GenAI4ELab/papercli-papers huggingface dataset:
CamPoint: Boosting Point Cloud Segmentation with Virtual Camera
Authors: Jianhui Zhang; Yizhi Luo; Zicheng Zhang; Xuecheng Nie; Bonan Li
Venue: CVPR 2025 (main)

Abstract: Local features aggregation and global information perception are the fundamental to point cloud segmentation. However, existing works often fall short in effectively identifying semantic relevant neighbors and face challenges in endowing each point with high-level information. Here, we propose CamPoint, an innovative method that employs virtual cameras to solve above problems. The core of CamPoint lies in introducing the novel camera visibility feature for points, where each dimension encodes the visibility of that point from a specific camera. Leveraging this feature, we propose the camera perspective slice distance for accurate relevant neighbor searching and design the camera parameter embedding to deliver rich feature representations for global interaction. Specifically, the camera perspective slice distance between two points is defined as a similarity metric derived from their camera visibility features, whereby an increased number of shared cameras observing both points corresponds to a reduced distance between them. To effectively facilitate global semantic perception, we assign each camera an optimizable embedding and then integrate these embeddings into the original spatial features based on visibility attributes, thereby obtaining high-level features enriched with camera priors. Additionally, the state space model characterized by linear computational complexity is employed as the operator to achieve global learning with efficiency. Comprehensive experiments on multiple datasets shows that our CamPoint surpasses the current state-of-the-art in multiple datasets, achieving low training cost and fast inference speed.
LightLoc: Learning Outdoor LiDAR Localization at Light Speed
Authors: Wen Li; Chen Liu; Shangshu Yu; Dunqiang Liu; Yin Zhou; Siqi Shen; Chenglu Wen; Cheng Wang
Venue: CVPR 2025 (main)

Abstract: Scene coordinate regression achieves impressive results in outdoor LiDAR localization but requires days of training. Since training needs to be repeated for each new scene, long training times make these impractical for applications requiring time-sensitive system upgrades, such as autonomous driving, drones, robotics, etc. We identify large coverage areas and vast amounts of data in large-scale outdoor scenes as key challenges that limit fast training. In this paper, we propose LightLoc, the first method capable of efficiently learning localization in a new scene at light speed. Beyond freezing the scene-agnostic feature backbone and training only the scene-specific prediction heads, we introduce two novel techniques to address these challenges. First, we introduce sample classification guidance to assist regression learning, reducing ambiguity from similar samples and improving training efficiency. Second, we propose redundant sample downsampling to remove well-learned frames during training, reducing training time without compromising accuracy. In addition, the fast training and confidence estimation characteristics of sample classification enable its integration into SLAM, effectively eliminating error accumulation. Extensive experiments on large-scale outdoor datasets demonstrate that LightLoc achieves state-of-the-art performance with just 1 hour of training--50x faster than existing methods. Our Code is available at https://github.com/liw95/LightLoc.
MERGE: Multi-faceted Hierarchical Graph-based GNN for Gene Expression Prediction from Whole Slide Histopathology Images
Authors: Aniruddha Ganguly; Debolina Chatterjee; Wentao Huang; Jie Zhang; Alisa Yurovsky; Travis Steele Johnson; Chao Chen
Venue: CVPR 2025 (main)

Abstract: Recent advances in Spatial Transcriptomics (ST) pair histology images with spatially resolved gene expression profiles, enabling predictions of gene expression across different tissue locations based on image patches. This opens up new possibilities for enhancing whole slide image (WSI) prediction tasks with localized gene expression. However, existing methods fail to fully leverage the interactions between different tissue locations, which are crucial for accurate joint prediction. To address this, we introduce MERGE (Multi-faceted hiErarchical gRaph for Gene Expressions), which combines a multi-faceted hierarchical graph construction strategy with graph neural networks (GNN) to improve gene expression predictions from WSIs. By clustering tissue image patches based on both spatial and morphological features, and incorporating intra- and inter-cluster edges, our approach fosters interactions between distant tissue locations during GNN learning. As an additional contribution, we evaluate different data smoothing techniques that are necessary to mitigate artifacts in ST data, often caused by technical imperfections. We advocate for adopting gene-aware smoothing methods that are more biologically justified. Experimental results on gene expression prediction show that our GNN method outperforms state-of-the-art techniques across multiple metrics.
Accurate Differential Operators for Hybrid Neural Fields
Authors: Aditya Chetan; Guandao Yang; Zichen Wang; Steve Marschner; Bharath Hariharan
Venue: CVPR 2025 (main)

Abstract: Neural fields have become widely used in various fields, from shape representation to neural rendering, and for solving partial differential equations (PDEs). With the advent of hybrid neural field representations like Instant NGP that leverage small MLPs and explicit representations, these models train quickly and can fit large scenes. Yet in many applications like rendering and simulation, hybrid neural fields can cause noticeable and unreasonable artifacts. This is because they do not yield accurate spatial derivatives needed for these downstream applications. In this work, we propose two ways to circumvent these challenges. Our first approach is a post hoc operator that uses local polynomial fitting to obtain more accurate derivatives from pre-trained hybrid neural fields. Additionally, we also propose a self-supervised fine-tuning approach that refines the hybrid neural field to yield accurate derivatives directly while preserving the initial signal. We show applications of our method to rendering, collision simulation, and solving PDEs. We observe that using our approach yields more accurate derivatives, reducing artifacts and leading to more accurate simulations in downstream applications.
FeedEdit: Text-Based Image Editing with Dynamic Feedback Regulation
Authors: Fengyi Fu; Lei Zhang; Mengqi Huang; Zhendong Mao
Venue: CVPR 2025 (main)

Abstract: Text-based image editing which aims at generating rigid or non-rigid changes to images conditioned on the given text, has recently attracted considerable interest. Previous works mainly follow the multi-step denoising diffusion paradigm, which adopts a fixed text guidance intensity (i.e., editing intensity) to inject textual features, while ignoring the step-specific editing requirements. This work argues that the editing intensity at each denoising step should be adaptively adjusted conditioned on the historical editing degree, to provide accurate text guidance for the whole denoising process. We thereby propose a novel feedback editing framework (FeedEdit), a training-free method which, explicitly exploits the feedback regulation on editing intensity to ensure precise and harmonious editing at all steps. Specifically, we design (1) Dynamic Editing Degree Perceiving module, which is based on specific frequency-domain filtering, to enhance and exploit the correlation between feature differences and editing degree for perceiving.  (2) Proportional-Integral feedback controller, to automatically map the perceived editing errors into appropriate feedback control signals. (3) Phrase-level Regulating Strategy, to achieve fine-grained function-specific regulation of textual features. Extensive experiments demonstrate the superiority of FeedEdit over existing methods in both editability and quality, especially for multi-function editing scenarios.
Classifier-guided CLIP Distillation for Unsupervised Multi-label Classification
Authors: Dongseob Kim; Hyunjung Shim
Venue: CVPR 2025 (main)

Abstract: Multi-label classification is crucial for comprehensive image understanding, yet acquiring accurate annotations is challenging and costly. To address this, a recent study suggests exploiting unsupervised multi-label classification leveraging CLIP, a powerful vision-language model. Despite CLIP's proficiency, it suffers from view-dependent predictions and inherent bias, limiting its effectiveness. We propose a novel method that addresses these issues by leveraging multiple views near target objects, guided by Class Activation Mapping (CAM) of the classifier, and debiasing pseudo-labels derived from CLIP predictions. Our Classifier-guided CLIP Distillation (CCD) enables selecting multiple local views without extra labels and debiasing predictions to enhance classification performance. Experimental results validate our method's superiority over existing techniques across diverse datasets. The code is available at https://github.com/k0u-id/CCD.
UMotion: Uncertainty-driven Human Motion Estimation from Inertial and Ultra-wideband Units
Authors: Huakun Liu; Hiroki Ota; Xin Wei; Yutaro Hirao; Monica Perusquia-Hernandez; Hideaki Uchiyama; Kiyoshi Kiyokawa
Venue: CVPR 2025 (main)

Abstract: Sparse wearable inertial measurement units (IMUs) have gained popularity for estimating 3D human motion. However, challenges such as pose ambiguity, data drift, and limited adaptability to diverse bodies persist. To address these issues, we propose UMotion, an uncertainty-driven, online fusing-all state estimation framework for 3D human shape and pose estimation, supported by six integrated, body-worn ultra-wideband (UWB) distance sensors with IMUs. UWB sensors measure inter-node distances to infer spatial relationships, aiding in resolving pose ambiguities and body shape variations when combined with anthropometric data. Unfortunately, IMUs are prone to drift, and UWB sensors are affected by body occlusions. Consequently, we develop a tightly coupled Unscented Kalman Filter (UKF) framework that fuses uncertainties from sensor data and estimated human motion based on individual body shape. The UKF iteratively refines IMU and UWB measurements by aligning them with uncertain human motion constraints in real-time, producing optimal estimates for each. Experiments on both synthetic and real-world datasets demonstrate the effectiveness of UMotion in stabilizing sensor data and the improvement over state of the art in pose accuracy. Code is available at: https://github.com/kk9six/umotion.
STEREO: A Two-Stage Framework for Adversarially Robust Concept Erasing from Text-to-Image Diffusion Models
Authors: Koushik Srivatsan; Fahad Shamshad; Muzammal Naseer; Vishal M. Patel; Karthik Nandakumar
Venue: CVPR 2025 (main)

Abstract: The rapid proliferation of large-scale text-to-image diffusion (T2ID) models has raised serious concerns about their potential misuse in generating harmful content.  Although numerous methods have been proposed for erasing undesired concepts from T2ID models, they often provide a false sense of security; concept-erased models (CEMs) can still be manipulated via adversarial attacks to regenerate the erased concept. While a few robust concept erasure methods based on adversarial training have emerged recently, they compromise on utility (generation quality for benign concepts) to achieve robustness and/or remain vulnerable to advanced embedding space attacks. These limitations stem from the failure of robust CEMs to thoroughly search for blind spots in the embedding space. To bridge this gap, we propose STEREO, a novel two-stage framework that employs adversarial training as a first step rather than the only step for robust concept erasure. In the first stage, STEREO employs adversarial training as a vulnerability identification mechanism to search thoroughly enough. In the second robustly erase once stage, STEREO introduces an anchor-concept-based compositional objective to robustly erase the target concept in a single fine-tuning stage, while minimizing the degradation of model utility. We benchmark STEREO against seven state-of-the-art concept erasure methods, demonstrating its superior robustness to both white-box and black-box attacks, while largely preserving utility.
Scene Map-based Prompt Tuning for Navigation Instruction Generation
Authors: Sheng Fan; Rui Liu; Wenguan Wang; Yi Yang
Venue: CVPR 2025 (main)

Abstract: Navigation instruction generation (NIG), which provides interactive feedback and guidance to humans along a trajectory, is vital for developing embodied agents capable of human-machine communication and collaboration through natural language. Early data-driven methods directly map sequences of past observations to trajectory descriptions on limited datasets, lacking the necessary spatial understanding in complex 3D environments. While recent approaches leverage Large Language Models (LLMs) to improve NIG, they often overlook the global spatial context in navigation, such as the inherent space discretization in maps. Instead of straightforwardly feeding textual descriptions of the map into LLMs, we propose a scene map-based prompt tuning framework for NIG, MAPInstructor, which incorporates map context for parameter-efficient updating of LLMs. MAPInstructor comprises three key components: (i) scene representation encoding, where egocentric observations are projected into 3D voxels for fine-grained scene understanding; (ii) map prompt tuning, which integrates a topological map representation of the entire trajectory into an LLM-based decoder; and (iii) landmark uncertainty assessment, which mitigates hallucinations in landmark predictions, thereby enhancing the reliability and coherence of instruction generation. Extensive experiments on three navigation datasets (i.e., R2R, REVERIE, RxR) confirm the generalization and effectiveness of our algorithm.
GenVDM: Generating Vector Displacement Maps From a Single Image
Authors: Yuezhi Yang; Qimin Chen; Vladimir G. Kim; Siddhartha Chaudhuri; Qixing Huang; Zhiqin Chen
Venue: CVPR 2025 (main)

Abstract: We introduce the first method for generating Vector Displacement Maps (VDMs): parameterized, detailed geometric stamps commonly used in 3D modeling. Given a single input image, our method first generates multi-view normal maps and then reconstructs a VDM from the normals via a novel reconstruction pipeline. We also propose an efficient algorithm for extracting VDMs from 3D objects, and present the first academic VDM dataset. Compared to existing 3D generative models focusing on complete shapes, we focus on generating parts that can be seamlessly attached to shape surfaces. The method gives artists rich control over adding geometric details to a 3D shape. Experiments demonstrate that our approach outperforms existing baselines. Generating VDMs offers additional benefits, such as using 2D image editing to customize and refine 3D details.
DropoutGS: Dropping Out Gaussians for Better Sparse-view Rendering
Authors: Yexing Xu; Longguang Wang; Minglin Chen; Sheng Ao; Li Li; Yulan Guo
Venue: CVPR 2025 (main)

Abstract: Although 3D Gaussian Splatting (3DGS) has demonstrated promising results in novel view synthesis, its performance degrades dramatically with sparse inputs and generates undesirable artifacts. As the number of training views decreases, the novel view synthesis task degrades to a highly under-determined problem such that existing methods suffer from the notorious overfitting issue. Interestingly, we observe that models with fewer Gaussian primitives exhibit less overfitting under spare inputs. Inspired by this observation, we propose a Random Dropout Regularization (RDR) to exploit the advantages of low-complexity models to alleviate overfitting. In addition, to remedy the lack of high-frequency details for these models, an Edge-guided Splitting Strategy (ESS) is developed. With these two techniques, our method (termed DropoutGS) provides a simple yet effective plug-in approach to improve the generalization performance of existing 3DGS methods. Extensive experiments show that our DropoutGS produces state-of-the-art performance under sparse views on benchmark datasets including Blender, LLFF, and DTU.
Effective SAM Combination for Open-Vocabulary Semantic Segmentation
Authors: Minhyeok Lee; Suhwan Cho; Jungho Lee; Sunghun Yang; Heeseung Choi; Ig-Jae Kim; Sangyoun Lee
Venue: CVPR 2025 (main)

Abstract: Open-vocabulary semantic segmentation aims to assign pixel-level labels to images across an unlimited range of classes. Traditional methods address this by sequentially connecting a powerful mask proposal generator, such as the Segment Anything Model (SAM), with a pre-trained vision-language model like CLIP. But these two-stage approaches often suffer from high computational costs, memory inefficiencies. In this paper, we propose ESC-Net, a novel one-stage open-vocabulary segmentation model that leverages the SAM decoder blocks for class-agnostic segmentation within an efficient inference framework. By embedding pseudo prompts generated from image-text correlations into SAM's promptable segmentation framework, ESC-Net achieves refined spatial aggregation for accurate mask predictions. Additionally, a Vision-Language Fusion (VLF) module enhances the final mask prediction through image and text guidance. ESC-Net achieves superior performance on standard benchmarks, including ADE20K, PASCAL-VOC, and PASCAL-Context, outperforming prior methods in both efficiency and accuracy. Comprehensive ablation studies further demonstrate its robustness across challenging conditions.
Bridging Modalities: Improving Universal Multimodal Retrieval by Multimodal Large Language Models
Authors: Xin Zhang; Yanzhao Zhang; Wen Xie; Mingxin Li; Ziqi Dai; Dingkun Long; Pengjun Xie; Meishan Zhang; Wenjie Li; Min Zhang
Venue: CVPR 2025 (main)

Abstract: Universal Multimodal Retrieval (UMR) aims to enable search across various modalities using a unified model, where queries and candidates can consist of pure text, images, or a combination of both. Previous work has attempted to adopt multimodal large language models (MLLMs) to realize UMR using only text data. However, our preliminary experiments demonstrate that more diverse multimodal training data can further unlock the potential of MLLMs. Despite its effectiveness, the existing multimodal training data is highly imbalanced in terms of modality, which motivates us to develop a training data synthesis pipeline and construct a large-scale, high-quality fused-modal training dataset. Based on the synthetic training data, we develop the General Multimodal Embedder (GME), an MLLM-based dense retriever designed for UMR. Furthermore, we construct a comprehensive UMR Benchmark (UMRB) to evaluate the effectiveness of our approach. Experimental results show that our method achieves state-of-the-art performance among existing UMR methods. Last, we provide in-depth analyses of model scaling and training strategies, and perform ablation studies on both the model and synthetic data.
Enhancing Dataset Distillation via Non-Critical Region Refinement
Authors: Minh-Tuan Tran; Trung Le; Xuan-May Le; Thanh-Toan Do; Dinh Phung
Venue: CVPR 2025 (main)

Abstract: Dataset distillation has gained popularity as a technique for compressing large datasets into smaller, more efficient representations while retaining essential information for model training.  Data features can be broadly divided into two types: instance-specific features, which capture unique, fine-grained details of individual examples, and class-general features, which represent shared, broad patterns across a class. However, previous approaches often struggle to balance these; some focus solely on class-general features, missing finer instance details, while others concentrate on instance-specific features, overlooking the shared characteristics essential for class-level understanding. In this paper, we propose the Non-Critical Region Refinement Dataset Distillation (NRR-DD) method, which preserves the instance-specific and fine-grained regions in synthetic data while enriching non-critical regions with more class-general information. This approach enables our models to leverage all pixel information to capture both types of features, thereby improving overall performance. Furthermore, we introduce Distance-Based Representative (DBR) knowledge transfer, which eliminates the need for soft labels in training by relying solely on the distance between synthetic data predictions and one-hot encoded labels. Experimental results demonstrate that our NRR-DD achieves state-of-the-art performance on both small-scale and large-scale datasets. Additionally, by storing only two distances per instance, our method achieves comparable results across various settings. Code will be available at https://github.com/tmtuan1307/NRR-DD.
Towards Visual Discrimination and Reasoning of Real-World Physical Dynamics: Physics-Grounded Anomaly Detection
Authors: Wenqiao Li; Yao Gu; Xintao Chen; Xiaohao Xu; Ming Hu; Xiaonan Huang; Yingna Wu
Venue: CVPR 2025 (main)

Abstract: Humans detect real-world object anomalies by perceiving, interacting, and reasoning based on object-conditioned physical knowledge. The long-term goal of Industrial Anomaly Detection (IAD) is to enable machines to autonomously replicate this skill. However, current IAD algorithms are largely developed and tested on static, semantically simple datasets, which diverge from real-world scenarios where physical understanding and reasoning are essential.To bridge this gap, we introduce the Physics Anomaly Detection (Phys-AD) dataset, the first large-scale, real-world, physics-grounded video dataset for industrial anomaly detection. Collected using a real robot arm and motor, Phys-AD provides a diverse set of dynamic, semantically rich scenarios. The dataset includes more than 6400 videos across 22 real-world object categories, interacting with robot arms and motors, and exhibits 47 types of anomalies. Anomaly detection in Phys-AD requires visual reasoning, combining both physical knowledge and video content to determine object abnormality.We benchmark state-of-the-art anomaly detection methods under three settings: unsupervised AD, weakly-supervised AD, and video-understanding AD, highlighting their limitations in handling physics-grounded anomalies. Additionally, we introduce the Physics Anomaly Explanation (PAEval) metric, designed to assess the ability of visual-language foundation models to not only detect anomalies but also provide accurate explanations for their underlying physical causes. Our project is available at https://guyao2023.github.io/Phys-AD/.