-to-fine process, thereby reducing the scale of data that the model needs to handle. Subsequently, we introduce query serialization, which transforms queries into an ordered sequence while preserving their spatial structure, This enables fine-grained feature interaction while maintaining linear computational complexity and a larger receptive field. Without complex architectural designs, SPSC significantly outperforms SOTA approaches, relatively enhances the mIoU by 12.0%, 11.0% and 4.8% on nuScenes-Occupancy dataset under the muli-modal, LiDAR and camera settings, respectively.
HyperSign: Hierarchical Hypergraph-based Co-occurrence Modeling for Sign Language Recognition and Translation
PDF ↗Effectively capturing co-occurrence signals, such as hand shapes, facial expressions, and body postures, is critical for semantic understanding in sign language recognition (SLR) and translation (SLT). Although skeleton data offer greater efficiency and robustness than RGB inputs, existing methods typically rely on pairwise graph structures, limiting their ability to model complex high-order interactions across body regions. To address this limitation, we propose HyperSign, a hierarchical hypergraph neural network that systematically captures high-order co-occurrence patterns among diverse body parts. The Co-occurrence Graph Perception Module jointly learns relational structures via three complementary pathways: (1) traditional graph convolutions for modeling physical joint connections, (2) dynamic geometric hypergraphs constructed via k-nearest neighbors to encode local spatial patterns, and (3) soft hypergraphs generated by learnable prototypes to reveal latent semantic associations. To further enhance structural modeling and semantic consistency, a Meta-Part Hypergraph Fusion Module abstracts feature streams from the hands, face, and body into unified hypergraph nodes, while leveraging empirically derived co-occurrence priors to model high-order cross-part dependencies. Moreover, an uncertainty-aware collaborative distillation mechanism guides the model to focus on critical body regions. Extensive experiments on standard SLR and SLT benchmarks (e.g., PHOENIX-2014, PHOENIX-2014T, and CSL-Daily) demonstrate that HyperSign not only outperforms existing skeleton-based approaches in both speed and accuracy but also achieves competitive or superior results compared to several state-of-the-art RGB-based methods across multiple evaluation metrics.
PhysPatch: A Physically Realizable and Transferable Adversarial Patch Attack for Multimodal Large Language Models-based Autonomous Driving Systems
PDF ↗Multimodal Large Language Models (MLLMs) are becoming integral to autonomous driving (AD) systems due to their strong vision-language reasoning capabilities. However, MLLMs are vulnerable to adversarial attacks—particularly adversarial patch attacks—which can pose serious threats in real-world scenarios. Existing patch-based attack methods are primarily designed for object detection models. Due to the more complex architectures and strong reasoning capabilities of MLLMs, these approaches perform poorly when transferred to MLLM-based systems. To address these limitations, we propose PhysPatch, a physically realizable and transferable adversarial patch framework tailored for MLLM-based AD systems. PhysPatch jointly optimizes patch location, shape, and content to enhance attack effectiveness and real-world applicability. It introduces a semantic-based mask initialization strategy for realistic placement, an SVD-based local alignment loss with patch-guided crop-resize to improve transferability, and a potential field-based mask refinement method. Extensive experiments across open-source, commercial, and reasoning-capable MLLMs demonstrate that PhysPatch significantly outperforms state-of-the-art (SOTA) methods in steering MLLM-based AD systems toward target-aligned perception and planning outputs. Moreover, PhysPatch consistently places adversarial patches in physically feasible regions of AD scenes, ensuring strong real-world applicability and deployability.
Recent advances in rotation-invariant (RI) learning for 3D point clouds typically replace raw coordinates with handcrafted RI features to ensure robustness under arbitrary rotations. However, these approaches often suffer from the loss of global pose information, making them incapable of distinguishing geometrically similar but spatially distinct structures. We identify that this limitation stems from the restricted receptive field in existing RI methods, leading to Wing–tip feature collapse, a failure to differentiate symmetric components (e.g., left and right airplane wings) due to indistinguishable local geometries. To overcome this challenge, we introduce the Shadow-informed Pose Feature (SiPF), which augments local RI descriptors with a globally consistent reference point (referred to as the “shadow”) derived from a learned shared rotation. This mechanism enables the model to preserve global pose awareness while maintaining rotation invariance. We further propose Rotation-invariant Attention Convolution (RIAttnConv), an attention-based operator that integrates SiPFs into the feature aggregation process, thereby enhancing the model’s capacity to distinguish structurally similar components. Additionally, we design a task-adaptive shadow locating module based on the Bingham distribution over unit quaternions, which dynamically learns the optimal global rotation for constructing consistent shadows. Extensive experiments on 3D classification and part segmentation benchmarks demonstrate that our approach substantially outperforms existing RI methods, particularly in tasks requiring fine-grained spatial discrimination under arbitrary rotations.
Recovering precise surface geometry from corrupted point clouds remains a core challenge in 3D vision. Although existing denoising techniques achieve remarkable success, balancing noise removal with preserving intricate geometric details continues to pose difficulties. A critical limitation of current methods is that their adaptive feature aggregation mechanisms rely heavily on intermediate network features that have not been explicitly regularized, resulting in unstable guidance signals. This instability restricts the capability of the network to optimally differentiate true geometric details from noise. To overcome this limitation, we propose a novel deep learning framework that explicitly learns structured representations as robust priors to guide feature refinement. Our approach first derives a set of representative local structural primitives from input features by means of a learned codebook. This learned structured representation then serves as a robust conditional signal, directing a subsequent feature fusion mechanism to dynamically aggregate information in a structure-aware manner, thereby more effectively discerning noise and meticulously reconstructing geometric details. Extensive experiments on several benchmarks have demonstrated the superiority of our framework over existing advanced techniques in terms of detail preservation and noise suppression.
Point Cloud Segmentation of Integrated Circuits Package Substrates Surface Defects Using Causal Inference: Dataset Construction and Methodology
PDF ↗The effective segmentation of 3D data is crucial for a wide range of industrial applications, especially for detecting subtle defects in the field of integrated circuits (IC). Ceramic package substrates (CPS), as an important electronic material, are essential in IC packaging owing to their superior physical and chemical properties. However, the complex structure and minor defects of CPS, along with the absence of a publically available dataset, significantly hinder the development of CPS surface defect detection. In this study, we construct a high-quality point cloud dataset for 3D segmentation of surface defects in CPS, i.e., CPS3D-Seg, which has the best point resolution and precision compared to existing 3D industrial datasets. CPS3D-Seg consists of 1300 point cloud samples under 20 product categories, and each sample provides accurate point-level annotations. Meanwhile, we conduct a comprehensive benchmark based on SOTA point cloud segmentation algorithms to validate the effectiveness of CPS3D-Seg. Additionally, we propose a novel 3D segmentation method based on causal inference (CINet), which quantifies potential confounders in point clouds through Structural Refine (SR) and Quality Assessment (QA) Modules. Extensive experiments demonstrate that CINet significantly outperforms existing algorithms in both mIoU and accuracy.
Infrared and Visible Image Fusion (IVIF) integrates complementary information from distinct modalities to enhance image quality. However, the effectiveness declines under unseen conditions such as novel weather or scenes, due to domain shifts primarily from variations of data distribution in the visible modality, while the infrared modality remains relatively stable. To overcome domain shifts caused by the imbalance between modalities during image fusion, we propose a Domain Adaptation Guided Infrared and Visible Image Fusion method, termed DAFusion, leveraging a dual-rank domain adapter to enable fast adaptation to diverse adverse conditions during image fusion. Specifically, trainable low-rank and high-rank embedding spaces are respectively used to capture knowledge common across domains (domain-shared) and those unique to target domains (domain-specific). To leverage the dual-rank adapter more effectively, we develop a homeostatic knowledge allotment strategy to integrate the distinct types of knowledge dynamically based on the uncertainty value of target domains. Since domain adaptation typically optimizes for feature alignment across domains and emphasizes invariance rather than preserving specific cues critical for image fusion, while the fusion objective requires retaining discriminative and complementary features, a conflict between the two modules appears. To reconcile this, we further adopt a bi-level optimization framework that structurally decouples the two objectives, enabling the fusion module to steer the adaptation process while benefiting in return from domain-aligned representations. Experimental results on three benchmarks demonstrate that our method significantly outperforms state-of-the-art approaches, achieving both an enhancement in fusion quality and an improvement on subsequent high-level tasks.