GenAI4ELab/papercli-papers huggingface dataset:
Not Only Text: Exploring Compositionality of Visual Representations in Vision-Language Models
Authors: Davide Berasi; Matteo Farina; Massimiliano Mancini; Elisa Ricci; Nicola Strisciuglio
Venue: CVPR 2025 (main)

Abstract: Vision-Language Models (VLMs) learn a shared feature space for text and images, enabling the comparison of inputs of different modalities. While prior works demonstrated that VLMs organize natural language representations into regular structures encoding composite meanings, it remains unclear if compositional patterns also emerge in the visual embedding space. In this work, we investigate compositionality in the image domain, where the analysis of compositional properties is challenged by noise and sparsity of visual data. We address these problems and propose a framework, called Geodesically Decomposable Embeddings (GDE), that approximates image representations with geometry-aware compositional structures in the latent space. We demonstrate that visual embeddings of pre-trained VLMs exhibit a compositional arrangement, and evaluate the effectiveness of this property in the tasks of compositional classification and group robustness. GDE achieves stronger performance in compositional classification compared to its counterpart method that assumes linear geometry of the latent space. Notably, it is particularly effective for group robustness, where we achieve higher results than task-specific solutions. Our results indicate that VLMs can automatically develop a human-like form of compositional reasoning in the visual domain, making their underlying processes more interpretable. Code is available at https://github.com/BerasiDavide/vlm_image_compositionality.
DefMamba: Deformable Visual State Space Model
Authors: Leiye Liu; Miao Zhang; Jihao Yin; Tingwei Liu; Wei Ji; Yongri Piao; Huchuan Lu
Venue: CVPR 2025 (main)

Abstract: Recently, state space models (SSM), particularly Mamba, have attracted significant attention from scholars due to their ability to effectively balance computational efficiency and performance. However, most existing visual Mamba methods flatten images into 1D sequences using predefined scan orders, which results the model being less capable of utilizing the spatial structural information of the image during the feature extraction process. To address this issue, we proposed a novel visual foundation model called DefMamba. This model includes a multi-scale backbone structure and deformable mamba (DM) blocks, which dynamically adjust the scanning path to prioritize important information, thus enhancing the capture and processing of relevant input features. By combining a deformable scanning (DS) strategy, this model significantly improves its ability to learn image structures and detects changes in object details. Numerous experiments have shown that DefMamba achieves state-of-the-art performance in various visual tasks, including image classification, object detection, instance segmentation, and semantic segmentation. The code is open source on DefMamba.
HOP: Heterogeneous Topology-based Multimodal Entanglement for Co-Speech Gesture Generation
Authors: Hongye Cheng; Tianyu Wang; Guangsi Shi; Zexing Zhao; Yanwei Fu
Venue: CVPR 2025 (main)

Abstract: Co-speech gestures are crucial non-verbal cues that enhance speech clarity and expressiveness in human communication, which have attracted increasing attention in multimodal research. While the existing methods have made strides in gesture accuracy, challenges remain in generating diverse and coherent gestures, as most approaches assume independence among multimodal inputs and lack explicit modeling of their interactions. In this work, we propose a novel multimodal learning method named HOP for co-speech gesture generation that captures the heterogeneous entanglement between gesture motion, audio rhythm, and text semantics, enabling the generation of coordinated gestures. By leveraging spatiotemporal graph modeling, we achieve the alignment of audio and action. Moreover, to enhance modality coherence, we build the audio-text semantic representation based on a reprogramming module, which is beneficial for cross-modality adaptation. Our approach enables the trimodal system to learn each other's features and represent them in the form of topological entanglement. Extensive experiments demonstrate that HOP achieves state-of-the-art performance, offering more natural and expressive co-speech gesture generation. More information, codes, and demos are available here: https://star-uu-wang.github.io/HOP/.
VoxelSplat: Dynamic Gaussian Splatting as an Effective Loss for Occupancy and Flow Prediction
Authors: Ziyue Zhu; Shenlong Wang; Jin Xie; Jiang-jiang Liu; Jingdong Wang; Jian Yang
Venue: CVPR 2025 (main)

Abstract: Recent advancements in camera-based occupancy prediction have focused on the simultaneous prediction of 3D semantics and scene flow, a task that presents significant challenges due to specific difficulties, e.g., occlusions and unbalanced dynamic environments. In this paper, we analyze these challenges and their underlying causes. To address them, we propose a novel regularization framework called VoxelSplat. This framework leverages recent developments in 3D Gaussian Splatting to enhance model performance in two key ways: (i)Enhanced Semantics Supervision through 2D Projection: During training, our method decodes sparse semantic 3D Gaussians from 3D representations and projects them onto the 2D camera view. This provides additional supervision signals in the camera-visible space, allowing 2D labels to improve the learning of 3D semantics. (ii) Scene Flow Learning: Our framework uses the predicted scene flow to model the motion of Gaussians, and is thus able to learn the scene flow of moving objects in a self-supervised manner using the labels of adjacent frames. Our method can be seamlessly integrated into various existing occupancy models, enhancingperformance without increasing inference time. Extensive experiments on benchmark datasets demonstrate the effectiveness of VoxelSplat in improving the accuracy of both semantic occupancy and scene flow estimation. The project page and codes are available at https://zzy816.github.io/VoxelSplat-Demo/.
Exploring Scene Affinity for Semi-Supervised LiDAR Semantic Segmentation
Authors: Chuandong Liu; Xingxing Weng; Shuguo Jiang; Pengcheng Li; Lei Yu; Gui-Song Xia
Venue: CVPR 2025 (main)

Abstract: This paper explores scene affinity (AIScene), namely intra-scene consistency and inter-scene correlation, for semi-supervised LiDAR semantic segmentation in driving scenes. Adopting teacher-student training, AIScene employs a teacher network to generate pseudo-labeled scenes from unlabeled data, which then supervise the student network's learning. Unlike most methods that include all points in pseudo-labeled scenes for forward propagation but only pseudo-labeled points for backpropagation, AIScene removes points without pseudo-labels, ensuring consistency in both forward and backward propagation within the scene. This simple point erasure strategy effectively prevents unsupervised, semantically ambiguous points (excluded in backpropagation) from affecting the learning of pseudo-labeled points. Moreover, AIScene incorporates patch-based data augmentation, mixing multiple scenes at both scene and instance levels. Compared to existing augmentation techniques that typically perform scene-level mixing between two scenes, our method enhances the semantic diversity of labeled (or pseudo-labeled) scenes, thereby improving the semi-supervised performance of segmentation models. Experiments show that AIScene outperforms previous methods on two popular benchmarks across four settings, achieving notable improvements of 1.9% and 2.1% in the most challenging 1% labeled data. The code will be released at https://github.com/azhuantou/AIScene.
ControlFace: Harnessing Facial Parametric Control for Face Rigging
Authors: Wooseok Jang; Youngjun Hong; Geonho Cha; Seungryong Kim
Venue: CVPR 2025 (main)

Abstract: Manipulation of facial images to meet specific controls such as pose, expression, and lighting, also referred to as face rigging is a complex task in computer vision. Existing methods are limited by their reliance on image datasets, which necessitates individual-specific fine-tuning and limits their ability to retain fine-grained identity and semantic details, reducing practical usability. To overcome these limitations, we introduce ControlFace, a novel face rigging method conditioned on 3DMM renderings that enables flexible, high-fidelity control. ControlFace employs a dual-branch U-Nets: one, referred to as FaceNet, captures identity and fine details, while the other focuses on generation. To enhance control precision, control mixer module encodes the correlated features between the target-aligned control and reference-aligned control, and a novel guidance method, reference control guidance, steers the generation process for better control adherence. By training on a facial video dataset, we fully utilize FaceNet's rich representations while ensuring control adherence. Extensive experiments demonstrate ControlFace's superior performance in identity preservation, and control precision, highlighting its practicality. Code and pre-trained weights will be publicly available.
Minority-Focused Text-to-Image Generation via Prompt Optimization
Authors: Soobin Um; Jong Chul Ye
Venue: CVPR 2025 (main)

Abstract: We investigate the generation of minority samples using pretrained text-to-image (T2I) latent diffusion models. Minority instances, in the context of T2I generation, can be defined as ones living on low-density regions of text-conditional data distributions. They are valuable for various applications of modern T2I generators, such as data augmentation and creative AI. Unfortunately, existing pretrained T2I diffusion models primarily focus on high-density regions, largely due to the influence of guided samplers (like CFG) that are essential for high-quality generation. To address this, we present a novel framework to counter the high-density-focus of T2I diffusion models. Specifically, we first develop an online prompt optimization framework that encourages emergence of desired properties during inference while preserving semantic contents of user-provided prompts. We subsequently tailor this generic prompt optimizer into a specialized solver that promotes generation of minority features by incorporating a carefully-crafted likelihood objective. Extensive experiments conducted across various types of T2I models demonstrate that our approach significantly enhances the capability to produce high-quality minority instances compared to existing samplers. Code is available at https://github.com/soobin-um/MinorityPrompt.
Imputation-free and Alignment-free: Incomplete Multi-view Clustering Driven by Consensus Semantic Learning
Authors: Yuzhuo Dai; Jiaqi Jin; Zhibin Dong; Siwei Wang; Xinwang Liu; En Zhu; Xihong Yang; Xinbiao Gan; Yu Feng
Venue: CVPR 2025 (main)

Abstract: In incomplete multi-view clustering (IMVC), missing data induce prototype shifts within views and semantic inconsistencies across views. A feasible solution is to explore cross-view consistency in paired complete observations, further imputing and aligning the similarity relationships inherently shared across views. Nevertheless, existing methods are constrained by two-tiered limitations: (1) Neither instance- nor cluster-level consistency learning construct a semantic space shared across views to learn consensus semantics. The former enforces cross-view instances alignment, and wrongly regards unpaired observations with semantic consistency as negative pairs; the latter focuses on cross-view cluster counterparts while coarsely handling fine-grained intra-cluster relationships within views. (2) Excessive reliance on consistency results in unreliable imputation and alignment without incorporating view-specific cluster information. Thus, we propose an IMVC framework, imputation- and alignment-free for consensus semantics learning (FreeCSL). To bridge semantic gaps across all observations, we learn consensus prototypes from available data to discover a shared space, where semantically similar observations are pulled closer for consensus semantics learning. To capture semantic relationships within specific views, we design a heuristic graph clustering based on modularity to recover cluster structure with intra-cluster compactness and inter-cluster separation for cluster semantics enhancement. Extensive experiments demonstrate, compared to state-of-the-art competitors, FreeCSL achieves more confident and robust assignments on IMVC task.
Sensitivity-Aware Efficient Fine-Tuning via Compact Dynamic-Rank Adaptation
Authors: Tianran Chen; Jiarui Chen; Baoquan Zhang; Zhehao Yu; Shidong Chen; Rui Ye; Xutao Li; Yunming Ye
Venue: CVPR 2025 (main)

Abstract: Parameter-Efficient Fine-Tuning (PEFT) is a fundamental research problem in computer vision, which aims to tune a few of parameters for efficient storage and adaptation of pre-trained vision models. Recently, sensitivity-aware parameter efficient fine-tuning method (SPT) addresses this problem by identifying sensitive parameters and then leveraging its sparse characteristic to combine unstructured and structured tuning for PEFT. However, existing methods only focus on sparse characteristic of sensitive parameters but overlook its distribution characteristic, which results in additional storage burden and limited performance improvement. In this paper, we find that the distribution of sensitive parameters is not chaotic, but concentrates in a small number of rows or columns in each parameter matrix. Inspired by this fact, we propose a Compact Dynamic-Rank Adaptation-based tuning method for Sensitivity-aware Parameter efficient fine-Tuning, called CDRA-SPT. Specifically, we first identify the sensitive parameters that require tuning for each downstream task. Then, we reorganize the sensitive parameters by following its row and column into a compact sub-parameter matrix. Finally, a dynamic-rank adaptation is designed and applied at sub-parameter matrix level for PEFT. Its advantage is that the dynamic-rank characteristic of sub-parameter matrix can be fully exploited for PEFT. Extensive experiments show that our method achieves superior performance over previous state-of-the-art methods.
MANTA: A Large-Scale Multi-View and Visual-Text Anomaly Detection Dataset for Tiny Objects
Authors: Lei Fan; Dongdong Fan; Zhiguang Hu; Yiwen Ding; Donglin Di; Kai Yi; Maurice Pagnucco; Yang Song
Venue: CVPR 2025 (main)

Abstract: We present MANTA, a visual-text anomaly detection dataset for tiny objects. The visual component comprises over 137.3K images across 38 object categories spanning five typical domains, of which 8.6K images are labeled as anomalous with pixel-level annotations. Each image is captured from five distinct viewpoints to ensure comprehensive object coverage. The text component consists of two subsets: Declarative Knowledge, including 875 words that describe common anomalies across various domains and specific categories, with detailed explanations for \left< what, why, how \right>,  including causes and visual characteristics; and Constructivist Learning, providing 2K multiple-choice questions with varying levels of difficulty, each paired with images and corresponded answer explanations. We also propose a baseline for visual-text tasks and conduct extensive benchmarking experiments to evaluate advanced methods across different settings, highlighting the challenges and efficacy of our dataset.
MoDec-GS: Global-to-Local Motion Decomposition and Temporal Interval Adjustment for Compact Dynamic 3D Gaussian Splatting
Authors: Sangwoon Kwak; Joonsoo Kim; Jun Young Jeong; Won-Sik Cheong; Jihyong Oh; Munchurl Kim
Venue: CVPR 2025 (main)

Abstract: 3D Gaussian Splatting (3DGS) has made significant strides in scene representation and neural rendering, with intense efforts focused on adapting it for dynamic scenes. Despite delivering remarkable rendering quality and speed, existing methods struggle with storage demands and the representation of complex real-world motions. To address these challenges, we propose MoDec-GS, a memory-efficient Gaussian splatting framework designed to reconstruct novel views in challenging scenarios with complex motions. We introduce Global-to-Local Motion Decomposition (GLMD) to effectively capture dynamic motions in a coarse-to-fine manner. This approach leverages Global Canonical Scaffolds (Global CS) and Local Canonical Scaffolds (Local CS), which extend static Scaffold representation to dynamic video reconstruction. For Global CS, we propose Global Anchor Deformation (GAD) to efficiently represent global dynamics along complex motions by directly deforming the implicit Scaffold attributes, including anchor position, offset, and local context features. Next, we finely adjust local motions via the Local Gaussian Deformation (LGD) of Local CS explicitly. Additionally, we introduce Temporal Interval Adjustment (TIA) to automatically control the temporal coverage of each Local CS during training, enabling MoDec-GS to find optimal interval assignments based on the specified number of temporal segments. Extensive evaluations demonstrate that MoDec-GS achieves an average 70% reduction in model size over state-of-the-art methods for dynamic 3D Gaussians from real-world dynamic videos while maintaining or even improving rendering quality.
DaCapo: Score Distillation as Stacked Bridge for Fast and High-quality 3D Editing
Authors: Yufei Huang; Bangyan Liao; Yuqi Hu; Haitao Lin; Lirong Wu; Siyuan Li; Cheng Tan; Zicheng Liu; Yunfan Liu; Zelin Zang; Chang Yu; Zhen Lei
Venue: CVPR 2025 (main)

Abstract: Score Distillation Sampling (SDS) has been successfully extended to text-driven 3D scene editing with 2D pretrained diffusion models. However, SDS-based editing methods suffer from lengthy optimization processes with slow inference and low quality. We attribute the issue of lengthy optimization to the stochastic optimization scheme used in SDS-based editing, where many steps may conflict with each other (e.g., the inherent trade-off between editing and preservation). To reduce this internal conflict and speed up the editing process, we propose to separate editing and preservation in time with a diffusion time schedule and frame the 3D editing optimization process as a diffusion bridge sampling process. Motivated by the analysis above, we introduce DaCapo, a fast diffusion sampling-like 3D editing method that incorporates a novel stacked bridge framework, which estimates a direct diffusion bridge between source and target distribution with only a pretrained 2D diffusion model. Specifically, It models the editing process as a combination of inversion and generation, where both processes happen simultaneously as a stack of Diffusion Bridges. DaCapo shows a 15x speed-up with comparable results to the state-of-the-art SDS-based method. It completes the process in just 2,500 steps on a single GPU and accommodates a variety of 3D representation methods.
A Selective Re-learning Mechanism for Hyperspectral Fusion Imaging
Authors: Yuanye Liu; Jinyang Liu; Renwei Dian; Shutao Li
Venue: CVPR 2025 (main)

Abstract: Hyperspectral fusion imaging is challenged by high computational cost due to the abundant spectral information. We find that pixels in regions with smooth spatial-spectral structure can be reconstructed well using a shallow network, while only those in regions with complex spatial-spectral structure require a deeper network. However, existing methods process all pixels uniformly, which ignores  this property. To leverage this property, we propose a Selective Re-learning Fusion Network (SRLF) that initially extracts features from all pixels uniformly and then selectively refines distorted feature points. Specifically, SRLF first employs a Preliminary Fusion Module with robust global modeling capability to generate a preliminary fusion feature. Afterward, it applies a Selective Re-learning Module to focus on improving distorted feature points in the preliminary fusion feature. To achieve targeted learning, we present a novel Spatial-Spectral Structure-Guided Selective Re-learning Mechanism (SSG-SRL) that integrates the observation model to identify the feature points with spatial or spectral distortions. Only these distorted points are sent to the corresponding re-learning blocks, reducing both computational cost and the risk of overfitting. Finally, we develop an SRLF-Net, composed of multiple cascaded SRLFs, which surpasses multiple state-of-the-art methods on several datasets with minimal computational cost.
SCSegamba: Lightweight Structure-Aware Vision Mamba for Crack Segmentation in Structures
Authors: Hui Liu; Chen Jia; Fan Shi; Xu Cheng; Shengyong Chen
Venue: CVPR 2025 (main)

Abstract: Pixel-level segmentation of structural cracks across various scenarios remains a considerable challenge. Current methods encounter challenges in effectively modeling crack morphology and texture, facing challenges in balancing segmentation quality with low computational resource usage. To overcome these limitations, we propose a lightweight Structure-Aware Vision Mamba Network (SCSegamba), capable of generating high-quality pixel-level segmentation maps by leveraging both the morphological information and texture cues of crack pixels with minimal computational cost. Specifically, we developed a Structure-Aware Visual State Space module (SAVSS), which incorporates a lightweight Gated Bottleneck Convolution (GBC) and a Structure-Aware Scanning Strategy (SASS). The key insight of GBC lies in its effectiveness in modeling the morphological information of cracks, while the SASS enhances the perception of crack topology and texture by strengthening the continuity of semantic information between crack pixels. Experiments on crack benchmark datasets demonstrate that our method outperforms other state-of-the-art (SOTA) methods, achieving the highest performance with only 2.8M parameters. On the multi-scenario dataset, our method reached 0.8390 in F1 score and 0.8479 in mIoU.
Autoregressive Sequential Pretraining for Visual Tracking
Authors: Shiyi Liang; Yifan Bai; Yihong Gong; Xing Wei
Venue: CVPR 2025 (main)

Abstract: Recent advancements in visual object tracking have shifted towards a sequential generation paradigm, where object deformation and motion exhibit strong temporal dependencies. Despite the importance of these dependencies, widely adopted image-level pretrained backbones barely capture the dynamics in the consecutive video, which is the essence of tracking. Thus, we propose AutoRegressive Sequential Pretraining (ARP), an unsupervised spatio-temporal learner, via generating the evolution of object appearance and motion in video sequences.Our method leverages a diffusion model to autoregressively generate the future frame appearance, conditioned on historical embeddings extracted by a general encoder. Furthermore, to ensure trajectory coherence, the same encoder is employed to learn trajectory consistency by generating coordinate sequences in a reverse autoregressive fashion, a process we term back-tracking. Further, we integrate the pretrained ARP into ARTrackV2, creating ARPTrack, which is further fine-tuned for tracking tasks. ARPTrack achieves state-of-the-art performance across multiple benchmarks, becoming the first tracker to surpass 80% AO on GOT-10k, while maintaining high efficiency. These results demonstrate the effectiveness of our approach in capturing temporal dependencies for continuous video tracking.