GenAI4ELab/papercli-papers huggingface dataset:
VolFormer: Explore More Comprehensive Cube Interaction for Hyperspectral Image Restoration and Beyond
Authors: Dabing Yu; Zheng Gao
Venue: CVPR 2025 (main)

Abstract: Capitalizing on the talent of self-attention in capturing non-local features, Transformer architectures have exhibited remarkable performance in single hyperspectral image restoration. For hyperspectral images, each pixel is located in the hyperspectral image cubes with a large spectral dimension and two spatial dimensions. Although uni-dimensional self-attention, like channel self-attention or spatial self-attention, builds long-range dependencies in spectral or spatial dimensions, they lack more comprehensive interactions across dimensions. To tackle the above drawback, we propose a VolFormer, a volumetric self-attention embedded Transformer network for single hyperspectral image restoration. Specifically, we propose volumetric self-attention (VolSA), which extends the interaction from 2D flat to 3D cube. VolSA can simultaneously model token interaction in the 3D cube, mining the potential correlations between the hyperspectral image cube. An attention decomposition form is proposed to reduce the computational burden of modeling volumetric information. In practical terms, VolSA adapts double similarity matrixes in spatial and channel dimensions to implicitly model 3D context information while transforming the complexity from cubic to quadratic. Additionally, we introduce the explicit spectral location prior to enhance the proposed self-attention. This property allows the target token to perceive global spectral information while simultaneously assigning different levels of attention to tokens at varying wavelength bands. Extensive experiments demonstrate that VolFormer achieves record-high performance on hyperspectral image super-resolution, denoise and classification benchmarks. Particularly, VolSA is portable and achieves inspiring results in hyperspectral classification. The source code is available at https://github.com/yudadabing/VolFormer.
Minding Fuzzy Regions: A Data-driven Alternating Learning Paradigm for Stable Lesion Segmentation
Authors: Lexin Fang; Yunyang Xu; Xiang Ma; Xuemei Li; Caiming Zhang
Venue: CVPR 2025 (main)

Abstract: Deep learning has achieved significant advancements in medical image segmentation, but existing models still face challenges in accurately segmenting lesion regions. The main reason is that some lesion regions in medical images have unclear boundaries, irregular shapes, and small tissue density differences, leading to label ambiguity. However, the existing model treats all data equally without taking quality differences into account in the training process, resulting in noisy labels negatively impacting model training and unstable feature representations. In this paper, a  **d**ata-driven **a**lternating **le**arning (**DALE**) paradigm is proposed to optimize the model's training process, achieving stable and high-precision segmentation. The paradigm focuses on two key points: (1) reducing the impact of noisy labels, and (2) calibrating unstable representations. To mitigate the negative impact of noisy labels, a loss consistency-based collaborative optimization method is proposed, and its effectiveness is theoretically demonstrated. Specifically, the label confidence parameters are introduced to dynamically adjust the influence of labels of different confidence levels during model training, thus reducing the influence of noise labels. To calibrate the learning bias of unstable representations, a distribution alignment method is proposed. This method restores the underlying distribution of unstable representations, thereby enhancing the discriminative capability of fuzzy region representations. Extensive experiments on various benchmarks and model backbones demonstrate the superiority of the DALE paradigm, achieving an average performance improvement of up to 7.16%.
BizGen: Advancing Article-level Visual Text Rendering for Infographics Generation
Authors: Yuyang Peng; Shishi Xiao; Keming Wu; Qisheng Liao; Bohan Chen; Kevin Lin; Danqing Huang; Ji Li; Yuhui Yuan
Venue: CVPR 2025 (main)

Abstract: Recently, state-of-the-art text-to-image generation models, such as Flux and Ideogram 2.0, have made significant progress in sentence-level visual text rendering. In this paper, we focus on the more challenging scenarios of article-level visual text rendering and address a novel task of generating high-quality business content, including infographics and slides, based on user provided article-level descriptive prompts and ultra-dense layouts.  The fundamental challenges are twofold: significantly longer context lengths and the scarcity of high-quality business content data. In contrast to most previous works that focus on a limited number of sub-regions and sentence-level prompts, ensuring precise adherence to ultra-dense layouts with tens or even hundreds of sub-regions in business content is far more challenging. We make two key technical contributions: (i) the construction of scalable, high-quality business content dataset, i.e.,Infographics-650K, equipped with ultra-dense layouts and prompts by implementing a layer-wise retrieval-augmented infographic generation scheme; and (ii) a layout-guided cross attention scheme, which injects tens of region-wise prompts into a set of cropped region latent space according to the ultra-dense layouts, and refine each sub-regions flexibly during inference using a layout conditional CFG. We demonstrate the  strong results of our system compared to previous SOTA systems such as Flux and SD3 on our BizEval prompt set. Additionally, we conduct thorough ablation experiments to verify the effectiveness of each component. We hope our constructed Infographics-650K and BizEval can encourage the broader community to advance the progress of business content generation.
MLVU: Benchmarking Multi-task Long Video Understanding
Authors: Junjie Zhou; Yan Shu; Bo Zhao; Boya Wu; Zhengyang Liang; Shitao Xiao; Minghao Qin; Xi Yang; Yongping Xiong; Bo Zhang; Tiejun Huang; Zheng Liu
Venue: CVPR 2025 (main)

Abstract: The evaluation of Long Video Understanding (LVU) performance poses an important but challenging research problem. Despite previous efforts, the existing video understanding benchmarks are severely constrained by several issues, especially the insufficient lengths of videos, a lack of diversity in video types and evaluation tasks, and the inappropriateness for evaluating LVU performances. To address the above problems, we propose a new benchmark called MLVU (Multi-task Long Video Understanding Benchmark) for the comprehensive and in-depth evaluation of LVU. MLVU presents the following critical values: 1) The substantial and flexible extension of video lengths, which enables the benchmark to evaluate LVU performance across a wide range of durations. 2) The inclusion of various video genres, such as movies, surveillance, egocentric videos, and cartoons, reflects the models' LVU performances in different scenarios. 3) The development of diversified evaluation tasks, which enables a comprehensive examination of MLLMs' key abilities in long-video understanding. The empirical study with 23 latest MLLMs reveals significant room for improvement in today's technique, as all existing methods struggle with most of the evaluation tasks and exhibit severe performance degradation when handling longer videos. Additionally, it suggests that factors such as context length, image-understanding ability, and the choice of LLM backbone can play critical roles in future advancements. We anticipate that MLVU will advance the research of LVU by providing a comprehensive and in-depth analysis of MLLMs. The code and dataset can be accessed from https://github.com/JUNJIE99/MLVU.
Recovering Dynamic 3D Sketches from Videos
Authors: Jaeah Lee; Changwoon Choi; Young Min Kim; Jaesik Park
Venue: CVPR 2025 (main)

Abstract: Understanding 3D motion from videos presents inherent challenges due to the diverse types of movement, ranging from rigid and deformable objects to articulated structures. To overcome this, we propose Liv3Stroke, a novel approach for abstracting objects in motion with deformable 3D strokes. The detailed movements of an object may be represented by unstructured motion vectors or a set of motion primitives using a pre-defined articulation from a template model. Just as a free-hand sketch can intuitively visualize scenes or intentions with a sparse set of lines, we utilize a set of parametric 3D curves to capture a set of spatially smooth motion elements for general objects with unknown structures. We first extract noisy, 3D point cloud motion guidance from video frames using semantic features, and our approach deforms a set of curves to abstract essential motion features as a set of explicit 3D representations. Such abstraction enables an understanding of prominent components of motions while maintaining robustness to environmental factors. Our approach allows direct analysis of 3D object movements from video, tackling the uncertainty that typically occurs when translating real-world motion into recorded footage. The project page is accessible via: https://jaeah.me/liv3stroke_web.
IM-Zero: Instance-level Motion Controllable Video Generation in a Zero-shot Manner
Authors: Yuyang Huang; Yabo Chen; Li Ding; Xiaopeng Zhang; Wenrui Dai; Junni Zou; Hongkai Xiong; Qi Tian
Venue: CVPR 2025 (main)

Abstract: Controllability of video generation has been recently concerned in addition to the quality of generated videos. The main challenge to controllable video generation is to synthesize videos based on user-specified instance spatial locations and movement trajectories. However, existing methods suffer from a dilemma between the resource consumption, generation quality, and user controllability. As an efficient alternative to prohibitive training-based video generation, existing zero-shot video generation methods cannot generate high-quality and motion-consistent videos under the control of layouts and movement trajectories. In this paper, we propose a novel zero-shot method named IM-Zero that ameliorates instance-level motion controllable video generation with enhanced control accuracy, motion consistency, and richness of details to address this problem. Specifically, we first present a motion generation stage that extracts motion and textural guidance from keyframe candidates from pre-trained grounded text-to-image model to generate the desired coarse motion video. Subsequently, we develop a video refinement stage that injects the motion priors of pre-trained text-to-video models and detail priors of pre-trained text-to-image models into the latents of coarse motion videos to further enhance video motion consistency and richness of details. To our best knowledge, IM-Zero is the first to simultaneously achieve high-quality video generation and allow to control both layouts and movement trajectories in a zero-shot manner. Extensive experiments demonstrate that IM-Zero outperforms existing methods in terms of video quality, inter-frame consistency, and the alignment of location and trajectory. Furthermore, compared with existing methods, IM-Zero enjoys extra advantages of versatility in video generation, including motion control of subparts within instances, finer control of specifying instance shapes via masks, and more difficult tasks of motion transfer for customizing fine-grained motion patterns through reference videos and high-quality text-to-video generation.
EigenGS Representation: From Eigenspace to Gaussian Image Space
Authors: Lo-Wei Tai; Ching-En Li; Cheng-Lin Chen; Chih-Jung Tsai; Hwann-Tzong Chen; Tyng-Luh Liu
Venue: CVPR 2025 (main)

Abstract: Principal Component Analysis (PCA), a classical dimensionality reduction technique, and 2D Gaussian representation, an adaptation of 3D Gaussian Splatting for image representation, offer distinct approaches to modeling visual data. We present EigenGS, a novel method that bridges these paradigms through an efficient transformation pipeline connecting eigenspace and image-space Gaussian representations. Our approach enables instant initialization of Gaussian parameters for new images without requiring per-image optimization from scratch, dramatically accelerating convergence. EigenGS introduces a frequency-aware learning mechanism that encourages Gaussians to adapt to different scales, effectively modeling varied spatial frequencies and preventing artifacts in high-resolution reconstruction. Extensive experiments demonstrate that EigenGS not only achieves superior reconstruction quality compared to direct 2D Gaussian fitting but also reduces the necessary parameter count and training time. The results highlight EigenGS's effectiveness and generalization ability across images with varying resolutions and diverse categories, making Gaussian-based image representation both high-quality and viable for real-time applications.
Link-based Contrastive Learning for One-Shot Unsupervised Domain Adaptation
Authors: Yue Zhang; Mingyue Bin; Yuyang Zhang; Zhongyuan Wang; Zhen Han; Chao Liang
Venue: CVPR 2025 (main)

Abstract: Unsupervised domain adaptation (UDA) aims to learn discriminative features from a labeled source domain by supervised learning and to transfer the knowledge to an unlabeled target domain via distribution alignment. However, in some real-world scenarios, e.g., public safety or access control, it's difficult to obtain sufficient source domain data, which hinders the application of existing UDA methods. To this end, this paper investigates a realistic but rarely studied problem called one-shot unsupervised domain adaptation (OSUDA), where there is only one example per category in the source domain and abundant unlabeled samples in the target domain. Compared with UDA, OSUDA faces dual challenges in both feature learning and domain alignment due to the lack of sufficient source data. To address these challenges, we propose a simple but effective link-based contrastive learning (LCL) method for OSUDA. On the one hand, with the help of in-domain links that indicate whether two samples are from the same cluster, LCL can learn discriminative features with abundant unlabeled target data. On the other hand, by constructing cross-domain links that show whether two clusters are bidirectionally matched, LCL can realize accurate domain alignment with only one source sample per category. Extensive experiments conducted on 4 public domain adaptation benchmarks, including VisDA-2017, Office-31, Office-Home, and DomainNet, demonstrate the effectiveness of the proposed LCL under the OSUDA setting. In addition, we build a realistic OSUDA surveillance video face recognition dataset, where LCL consistently improves the recognition accuracy across various face recognition methods.
SmartCLIP: Modular Vision-language Alignment with Identification Guarantees
Authors: Shaoan Xie; Lingjing Lingjing; Yujia Zheng; Yu Yao; Zeyu Tang; Eric P. Xing; Guangyi Chen; Kun Zhang
Venue: CVPR 2025 (main)

Abstract: Contrastive Language-Image Pre-training (CLIP) \citep radford2021learning  has emerged as a pivotal model in computer vision and multimodal learning, achieving state-of-the-art performance at aligning visual and textual representations through contrastive learning. However, CLIP struggles with potential information misalignment in many image-text datasets and suffers from entangled representation. On the one hand, short captions for a single image in datasets like MSCOCO may describe disjoint regions in the image, leaving the model uncertain about which visual features to retain or disregard. On the other hand, directly aligning long captions with images can lead to the retention of entangled details, preventing the model from learning disentangled, atomic concepts -- ultimately limiting its generalization on certain downstream tasks involving short prompts. In this paper, we establish theoretical conditions that enable flexible alignment between textual and visual representations across varying levels of granularity. Specifically, our framework ensures that a model can not only preserve cross-modal semantic information in its entirety but also disentangle visual representations to capture fine-grained textual concepts.  Building on this foundation, we introduce \ours, a novel approach that identifies and aligns the most relevant visual and textual representations in a modular manner. Superior performance across various tasks demonstrates its capability to handle information misalignment and supports our identification theory. The code is available at https://github.com/Mid-Push/SmartCLIP.
UniMamba: Unified Spatial-Channel Representation Learning with Group-Efficient Mamba for LiDAR-based 3D Object Detection
Authors: Xin Jin; Haisheng Su; Kai Liu; Cong Ma; Wei Wu; Fei HUI; Junchi Yan
Venue: CVPR 2025 (main)

Abstract: Recent advances in LiDAR 3D detection have demonstrated the effectiveness of Transformer-based frameworks in capturing the global dependencies from point cloud spaces, which serialize the 3D voxels into the flattened 1D sequence for iterative self-attention. However, the spatial structure of 3D voxels will be inevitably destroyed during the serialization process. Besides, due to the considerable number of 3D voxels and quadratic complexity of Transformers, multiple sequences are grouped before feeding to Transformers, leading to a limited receptive field. Inspired by the impressive performance of State Space Models (SSM), in this paper, we propose a novel Unified Mamba (UniMamba), which seamlessly integrates the merits of 3D convolution and SSM in a concise multi-head manner, aiming to perform "local and global" spatial context aggregation efficiently and simultaneously. Specifically, a UniMamba block is designed which mainly consists of spatial locality modeling, complementary Z-order serialization and local-global sequential aggregator. The spatial locality modeling module integrates 3D submanifold convolution to capture the dynamic spatial position embedding before serialization. Then the efficient Z-order curve is adopted for serialization both horizontally and vertically. Furthermore, the local-global sequential aggregator adopts the channel grouping strategy to efficiently encode both "local and global" spatial inter-dependencies using multi-head SSM. Additionally, an encoder-decoder architecture with stacked UniMamba blocks is formed to facilitate multi-scale spatial learning hierarchically. Extensive experiments are conducted on three popular datasets: nuScenes, Waymo and Argoverse 2. Particularly, our UniMamba achieves 70.2 mAP on the nuScenes dataset.
MaSS13K: A Matting-level Semantic Segmentation Benchmark
Authors: Chenxi Xie; Minghan Li; Hui Zeng; Jun Luo; Lei Zhang
Venue: CVPR 2025 (main)

Abstract: High-resolution semantic segmentation is essential for applications such as image editing, bokeh imaging, AR/VR, etc. Unfortunately, existing datasets often have limited resolution and lack precise mask details and boundaries. In this work, we build a large-scale, matting-level semantic segmentation dataset, named MaSS13K, which consists of 13,348 real-world images, all at 4K resolution. MaSS13K provides high-quality mask annotations of a number of objects, which are categorized into seven categories: human, vegetation, ground, sky, water, building, and others. MaSS13K features precise masks, with an average mask complexity 20-50 times higher than existing semantic segmentation datasets. We consequently present a method specifically designed for high-resolution semantic segmentation, namely MaSSFormer, which employs an efficient pixel decoder that aggregates high-level semantic features and low-level texture features across three stages, aiming to produce high-resolution masks with minimal computational cost. Finally, we propose a new learning paradigm, which integrates the high-quality masks of the seven given categories with pseudo labels from new classes, enabling MaSSFormer to transfer its accurate segmentation capability to other classes of objects. Our proposed MaSSFormer is comprehensively evaluated on the MaSS13K benchmark together with 14 representative segmentation models.  We expect that our meticulously annotated MaSS13K dataset and the MaSSFormer model can facilitate the research of high-resolution and high-quality semantic segmentation. Datasets and codes can be found at https://github.com/xiechenxi99/MaSS13K.
Rethinking the Adversarial Robustness of Multi-Exit Neural Networks in an Attack-Defense Game
Authors: Keyizhi Xu; Chi Zhang; Zhan Chen; Zhongyuan Wang; Chunxia Xiao; Chao Liang
Venue: CVPR 2025 (main)

Abstract: Multi-exit neural networks represent a promising approach to enhancing model inference efficiency, yet like common neural networks, they suffer from significantly reduced robustness against adversarial attacks. While some defense methods have been raised to strengthen the adversarial robustness of multi-exit neural networks, we identify a long-neglected flaw in the evaluation of previous studies: simply using a fixed set of exits for attack may lead to an overestimation of their defense capacity. Based on this finding, our work explores the following three key aspects in the adversarial robustness of multi-exit neural networks: (1) we discover that a mismatch of the network exits used by the attacker and defender is responsible for the overestimated robustness of previous defense methods; (2) by finding the best strategy in a two-player zero-sum game, we propose AIMER as an improved evaluation scheme to measure the intrinsic robustness of multi-exit neural networks; (3) going further, we introduce NEED defense method under the evaluation of AIMER that can optimize the defender's strategy by finding a Nash equilibrium of the game. Experiments over 3 datasets, 7 architectures, 7 attacks and 4 baselines show that AIMER evaluates the robustness 13.52% lower than previous methods under AutoAttack, while the robust performance of NEED surpasses single-exit networks of the same backbones by 5.58% maximally.
Enhancing Testing-Time Robustness for Trusted Multi-View Classification in the Wild
Authors: Wei Liu; Yufei Chen; Xiaodong Yue
Venue: CVPR 2025 (main)

Abstract: Trusted multi-view classification (TMVC) addresses variations in data quality by evaluating the reliability of each view based on prediction uncertainty at the evidence level, reducing the impact of low-quality views commonly encountered in real-world scenarios. However, existing TMVC methods often struggle to maintain robustness during testing, particularly when integrating noisy or corrupted views.  This limitation arises because the evidence collected by TMVC may be unreliable, frequently providing incorrect information due to complex view distributions and optimization challenges, ultimately leading to classification performance degradation. To enhance the robustness of TMVC methods in real-world conditions, we propose a generalized evidence filtering mechanism that is compatible with various fusion strategies commonly used in TMVC, including Belief Constraint Fusion, Aleatory Cumulative Belief Fusion, and Averaging Belief Fusion. Specifically, we frame the identification of unreliable evidence as a multiple testing problem and introduce p-values to control the risk of false identification. By selectively down-weighting unreliable evidence during testing, our mechanism ensures robust fusion and mitigates performance degradation. Both theoretical guarantees and empirical results demonstrate significant improvements in the classification performance of TMVC methods, supporting their reliable application in challenging, real-world environments.