CVPR 2025 论文列表

Deep learning techniques have enabled vast improvements in computer vision technologies. Nevertheless, these models are vulnerable to adversarial patch attacks which catastrophically impair performance. The physically realizable nature of these attacks calls for certifiable defenses, which feature provable guarantees on robustness. While certifiable defenses have been successfully applied to single-label classification, limited work has been done for multi-label classification. In this work, we present PatchDEMUX, a certifiably robust framework for multi-label classifiers against adversarial patches. Our approach is a generalizable method which can extend any existing certifiable defense for single-label classification; this is done by considering the multi-label classification task as a series of isolated binary classification problems to provably guarantee robustness. Furthermore, in the scenario where an attacker is limited to a single patch we propose an additional certification procedure that can provide tighter robustness bounds. Using the current state-of-the-art (SOTA) single-label certifiable defense PatchCleanser as a backbone, we find that PatchDEMUX can achieve non-trivial robustness on the MS-COCO and PASCAL VOC datasets while maintaining high clean performance

Natural Language-based Egocentric Task Verification (NLETV) aims to equip agents to determine if operation flows of procedural tasks in egocentric videos align with natural language instructions. Describing rules with natural language provides generalizable applications, but also raises cross-modal heterogeneity and hierarchical misalignment challenges. In this paper, we proposed a novel approach termed Procedural Heterogeneous Graph Completion (PHGC), which addresses these challenges with heterogeneous graphs representing the logic in rules and operation flows. Specifically, our PHGC method mainly consists of three key components: (1) Heterogeneous Graph Construction module that defines objective states and operation flows as vertices, with temporal and sequential relations as edges. (2) Cross-Modal Path Finding module that aligns semantic relations between hierarchical video and text elements. (3) Discriminative Entity Representation module excavates hidden entities that integrate general logical relations and discriminative cues to reveal final verification results. Additionally, we further constructed a new dataset called CSV-NL comprised of realistic videos. Extensive experiments on the two benchmark datasets covering both digital and physical scenarios, i.e., EgoTV and CSV-NL, demonstrate that our proposed PHGC establishes state-of-the-art performance across different settings. Our code and dataset are available at https://github.com/XunCHN/PHGC.

Robotic manipulation based on visual observations and natural language instructions is a long-standing challenge in robotics. Yet prevailing approaches model action distribution by adopting explicit or implicit representations, which often struggle to achieve a trade-off between accuracy and efficiency. In response, we propose PDFactor, a novel framework that models action distribution with a hybrid triplane representation. In particular, PDFactor decomposes 3D point cloud into three orthogonal feature planes and leverages a tri-perspective view transformer to produce dense cubic features as a latent diffusion field aligned with observation space representing 6-DoF action probability distribution at an arbitrary location. We employ a small denoising network conceptually as both a parameterized loss function measuring the quality of the learned latent features and an action gradient decoder to sample actions from the latent diffusion field during inference. This design enables our PDFactor to benefit from spatial awareness of explicit representation and arbitrary resolution of implicit representation, rendering it with manipulation accuracy, inference efficiency, and model scalability. Experiments demonstrate that PDFactor outperforms state-of-the-art approaches across a diverse range of manipulation tasks in RLBench simulation. Moreover, PDFactor can effectively learn multi-task policies from a limited number of human demonstrations, achieving promising accuracy in a variety of real-world manipulation tasks.

Panoramic imagery, with its 360deg field of view, offers comprehensive information to support Multi-Object Tracking (MOT) in capturing spatial and temporal relationships of surrounding objects. However, most MOT algorithms are tailored for pinhole images with limited views, impairing their effectiveness in panoramic settings. Additionally, panoramic image distortions, such as resolution loss, geometric deformation, and uneven lighting, hinder direct adaptation of existing MOT methods, leading to significant performance degradation. To address these challenges, we propose OmniTrack, an omnidirectional MOT framework that incorporates Tracklet Management to introduce temporal cues, FlexiTrack Instances for object localization and association, and the CircularStatE Module to alleviate image and geometric distortions. This integration enables tracking in panoramic field-of-view scenarios, even under rapid sensor motion. To mitigate the lack of panoramic MOT datasets, we introduce the QuadTrack dataset--a comprehensive panoramic dataset collected by a quadruped robot, featuring diverse challenges such as panoramic fields of view, intense motion, and complex environments. Extensive experiments on the public JRDB dataset and the newly introduced QuadTrack benchmark demonstrate the state-of-the-art performance of the proposed framework. OmniTrack achieves a HOTA score of 26.92% on JRDB, representing an improvement of 3.43%, and further achieves 23.45% on QuadTrack, surpassing the baseline by 6.81%. The established dataset and source code are available at https://github.com/xifen523/OmniTrack.

This paper investigates the research task of reconstructing the 3D clothed human body from a monocular image. Due to the inherent ambiguity of single-view input, existing approaches leverage pre-trained SMPL(-X) estimation models or generative models to provide auxiliary information for human reconstruction. However, these methods capture only the general human body geometry and overlook specific geometric details, leading to inaccurate skeleton reconstruction, incorrect joint positions, and unclear cloth wrinkles. In response to these issues, we propose a multi-level geometry learning framework. Technically, we design three key components: skeleton-level enhancement, joint-level augmentation, and wrinkle-level refinement modules. Specifically, we effectively integrate the projected 3D Fourier features into a Gaussian reconstruction model, introduce perturbations to improve joint depth estimation during training, and refine the human coarse wrinkles by resembling the de-noising process of the diffusion model. Extensive quantitative and qualitative experiments on two test sets show the superior performance of our approach compared to state-of-the-art (SOTA) methods.

The rapid development of spatial transcriptomics (ST) allows researchers to measure the spatial-level gene expression in tissues. Although powerful, the cost for collecting the ST data is expensive, and thus several studies aim to predict gene expression in ST by utilizing their corresponding H/E stained pathology images. The existing ST based gene expression prediction models either adopt the pre-trained networks or rely on the handcrafted features to describe the pathology images, which still lack a systematic way to combine them together to define a spot-level representation that can reflect the topological profiles of different spots. On the other hand, all the ST based gene prediction models treat the prediction task for each gene independently, which overlook the fact that the exploration of potential interrelationships among them can help improve the prediction performance for individual genes.To address the above issues, we propose a multi-modal topology-embedded graph learning algorithm guided by prior Gene Ontology similarity information (i.e., M2TGLGO) to predict the spatial resolved genes from pathology image. Specifically, M2TGLGO co-learns the image representation of different spots from both deep and handcrafted features by considering the within-modal and inter-modal interactions. Next, to keep the topological structure among different spots, a spatial-oriented ranking module is also incorporated to preserve their neighborhood similarity information. Finally, we present a Gene Ontology knowledge guided graph neural network for simultaneously predicting multiple gene expressions by considering their functional associations. We evaluate our method on three public available ST datasets, the experimental results show the effectiveness of our M2TGLGO in comparison with the existing studies.

As a fundamental visual task, optical flow estimation has widespread applications in computer vision. However, it faces significant challenges under adverse lighting conditions, where low texture and noise make accurate optical flow estimation particularly difficult.In this paper, we propose an optical flow method that employs implicit image enhancement through multi-modal synergistic training. To supplement the scene information missing in the original low-quality image, we utilize a high-low frequency feature enhancement network. The enhancement network is implicitly guided by multi-modal data and the specific subsequent tasks, enabling the model to learn multi-modal knowledge that enhances feature information suitable for optical flow estimation during inference. By using RGBD multi-modal data, the proposed method avoids the reliance on the images captured from the same view, a common limitation in traditional image enhancement methods.During training, the encoded features extracted from the enhanced images are synergistically supervised by features from the RGBD fusion as well as by the optical flow task.Experiments conducted on both synthetic and real datasets demonstrate that the proposed method significantly improves performance on public datasets.

Place recognition (PR) aims at retrieving the query place from a database and plays a crucial role in various applications, including navigation, autonomous driving, and augmented reality. While previous multi-modal PR works have mainly focused on the same-view scenario in which ground-view descriptors are matched with a database of ground-view descriptors during inference, the multi-modal cross-view scenario, in which ground-view descriptors are matched with aerial-view descriptors in a database, remains under-explored. We propose AGPlace, a model that effectively integrates information from multi-modal ground sensors (cameras and LiDARs) to achieve accurate aerial-ground PR. AGPlace achieves effective aerial-ground cross-view PR by leveraging a manifold-based neural ordinary differential equation (ODE) framework with a multi-domain alignment loss. It outperforms existing state-of-the-art cross-view PR models on large-scale datasets. As most existing PR models are designed for ground-ground PR, we adapt these baselines into our cross-view pipeline. Experiments demonstrate that this direct adaptation performs worse than our overall model architecture AGPlace. AGPlace represents a significant advancement in multi-modal aerial-ground PR, with promising implications for real-world applications.

Existing methods enhance the training of detection transformers by incorporating an auxiliary one-to-many assignment. In this work, we treat the model as a multi-task framework, simultaneously performing one-to-one and one-to-many predictions. We investigate the roles of each component in the transformer decoder across these two training targets, including self-attention, cross-attention, and feed-forward network. Our empirical results demonstrate that any independent component in the decoder can effectively learn both targets simultaneously, even when other components are shared. This finding leads us to propose a multi-route training mechanism, featuring a primary route for one-to-one prediction and two auxiliary training routes for one-to-many prediction. We enhance the training mechanism with a novel instructive self-attention that dynamically and flexibly guides object queries for one-to-many prediction. The auxiliary routes are removed during inference, ensuring no impact on model architecture or inference cost. We conduct extensive experiments on various baselines, achieving consistent improvements as shown in Fig. 1. Project page: https://visual-ai.github.io/mrdetr

Existing methods for capturing multi-person holistic human motions from a monocular video usually involve integrating the detector, the tracker, and the human pose & shape estimator into a cascaded system. Differently, we develop a one-stage multi-person holistic human motion capture system, which 1) employs only one network, enabling significant benefits from the end-to-end training on a large-scale dataset; 2) enables performance improving of the tracking module during training, avoiding being limited by a pre-trained tracker; 3) captures the motions of all individuals within a single shot, rather than tracking and estimating each person sequentially. In this system, each query within a temporal cross-attention module is responsible for the long motion of a specific individual, implicitly aggregating individual-specific information throughout the entire video. To further boost the proposed system from end-to-end training, we also construct a synthetic human video dataset, with multi-person and whole-body annotations. Extensive experiments across different datasets demonstrate both the efficacy and the efficiency of both the proposed method and the dataset. The code of our method will be made publicly available.

Previous research on lightweight models has primarily focused on CNNs and Transformer-based designs. CNNs, with their local receptive fields, struggle to capture long-range dependencies, while Transformers, despite their global modeling capabilities, are limited by quadratic computational complexity in high-resolution scenarios. Recently, state-space models have gained popularity in the visual domain due to their linear computational complexity. Despite their low FLOPs, current lightweight Mamba-based models exhibit suboptimal throughput. In this work, we propose the MobileMamba framework, which balances efficiency and performance. We design a three-stage network to enhance inference speed significantly. At a fine-grained level, we introduce the Multi-Receptive Field Feature Interaction (MRFFI) module, comprising the Long-Range Wavelet Transform-Enhanced Mamba (WTE-Mamba), Efficient Multi-Kernel Depthwise Deconvolution (MK-DeConv), and Eliminate Redundant Identity components. This module integrates multi-receptive field information and enhances high-frequency detail extraction. Additionally, we employ training and testing strategies to further improve performance and efficiency. MobileMamba achieves up to 83.6% on Top-1, surpassing existing state-of-the-art methods which is maximum x21| faster than LocalVim on GPU. Extensive experiments on high-resolution downstream tasks demonstrate that MobileMamba surpasses current efficient models, achieving an optimal balance between speed and accuracy.

Deep multi-view clustering methods utilize information from multiple views to achieve enhanced clustering results and have gained increasing popularity in recent years. Most existing methods typically focus on either inter-view or intra-view relationships, aiming to align information across views or analyze structural patterns within individual views. However, they often incorporate inter-view complementary information in a simplistic manner, while overlooking the complex, high-order relationships within multi-view data and the interactions among samples, resulting in an incomplete utilization of the rich information available. Instead, we propose a multi-scale approach that exploits all of the available information. We first introduce a dual graph diffusion module guided by a consensus graph. This module leverages inter-view information to enhance the representation of both nodes and edges within each view. Secondly, we propose a novel contrastive loss function based on hypergraphs to more effectively model and leverage complex intra-view data relationships. Finally, we propose to adaptively learn fusion weights at the sample level, which enables a more flexible and dynamic aggregation of multi-view information. Extensive experiments on eight datasets show favorable performance of the proposed method compared to state-of-the-art approaches, demonstrating its effectiveness across diverse scenarios.

Derived from diffusion models, MangaNinja specializes in the task of reference-guided line art colorization. We incorporate two thoughtful designs to ensure precise character detail transcription, including a patch shuffling module to facilitate correspondence learning between the reference color image and the target line art, and a point-driven control scheme to enable fine-grained color matching. Experiments on a self-collected benchmark demonstrate the superiority of our model over current solutions in terms of precise colorization. We further showcase the potential of the proposed interactive point control in handling challenging cases (*e.g.*, extreme poses and shadows), cross-character colorization, multi-reference harmonization, *etc.*, beyond the reach of existing algorithms.

DUSt3R introduced a novel paradigm in geometric computer vision by proposing a model that can provide dense and unconstrained Stereo 3D Reconstruction of arbitrary image collections with no prior information about camera calibration nor viewpoint poses. Under the hood, however, DUSt3R processes image pairs, regressing local 3D reconstructions that need to be aligned in a global coordinate system. The number of pairs, growing quadratically, is an inherent limitation that becomes especially concerning for robust and fast optimization in the case of large image collections. In this paper, we propose an extension of DUSt3R from pairs to multiple views, that addresses all aforementioned concerns. Indeed, we propose a Multi-view Network for Stereo 3D Reconstruction, or MUSt3R, that modifies the DUSt3R architecture by making it symmetric and extending it to directly predict 3D structure for all views in a common coordinate frame. Second, we entail the model with a multi-layer memory mechanism which allows to reduce the computational complexity and to scale the reconstruction to large collections, inferring thousands of 3D pointmaps at high frame-rates with limited added complexity. The framework is designed to perform 3D reconstruction both offline and online, and hence can be seamlessly applied to SfM and visual SLAM scenarios showing state-of-the-art performance on various 3D downstream tasks, including uncalibrated Visual Odometry, relative camera pose, scale and focal estimation, 3D reconstruction and multi-view depth estimation.

Repurposing pre-trained diffusion models has been proven to be effective for NVS. However, these methods are mostly limited to a single object; directly applying such methods to compositional multi-object scenarios yields inferior results, especially incorrect object placement and inconsistent shape and appearance under novel views. How to enhance and systematically evaluate the cross-view consistency of such models remains under-explored. To address this issue, we propose MOVIS to enhance the structural awareness of the view-conditioned diffusion model for multi-object NVS in terms of model inputs, auxiliary tasks, and training strategy. First, we inject structure-aware features, including depth and object mask, into the denoising U-Net to enhance the model's comprehension of object instances and their spatial relationships. Second, we introduce an auxiliary task requiring the model to simultaneously predict novel view object masks, further improving the model's capability in differentiating and placing objects. Finally, we conduct an in-depth analysis of the diffusion sampling process and carefully devise a structure-guided timestep sampling scheduler during training, which balances the learning of global object placement and fine-grained detail recovery. To systematically evaluate the plausibility of synthesized images, we propose to assess cross-view consistency and novel view object placement alongside existing image-level NVS metrics. Extensive experiments on challenging synthetic and realistic datasets demonstrate that our method exhibits strong generalization capabilities and produces consistent novel view synthesis, highlighting its potential to guide future 3D-aware multi-object NVS tasks. Our project page is available at https://jason-aplp.github.io/MOVIS/.

Crafting adversarial examples is crucial for evaluating and enhancing the robustness of Deep Neural Networks (DNNs), presenting a challenge equivalent to maximizing a non-differentiable 0-1 loss function. However, existing single objective methods, namely adversarial attacks focus on a surrogate loss function, do not fully harness the benefits of engaging multiple loss functions, as a result of insufficient understanding of their synergistic and conflicting nature. To overcome these limitations, we propose the Multi-Objective Set-based Attack (MOS Attack), a novel adversarial attack framework leveraging multiple loss functions and automatically uncovering their interrelations. The MOS Attack adopts a set-based multi-objective optimization strategy, enabling the incorporation of numerous loss functions without additional parameters. It also automatically mines synergistic patterns among various losses, facilitating the generation of potent adversarial attacks with fewer objectives. Extensive experiments have shown that our MOS Attack outperforms single-objective attacks. Furthermore, by harnessing the identified synergistic patterns, MOS Attack continues to show superior results with a reduced number of loss functions.

Advanced driver assistance systems require a comprehensive understanding of the driver's mental/physical state and traffic context but existing works often neglect the potential benefits of joint learning between these tasks. This paper proposes MMTL-UniAD, a unified multi-modal multi-task learning framework that simultaneously recognizes driver behavior (e.g., looking around, talking), driver emotion (e.g., anxiety, happiness), vehicle behavior (e.g., parking, turning), and traffic context (e.g., traffic jam, traffic smooth). A key challenge is avoiding negative transfer between tasks, which can impair learning performance. To address this, we introduce two key components into the framework: one is the multi-axis region attention network to extract global context-sensitive features, and the other is the dual-branch multimodal embedding to learn multimodal embeddings from both task-shared and task-specific features. The former uses a multi-attention mechanism to extract task-relevant features, mitigating negative transfer caused by task-unrelated features. The latter employs a dual-branch structure to adaptively adjust task-shared and task-specific parameters, enhancing cross-task knowledge transfer while reducing task conflicts. We assess MMTL-UniAD on the AIDE dataset, using a series of ablation studies, and show that it outperforms state-of-the-art methods across all four tasks. The code is available on https://github.com/Wenzhuo-Liu/MMTL-UniAD.

Large-scale pre-trained Vision-Language Models (VLMs) have become essential for transfer learning across diverse tasks. However, adapting these models with limited few-shot data often leads to overfitting, diminishing their performance on new tasks. To tackle this issue, we propose a novel Multi-Modal Representation Learning (MMRL) framework that introduces a shared, learnable, and modality-agnostic representation space. MMRL projects the space tokens to text and image representation tokens, facilitating more effective multi-modal interactions. Unlike previous approaches that solely optimize class token features, MMRL integrates representation tokens at higher layers of the encoders--where dataset-specific features are more prominent--while preserving generalized knowledge in the lower layers. During training, both representation and class features are optimized, with trainable projection layer applied to the representation tokens, whereas the class token projection layer remains frozen to retain pre-trained knowledge. Furthermore, a regularization term is introduced to align the class features and text features with the zero-shot features from the frozen VLM, thereby safeguarding the model's generalization capacity. For inference, a decoupling strategy is employed, wherein both representation and class features are utilized for base classes, while only the class features, which retain more generalized knowledge, are used for new tasks. Extensive experiments across 15 datasets demonstrate that MMRL outperforms state-of-the-art methods, achieving a balanced trade-off between task-specific adaptation and generalization. Code is available at https://github.com/yunncheng/MMRL.