GenAI4ELab/papercli-papers huggingface dataset:
PBFG: A New Physically-Based Dataset and Removal of Lens Flares and Glares
Authors: Jie Zhu; Sungkil Lee
Venue: ICCV 2025 (main)

Abstract: Flare and glare are common nighttime artifacts that degrade image quality and hinder computer vision tasks. Existing synthetic datasets lack physical realism and diversity, while deep learning-based removal methods struggle in complex scenes, posing significant challenges. To address these issues, we introduce the high-quality annotated Physically-Based Flare and Glare (PBFG) dataset and a Flare and Glare Removal Network (FGRNet). PBFG comprises 2,600 flares and 4,000 glares using our computational rendering scheme with diverse lens systems and optical configurations. Our advanced streak synthesis enhances template fidelity and improves streak removal accuracy. FGRNet leverages spatial-frequency features for comprehensive local and global feature extraction. It introduces a Spatial-Frequency Enhanced Module with a Spatial Reconstruction Unit and a Frequency-Enhanced Unit to extract multi-scale spatial information and enhance frequency representation. This design effectively removes complex artifacts, including large-area glares, diverse flares, and multiple or off-screen-induced streaks. Additionally, a histogram-matching module ensures stylistic and visual consistency with ground truth. Extensive experiments confirm that PBFG accurately replicates real-world patterns, and FGRNet outperforms state-of-the-art methods both quantitatively and qualitatively, resulting in significant gains of PSNRs (up to 2.3 dB and 3.14 dB in an image and its glare regions, respectively).
Active Perception Meets Rule-Guided RL: A Two-Phase Approach for Precise Object Navigation in Complex Environments
Authors: Liang Qin; Min Wang; Peiwei Li; Wengang Zhou; Houqiang Li
Venue: ICCV 2025 (main)

Abstract: Object Goal Navigation (ObjectNav) in unknown environments presents significant challenges, particularly in Open-Vocabulary Mobile Manipulation (OVMM), where robots must efficiently explore large spaces, locate small objects, and accurately position themselves for subsequent manipulation. Existing approaches struggle to meet these demands: rule-based methods offer structured exploration but lack adaptability, while reinforcement learning (RL)-based methods enhance adaptability but fail to ensure effective long-term navigation. Moreover, both approaches often overlook precise stopping positions, which are critical for successful manipulation.To address these challenges, we propose APRR (Active Perception Meets Rule-Guided RL), a two-phase framework that designs a new rule-guided RL policy for the exploration phase and a novel active target perception policy for the last-mile navigation phase. Inspired by human search behavior, our rule-guided RL policy enables efficient and adaptive exploration by combining structured heuristics with learning-based decision-making. In the last-mile navigation phase, we introduce an RL-based policy enhanced with active target perception, allowing the robot to refine its position dynamically based on real-time detection feedback. Experimental results demonstrate that APRR improves the success rate by 13%, significantly outperforming existing methods. Furthermore, real-world experiments validate the practicality and effectiveness of APRR in real-world mobile manipulation scenarios, offering a robust and adaptable solution for precise object navigation. The code is available at https://github.com/qinliangql/APRR.
CAT: A Unified Click-and-Track Framework for Realistic Tracking
Authors: Yongsheng Yuan; Jie Zhao; Dong Wang; Huchuan Lu
Venue: ICCV 2025 (main)

Abstract: Modern visual trackers have achieved robust performance with precisely initialized target bounding boxes. However, providing high-precision initial annotations is a process both labor-intensive and error-prone in real-world scenarios. Interactive initialization (e.g., click-based, scribble-based) presents a more practical alternative. In this paper, we introduce a unified Click-and-Track (CAT) framework for full-process tracking, eliminating the need for auxiliary models or complex initialization pipelines. We present a novel fine-tuning paradigm that bridges the information gap inherent in click-based initialization through two key innovations: 1) The proposed click-based localization and joint spatial-visual prompt refinement are sequentially performed to compensate for the geometric information loss (e.g., boundary ambiguity, shape uncertainty) inherent in click-based initialization. 2) We design a parameter-efficient module called CTMoE to leverage the tracker's inherent capabilities when fine-tuning. The proposed CTMoE enables the foundation model to learn different matching patterns, unifying click-based initialization and tracking within a unified architecture. Extensive experimental results demonstrate state-of-the-art performance of our click-based tracking method on the LaSOT benchmark (70.5% AUC) while maintaining parameter efficiency, surpassing existing click-based tracking frameworks by a large margin and even outperforming some bounding-box-initialized trackers. The code and models are available at https://github.com/ysyuann/CAT.
Differential-informed Sample Selection Accelerates Multimodal Contrastive Learning
Authors: Zihua Zhao; Feng Hong; Mengxi Chen; Pengyi Chen; Benyuan Liu; Jiangchao Yao; Ya Zhang; Yanfeng Wang
Venue: ICCV 2025 (main)

Abstract: The remarkable success of contrastive-learning-based multimodal models has been greatly driven by training on ever-larger datasets with expensive compute consumption. Sample selection as an alternative efficient paradigm plays an important direction to accelerate the training process. However, recent advances on sample selection either mostly rely on an oracle model to offline select a high-quality coreset, which is limited in the cold-start scenarios, or focus on online selection based on real-time model predictions, which has not sufficiently or efficiently considered the noisy correspondence. To address this dilemma, we propose a novel Differential-Informed Sample Selection (DISSect) method, which accurately and efficiently discriminates the noisy correspondence for training acceleration. Specifically, we rethink the impact of noisy correspondence on contrastive learning and propose that the differential between the predicted correlation of the current model and that of a historical model is more informative to characterize sample quality. Based on this, we construct a robust differential-based sample selection and analyze its theoretical insights. Extensive experiments on three benchmark datasets and various downstream tasks demonstrate the consistent superiority of DISSect over current state-of-the-art methods. Source code is available at: https://github.com/MediaBrain-SJTU/DISSect.
A View-consistent Sampling Method for Regularized Training of Neural Radiance Fields
Authors: Aoxiang Fan; Corentin Dumery; Nicolas Talabot; Pascal Fua
Venue: ICCV 2025 (main)

Abstract: Neural Radiance Fields (NeRF) has emerged as a compelling framework for scene representation and 3D recovery. To improve its performance on real-world data, depth regularizations have proven to be the most effective ones. However, depth estimation models not only require expensive 3D supervision in training, but also suffer from generalization issues. As a result, the depth estimations can be erroneous in practice, especially for outdoor unbounded scenes. In this paper, we propose to employ view-consistent distributions instead of fixed depth value estimations to regularize NeRF training. Specifically, the distribution is computed by utilizing both low-level color features and high-level distilled features from foundation models at the projected 2D pixel-locations from per-ray sampled 3D points. By sampling from the view-consistency distributions, an implicit regularization is imposed on the training of NeRF. We also propose a novel depth-pushing loss that works in conjunction with the sampling technique to jointly provide effective regularizations for eliminating the failure modes. Extensive experiments conducted on various scenes from public datasets demonstrate that our proposed method can generate significantly better novel view synthesis results than state-of-the-art NeRF variants as well as different depth regularization methods.
LUT-Fuse: Towards Extremely Fast Infrared and Visible Image Fusion via Distillation to Learnable Look-Up Tables
Authors: Xunpeng Yi; Yibing Zhang; Xinyu Xiang; Qinglong Yan; Han Xu; Jiayi Ma
Venue: ICCV 2025 (main)

Abstract: Current advanced research on infrared and visible image fusion primarily focuses on improving fusion performance, often neglecting the applicability on real-time fusion devices. In this paper, we propose a novel approach that towards extremely fast fusion via distillation to learnable lookup tables specifically designed for image fusion, termed as LUT-Fuse. Firstly, we develop a look-up table structure that utilizing low-order approximation encoding and high-level joint contextual scene encoding, which is well-suited for multi-modal fusion. Moreover, given the lack of ground truth in multi-modal image fusion, we naturally proposed the efficient LUT distillation strategy instead of traditional quantization LUT methods. By integrating the performance of the multi-modal fusion network (MM-Net) into the MM-LUT model, our method achieves significant breakthroughs in efficiency and performance. It typically requires less than one-tenth of the time compared to the current lightweight SOTA fusion algorithms, ensuring high operational speed across various scenarios, even in low-power mobile devices. Extensive experiments validate the superiority, reliability, and stability of our fusion approach. The code is available at https://github.com/zyb5/LUT-Fuse.
AdsQA: Towards Advertisement Video Understanding
Authors: Xinwei Long; Kai Tian; Peng Xu; Guoli Jia; Jingxuan Li; Sa Yang; Yihua Shao; Kaiyan Zhang; Che Jiang; Hao Xu; Yang Liu; Jiaheng Ma; Bowen Zhou
Venue: ICCV 2025 (main)

Abstract: Large language models (LLMs) have taken a great step towards AGI. Meanwhile, an increasing number of domain-specific problems such as math and programming boost these general-purpose models to continuously evolve via learning deeper expertise. Now is thus the time further to extend the diversity of specialized applications for knowledgeable LLMs, though collecting high quality data with unexpected and informative tasks is challenging. In this paper, we propose to use advertisement (ad) videos as a challenging test-bed to probe the ability of LLMs in perceiving beyond the objective physical content of common visual domain. Our motivation is to take full advantage of the clue-rich and information-dense ad videos' traits, e.g., marketing logic, persuasive strategies, and audience engagement. Our contribution is three-fold: (1) To our knowledge, this is the first attempt to use ad videos with well-designed tasks to evaluate LLMs. We contribute AdsQA, a challenging ad Video QA benchmark derived from 1,544 ad videos with 10,962 clips, totaling 22.7 hours, providing 5 challenging tasks. (2) We propose ReAd-R, a Deepseek-R1 styled RL model that reflects on questions, and generates answers via reward-driven optimization. (3) We benchmark  14  top-tier LLMs on AdsQA, and our ReAd-R achieves the state-of-the-art outperforming strong competitors equipped with long-chain reasoning capabilities by a clear margin.
Fine-grained Abnormality Prompt Learning for Zero-shot Anomaly Detection
Authors: Jiawen Zhu; Yew-Soon Ong; Chunhua Shen; Guansong Pang
Venue: ICCV 2025 (main)

Abstract: Current zero-shot anomaly detection (ZSAD) methods show remarkable success in prompting large pre-trained vision-language models to detect anomalies in a target dataset without using any dataset-specific training or demonstration. However, these methods often focus on crafting/learning prompts that capture only coarse-grained semantics of abnormality, e.g., high-level semantics like 'damaged', 'imperfect', or 'defective' objects. They therefore have limited capability in recognizing diverse abnormality details that deviate from these general abnormal patterns in various ways. To address this limitation, we propose FAPrompt, a novel framework designed to learn Fine-grained Abnormality Prompts for accurate ZSAD. To this end, a novel Compound Abnormality Prompt learning (CAP) module is introduced in FAPrompt to learn a set of complementary, decomposed abnormality prompts, where abnormality prompts are enforced to model diverse abnormal patterns derived from the same normality semantic. On the other hand, the fine-grained abnormality patterns can be different from one dataset to another. To enhance the cross-dataset generalization, another novel module, namely Data-dependent Abnormality Prior learning (DAP), is introduced in FAPrompt to learn a sample-wise abnormality prior from abnormal features of each test image to dynamically adapt the abnormality prompts to individual test images. Comprehensive experiments on 19 real-world datasets, covering both industrial defects and medical anomalies, demonstrate that FAPrompt substantially outperforms state-of-the-art methods in both image- and pixel-level ZSAD tasks. Code is available at https:// github.com/ mala-lab/ FAPrompt.
Learning Dense Feature Matching via Lifting Single 2D Image to 3D Space
Authors: Yingping Liang; Yutao Hu; Wenqi Shao; Ying Fu
Venue: ICCV 2025 (main)

Abstract: Feature matching plays a fundamental role in many computer vision tasks, yet existing methods rely on scarce and clean multi-view image collections, which constrains their generalization to diverse and challenging scenarios. Moreover, conventional feature encoders are typically trained on single-view 2D images, limiting their capacity to capture 3D-aware correspondences. In this paper, we propose a novel two-stage framework that lifts 2D images to 3D space, named as Lift to Match (L2M), taking full advantage of large-scale and diverse single-view images. To be specific, in the first stage, we learn a 3D-aware feature encoder using a combination of multi-view image synthesis and 3D feature Gaussian representation, which injects 3D geometry knowledge into the encoder. In the second stage, a novel-view rendering strategy, combined with large-scale synthetic data generation from single-view images, is employed to learn a feature decoder for robust feature matching, thus achieving generalization across diverse domains. Extensive experiments demonstrate that our method achieves superior generalization across zero-shot evaluation benchmarks, highlighting the effectiveness of the proposed framework for robust feature matching.
DNF-Intrinsic: Deterministic Noise-Free Diffusion for Indoor Inverse Rendering
Authors: Rongjia Zheng; Qing Zhang; Chengjiang Long; Wei-Shi Zheng
Venue: ICCV 2025 (main)

Abstract: Recent methods have shown that pre-trained diffusion models can be fine-tuned to enable generative inverse rendering by learning image-conditioned noise-to-intrinsic mapping. Despite their remarkable progress, they struggle to robustly produce high-quality results as the noise-to-intrinsic paradigm essentially utilizes noisy images with deteriorated structure and appearance for intrinsic prediction, while it is common knowledge that structure and appearance information in an image are crucial for inverse rendering. To address this issue, we present DNF-Intrinsic, a robust yet efficient inverse rendering approach fine-tuned from a pre-trained diffusion model, where we propose to take the source image rather than Gaussian noise as input to directly predict deterministic intrinsic properties via flow matching. Moreover, we design a generative renderer to constrain that the predicted intrinsic properties are physically faithful to the source image. Experiments on both synthetic and real-world datasets show that our method clearly outperforms existing state-of-the-art methods. Our code is available at https://github.com/OnlyZZZZ/DNF-Intrinsic.
Unified Open-World Segmentation with Multi-Modal Prompts
Authors: Yang Liu; Yufei Yin; Chenchen Jing; Muzhi Zhu; Hao Chen; Yuling Xi; Bo Feng; Hao Wang; Shiyu Li; Chunhua Shen
Venue: ICCV 2025 (main)

Abstract: In this work, we present COSINE, a unified open-world segmentation model that Consolidates Open-vocabulary Segmentation and IN-context sEgmentation with multi-modal prompts (e.g., text and image). COSINE exploits foundation models to extract representations for an input image and corresponding multi-modal prompts, and a SegDecoder to align these representations, model their interaction, and obtain masks specified by input prompts across different granularities. In this way, COSINE overcomes architectural discrepancies, divergent learning objectives, and distinct representation learning strategies of previous pipelines for open-vocabulary segmentation and in-context segmentation. Comprehensive experiments demonstrate that COSINE has significant performance improvements in both open-vocabulary and in-context segmentation tasks. Our exploratory analyses highlight that the synergistic collaboration between using visual and textual prompts leads to significantly improved generalization over single-modality approaches.
GausSim: Foreseeing Reality by Gaussian Simulator for Elastic Objects
Authors: Yidi Shao; Mu Huang; Chen Change Loy; Bo Dai
Venue: ICCV 2025 (main)

Abstract: We introduce GausSim, a novel neural network-based simulator designed to capture the dynamic behaviors of real-world elastic objects represented through Gaussian kernels. We leverage continuum mechanics and treat each kernel as a Center of Mass System (CMS) that describes continuous piece of matter, accounting for realistic deformations without idealized assumptions. To improve computational efficiency and fidelity, we employ a hierarchical structure that further organizes kernels into CMSs with explicit formulations, enabling a coarse-to-fine simulation approach. This structure significantly reduces computational overhead while preserving detailed dynamics. In addition, GausSim incorporates explicit physics constraints, such as mass and momentum conservation, ensuring interpretable results and robust, physically plausible simulations. To validate our approach, we present a new dataset, READY, containing multi-view videos of real-world elastic deformations. Experimental results demonstrate that GausSim achieves superior performance compared to existing physics-driven baselines, offering a practical and accurate solution for simulating complex dynamic behaviors. Code and model are available at https://www.mmlab-ntu.com/project/gausim/index.html.
ClaraVid: A Holistic Scene Reconstruction Benchmark From Aerial Perspective With Delentropy-Based Complexity Profiling
Authors: Radu Beche; Sergiu Nedevschi
Venue: ICCV 2025 (main)

Abstract: The development of aerial holistic scene understanding algorithms is hindered by the scarcity of comprehensive datasets that enable both semantic and geometric reconstruction. While synthetic datasets offer an alternative, existing options exhibit task-specific limitations, unrealistic scene compositions, and rendering artifacts that compromise real-world applicability. We introduce ClaraVid, a synthetic aerial dataset specifically designed to overcome these limitations. Comprising 16,917 high-resolution images captured at 4032x3024 from multiple viewpoints across diverse landscapes, ClaraVid provides dense depth maps, panoptic segmentation, sparse point clouds, and dynamic object masks, while mitigating common rendering artifacts. To further advance neural reconstruction, we introduce the Delentropic Scene Profile (DSP), a novel complexity metric derived from differential entropy analysis, designed to quantitatively assess scene difficulty and inform reconstruction tasks. Utilizing DSP, we systematically benchmark neural reconstruction methods, uncovering a consistent, measurable correlation between scene complexity and reconstruction accuracy. Empirical results indicate that higher delentropy strongly correlates with increased reconstruction errors, validating DSP as a reliable complexity prior. The data and code are available on the project page: https://rdbch.github.com/claravid.
TextMaster: A Unified Framework for Realistic Text Editing via Glyph-Style Dual-Control
Authors: Zhenyu Yan; Jian Wang; Aoqiang Wang; Yuhan Li; Wenxiang Shang; Zhu Hangcheng
Venue: ICCV 2025 (main)

Abstract: In image editing tasks, high-quality text editing capabilities can significantly reduce both human and material resource costs. Existing methods, however, face significant limitations in terms of stroke accuracy for complex text and controllability of generated text styles. To address these challenges, we propose TextMaster, a solution capable of accurately editing text across various scenarios and image regions, while ensuring proper layout and controllable text style. Our method enhances the accuracy and fidelity of text rendering by incorporating high-resolution standard glyph information and applying perceptual loss within the text editing region. Additionally, we leverage an attention mechanism to compute intermediate layer bounding box regression loss for each character, enabling the model to learn text layout across varying contexts. Furthermore, we propose a novel style injection technique that enables controllable style transfer for the injected text. Through comprehensive experiments, we demonstrate the state-of-the-art performance of our method.
X2-Gaussian: 4D Radiative Gaussian Splatting for Continuous-time Tomographic Reconstruction
Authors: Weihao Yu; Yuanhao Cai; Ruyi Zha; Zhiwen Fan; Chenxin Li; Yixuan Yuan
Venue: ICCV 2025 (main)

Abstract: Four-dimensional computed tomography (4D CT) reconstruction is crucial for capturing dynamic anatomical changes but faces inherent limitations from conventional phase-binning workflows. Current methods discretize temporal resolution into fixed phases with respiratory gating devices, introducing motion misalignment and restricting clinical practicality. In this paper, We propose X^2-Gaussian, a novel framework that enables continuous-time 4D-CT reconstruction by integrating dynamic radiative Gaussian splatting with self-supervised respiratory motion learning. Our approach models anatomical dynamics through a spatiotemporal encoder-decoder architecture that predicts time-varying Gaussian deformations, eliminating phase discretization. To remove dependency on external gating devices, we introduce a physiology-driven periodic consistency loss that learns patient-specific breathing cycles directly from projections via differentiable optimization. Extensive experiments demonstrate state-of-the-art performance, achieving a 9.93 dB PSNR gain over traditional methods and 2.25 dB improvement against prior Gaussian splatting techniques. By unifying continuous motion modeling with hardware-free period learning, X^2-Gaussian advances high-fidelity 4D CT reconstruction for dynamic clinical imaging. Code will be publicly available at https://github.com/CUHK-AIM-Group/X2-Gaussian.