GenAI4ELab/papercli-papers huggingface dataset:
Tracing Copied Pixels and Regularizing Patch Affinity in Copy Detection
Authors: Yichen Lu; Siwei Nie; Minlong Lu; Xudong Yang; Xiaobo Zhang; Peng Zhang
Venue: ICCV 2025 (main)

Abstract: Image Copy Detection (ICD) aims to identify manipulated content between image pairs through robust feature representation learning. While self-supervised learning (SSL) has advanced ICD systems, existing view-level contrastive methods struggle with sophisticated edits due to insufficient fine-grained correspondence learning. We address this limitation by exploiting the inherent geometric traceability in edited content through two key innovations. First, we propose PixTrace - a pixel coordinate tracking module that maintains explicit spatial mappings across editing transformations. Second, we introduce CopyNCE, a geometrically-guided contrastive loss that regularizes patch affinity using overlap ratios derived from PixTrace's verified mappings. Our method bridges pixel-level traceability with patch-level similarity learning, suppressing supervision noise in SSL training. Extensive experiments demonstrate not only state-of-the-art performance (88.7% mAP / 83.9% RP90 for matcher, 72.6% mAP / 68.4% RP90 for descriptor on DISC21 dataset) but also better interpretability over existing methods. Code is available.
InstructSeg: Unifying Instructed Visual Segmentation with Multi-modal Large Language Models
Authors: Cong Wei; Yujie Zhong; Haoxian Tan; Yingsen Zeng; Yong Liu; Hongfa Wang; Yujiu Yang
Venue: ICCV 2025 (main)

Abstract: Boosted by Multi-modal Large Language Models (MLLMs), text-guided universal segmentation models for the image and video domains have made rapid progress recently. However, these methods are often developed separately for specific domains, overlooking the similarities in task settings and solutions across these two areas. In this paper, we define the union of referring segmentation and reasoning segmentation at both the image and video levels as Instructed Visual Segmentation (IVS). Correspondingly, we propose InstructSeg, an end-to-end segmentation pipeline equipped with MLLMs for IVS. Specifically, we employ an object-aware video perceiver to extract temporal and object information from reference frames, facilitating comprehensive video understanding. Additionally, we introduce vision-guided multi-granularity text fusion to better integrate global and detailed text information with fine-grained visual guidance. By leveraging multi-task and end-to-end training, InstructSeg demonstrates superior performance across diverse image and video segmentation tasks, surpassing both segmentation specialists and MLLM-based methods with a single model.
Multispectral Demosaicing via Dual Cameras
Authors: SaiKiran Tedla; Junyong Lee; Beixuan Yang; Mahmoud Afifi; Michael S. Brown
Venue: ICCV 2025 (main)

Abstract: Multispectral (MS) images capture detailed scene information across a wide range of spectral bands, making them invaluable for applications requiring rich spectral data. Integrating MS imaging into multi-camera devices, such as smartphones, has the potential to enhance both spectral applications and RGB image quality. A critical step in processing MS data is demosaicing, which reconstructs color information from the mosaic MS images captured by the camera. This paper proposes a method for MS image demosaicing specifically designed for dual-camera setups where both RGB and MS cameras capture the same scene. Our approach leverages co-captured RGB images, which typically have higher spatial fidelity, to guide the demosaicing of lower-fidelity MS images. We introduce the Dual-camera RGB-MS Dataset - a large collection of paired RGB and MS mosaiced images with ground-truth demosaiced outputs - that enables training and evaluation of our method. Experimental results demonstrate that our method achieves state-of-the-art accuracy compared to existing techniques.
Reflect-DiT: Inference-Time Scaling for Text-to-Image Diffusion Transformers via In-Context Reflection
Authors: Shufan Li; Konstantinos Kallidromitis; Akash Gokul; Arsh Koneru; Yusuke Kato; Kazuki Kozuka; Aditya Grover
Venue: ICCV 2025 (main)

Abstract: The predominant approach to advancing text-to-image generation has been training-time scaling, where larger models are trained on more data using greater computational resources. While effective, this approach is computationally expensive, leading to growing interest in inference-time scaling to improve performance. Currently, inference-time scaling for text-to-image diffusion models is largely limited to best-of-N sampling, where multiple images are generated per prompt and a selection model chooses the best output. Inspired by the recent success of reasoning models like DeepSeek-R1 in the language domain, we introduce an alternative to naive best-of-N sampling by equipping text-to-image Diffusion Transformers with in-context reflection capabilities. We propose Reflect-DiT, a method that enables Diffusion Transformers to refine their generations using in-context examples of previously generated images alongside textual feedback describing necessary improvements. Instead of passively relying on random sampling and hoping for a better result in a future generation, Reflect-DiT explicitly tailors its generations to address specific aspects requiring enhancement. Experimental results demonstrate that Reflect-DiT improves performance on the GenEval benchmark (+0.19) using SANA-1.0-1.6B as a base model. Additionally, it achieves a new state-of-the-art score of 0.81 on GenEval while generating only 20 samples per prompt, surpassing the previous best score of 0.80, which was obtained using a significantly larger model (SANA-1.5-4.8B) with 2048 samples under the best-of-N approach.
HAMoBE: Hierarchical and Adaptive Mixture of Biometric Experts for Video-based Person ReID
Authors: Yiyang Su; Yunping Shi; Feng Liu; Xiaoming Liu
Venue: ICCV 2025 (main)

Abstract: Recently, research interest in person re-identification (ReID) has increasingly focused on video-based scenarios, essential for robust surveillance and security in varied and dynamic environments. However, existing video-based ReID methods often overlook the necessity of identifying and selecting the most discriminative features from both videos in a query-gallery pair for effective matching. To address this challenge, we propose a novel Hierarchical and Adaptive Mixture of Biometric Experts (HAMoBE) framework, which leverages multi-scale features from a pre-trained large model (e.g., CLIP) and is designed to mimic human perceptual mechanisms by independently modeling key biometric features--appearance, Static body shape, and dynamic gait--and adaptively integrating them. Specifically, HAMoBE includes two levels: the first level extracts low-level features from multi-scale representations provided by the frozen large model, while the second level consists of specialized experts focusing on long-term, short-term, and temporal features. To ensure robust matching, we introduce a new dual-input decision gating network that dynamically adjusts the contributions of each expert based on their relevance to the input scenarios. Extensive evaluations on benchmarks like MEVID demonstrate that our approach yields significant performance improvements (+11.0% Rank1).
TopicGeo: An Efficient Unified Framework for Geolocation
Authors: Xin Wang; Xinlin Wang; Shuiping Gou
Venue: ICCV 2025 (main)

Abstract: Vision-based geolocation techniques that establish spatial correspondences between smaller query images and larger georeferenced images have gained significant attention. Existing approaches typically employ a separate "retrieve-then-match" paradigm, whereas such paradigms suffer from computational inefficiency or precision limitations. To this end, we propose TopicGeo, a unified framework for direct and precise query-to-reference image matching via three key innovations. The textual object semantics, called topics, distilled from CLIP prompt learning are embedded into the geolocation framework to eliminate intra-class and inter-class distribution discrepancies while also enhancing processing efficiency. Center-based adaptive label assignment and outlier rejection mechanisms as a joint retrieval-matching optimization strategy ensure task-coherent feature learning and precise spatial correspondences. A multi-level fine matching pipeline is introduced to refine matching from quality and quantity. Evaluations on large-scale synthetic and real-world datasets illustrate that TopicGeo achieves state-of-the-art performance in retrieval recall and matching accuracy while maintaining a balance in computational efficiency.
Neighboring Autoregressive Modeling for Efficient Visual Generation
Authors: Yefei He; Yuanyu He; Shaoxuan He; Feng Chen; Hong Zhou; Kaipeng Zhang; Bohan Zhuang
Venue: ICCV 2025 (main)

Abstract: Visual autoregressive models typically adhere to a raster-order "next-token prediction" paradigm, which overlooks the spatial and temporal locality inherent in visual content. Specifically, visual tokens exhibit significantly stronger correlations with their spatially or temporally adjacent tokens compared to those that are distant. In this paper, we propose Neighboring Autoregressive Modeling (NAR), a novel paradigm that formulates autoregressive visual generation as a progressive outpainting procedure, following a near-to-far "next-neighbor prediction" mechanism. Starting from an initial token, the remaining tokens are decoded in ascending order of their Manhattan distance from the initial token in the spatial-temporal space, progressively expanding the boundary of the decoded region. To enable parallel prediction of multiple adjacent tokens in the spatial-temporal space, we introduce a set of dimension-oriented decoding heads, each predicting the next token along a mutually orthogonal dimension. During inference, all tokens adjacent to the decoded tokens are processed in parallel, substantially reducing the model forward steps for generation. Experiments on ImageNet 256x256 and UCF101 demonstrate that NAR achieves 2.4x and 8.6x higher throughput respectively, while obtaining superior FID/FVD scores for both image and video generation tasks compared to the PAR-4X approach. When evaluating on text-to-image generation benchmark GenEval, NAR with 0.8B parameters outperforms Chameleon-7B while using merely 0.4% of the training data.
Beyond Simple Edits: Composed Video Retrieval with Dense Modifications
Authors: Omkar Thawakar; Dmitry Demidov; Ritesh Thawkar; Rao Muhammad Anwer; Mubarak Shah; Fahad Shahbaz Khan; Salman Khan
Venue: ICCV 2025 (main)

Abstract: Composed video retrieval is a challenging task that strives to retrieve a target video based on a query video and a textual description detailing specific modifications. Standard retrieval frameworks typically struggle to handle the complexity of fine-grained compositional queries and variations in temporal understanding limiting their retrieval ability in the fine-grained setting. To address this issue, we introduce a novel dataset that captures both fine-grained and composed actions across diverse video segments, enabling more detailed compositional changes in retrieved video content.The proposed dataset, named Dense-WebVid-CoVR, consists of 1.6 million samples with dense modification text that is around seven times more than its existing counterpart. We further develop a new model that integrates visual and textual information through Cross-Attention (CA) fusion using grounded text encoder, enabling precise alignment between dense query modifications and target videos. The proposed model achieves state-of-the-art results surpassing existing methods on all metrics. Notably, it achieves 71.3% Recall@1 in visual+text setting and outperforms the state-of-the-art by 3.4%, highlighting its efficacy in terms of leveraging detailed video descriptions and dense modification texts. Our proposed dataset, code, and model will be publicly released.
MagicColor: Multi-Instance Sketch Colorization
Authors: Yinhan Zhang; Yue Ma; Bingyuan Wang; Qifeng Chen; Zeyu Wang
Venue: ICCV 2025 (main)

Abstract: We present MagicColor, a diffusion-based framework for multi-instance sketch colorization. The production of multi-instance 2D line art colorization adheres to an industry-standard workflow, which consists of three crucial stages: the design of line art characters, the coloring of individual objects, and the refinement process. The artists are required to repeat the process of coloring each instance one by one, which is inaccurate and inefficient. Meanwhile, current generative methods fail to solve this task due to the challenge of multi-instance pair data collection. To tackle these challenges, we incorporate three technical designs to ensure precise character detail transcription and achieve multi-instance sketch colorization in a single forward pass. Specifically, we first propose the self-play training strategy to address the lack of training data. Then we introduce an instance guider to feed the color of the instance. To achieve accurate color matching, we present fine-grained color matching with edge loss to enhance visual quality. Equipped with the proposed modules, MagicColor enables automatically transforming sketches into vividly-colored images with accurate consistency and multi-instance control. Experiments on our collected datasets show that our model outperforms existing methods regarding chromatic precision. Specifically, our model critically automates the colorization process with zero manual adjustments, so novice users can produce stylistically consistent artwork by providing reference instances and the original line art.
Active Learning Meets Foundation Models: Fast Remote Sensing Data Annotation for Object Detection
Authors: Marvin Burges; Philipe Ambrozio Dias; Carson Woody; Sarah Walters; Dalton Lunga
Venue: ICCV 2025 (main)

Abstract: Object detection in remote sensing demands extensive, high-quality annotations--a process that is both labor-intensive and time-consuming. In this work, we introduce a real-time active learning and semi-automated labeling framework that leverages foundation models to streamline dataset annotation for object detection in remote sensing imagery. For example, by integrating a Segment Anything Model (SAM), our approach generates mask-based bounding boxes that serve as the basis for dual sampling: (a) uncertainty estimation to pinpoint challenging samples, and (b) diversity assessment to ensure broad data coverage. Furthermore, our Dynamic Box Switching Module (DBS) addresses the well-known cold start problem for object detection models by replacing its suboptimal initial predictions with SAM-derived masks, thereby enhancing early-stage localization accuracy. Extensive evaluations on multiple remote sensing datasets plus a real-world user study, demonstrate that our framework not only reduces annotation effort, but also significantly boosts detection performance compared to traditional active learning sampling methods. The code for training and the user interface will be made available.
Im2Haircut: Single-view Strand-based Hair Reconstruction for Human Avatars
Authors: Vanessa Sklyarova; Egor Zakharov; Malte Prinzler; Giorgio Becherini; Michael J. Black; Justus Thies
Venue: ICCV 2025 (main)

Abstract: We present a novel approach for 3D hair reconstruction from single photographs based on a global hair prior combined with local optimization. Capturing strand-based hair geometry from single photographs is challenging due to the variety and geometric complexity of hairstyles and the lack of ground truth training data. Classical reconstruction methods like multi-view stereo only reconstruct the visible hair strands, missing the inner structure of hairstyles and hampering realistic hair simulation. To address this, existing methods leverage hairstyle priors trained on synthetic data. Such data, however, is limited in both quantity and quality since it requires manual work from skilled artists to model the 3D hairstyles and create near-photorealistic renderings. To address this, we propose a novel approach that uses both, real and synthetic data to learn an effective hairstyle prior. Specifically, we train a transformer-based prior model on synthetic data to obtain knowledge of the internal hairstyle geometry and introduce real data in the learning process to model the outer structure. This training scheme is able to model the visible hair strands depicted in an input image, while preserving the general 3D structure of hairstyles. We exploit this prior to create a Gaussian-splatting-based reconstruction method that creates hairstyles from one or more images. Qualitative and quantitative comparisons with existing reconstruction pipelines demonstrate the effectiveness and superior performance of our method for capturing detailed hair orientation, overall silhouette, and backside consistency. For additional results and code, please refer to https://im2haircut.is.tue.mpg.de.
Leveraging Prior Knowledge of Diffusion Model for Person Search
Authors: Giyeol Kim; Sooyoung Yang; Jihyong Oh; Myungjoo Kang; Chanho Eom
Venue: ICCV 2025 (main)

Abstract: Person search aims to jointly perform person detection and re-identification by localizing and identifying a query person within a gallery of uncropped scene images. Existing methods predominantly utilize ImageNet pre-trained backbones, which may be suboptimal for capturing the complex spatial context and fine-grained identity cues necessary for person search. Moreover, they rely on a shared backbone feature for both person detection and re-identification, leading to suboptimal features due to conflicting optimization objectives. In this paper, we propose DiffPS (Diffusion Prior Knowledge for Person Search), a novel framework that leverages a pre-trained diffusion model while eliminating the optimization conflict between two sub-tasks. We analyze key properties of diffusion priors and propose three specialized modules: (i) Diffusion-Guided Region Proposal Network (DGRPN) for enhanced person localization, (ii) Multi-Scale Frequency Refinement Network (MSFRN) to mitigate shape bias, and (iii) Semantic-Adaptive Feature Aggregation Network (SFAN) to leverage text-aligned diffusion features. DiffPS sets a new state-of-the-art on CUHK-SYSU and PRW.
PS3: A Multimodal Transformer Integrating Pathology Reports with Histology Images and Biological Pathways for Cancer Survival Prediction
Authors: Manahil Raza; Ayesha Azam; Talha Qaiser; Nasir Rajpoot
Venue: ICCV 2025 (main)

Abstract: Current multimodal fusion approaches in computational oncology primarily focus on integrating multi-gigapixel histology whole slide images (WSIs) with genomic or transcriptomic data, demonstrating improved survival prediction. We hypothesize that incorporating pathology reports can further enhance prognostic performance. Pathology reports, as essential components of clinical workflows, offer readily available complementary information by summarizing histopathological findings and integrating expert interpretations and clinical context. However, fusing these modalities poses challenges due to their heterogeneous nature. WSIs are high-dimensional, each containing several billion pixels, whereas pathology reports consist of concise text summaries of varying lengths, leading to potential modality imbalance. To address this, we propose a prototype-based approach to generate balanced representations, which are then integrated using a Transformer-based fusion model for survival prediction that we term PS3 (Predicting Survival from Three Modalities). Specifically, we present: (1) Diagnostic prototypes from pathology reports, leveraging self-attention to extract diagnostically relevant sections and standardize text representation; (2) Histological prototypes to compactly represent key morphological patterns in WSIs; and (3) Biological pathway prototypes to encode transcriptomic expressions, accurately capturing cellular functions. PS3, the three-modal transformer model, processes the resulting prototype-based multimodal tokens and models intra-modal and cross-modal interactions across pathology reports, WSIs and transcriptomic data. The proposed model outperforms state-of-the-art methods when evaluated against clinical, unimodal and multimodal baselines on six datasets from The Cancer Genome Atlas (TCGA). The code will be released upon publication.
Debiasing Trace Guidance: Top-down Trace Distillation and Bottom-up Velocity Alignment for Unsupervised Anomaly Detection
Authors: Xingjian Wang; Li Chai; Jiming Chen
Venue: ICCV 2025 (main)

Abstract: The leak of anomalous information from input condition poses a great challenge to reconstruction-based anomaly detection. Recent diffusion-based methods respond to this issue by suppressing anomaly information for condition injection or in-sampling inversion. However, since they treat conditions as a time-invariant prior, they fall into a trade-off problem between anomaly suppression and normal pattern consistency. To address this probelm, we propose Debiasing Trace Guidance (DTG) framework based on Flow Matching towards debiasing generation for more accurate unsupervised multi-class anomaly detection. Generally, DTG distills a low-dimensional generation sub-trace robust to anomalies by Top-down Trace Distillation, and then utilizes its time-varying velocity features to guide a debiasing generation by Bottom-up Velocity Alignment. The trace distillation filters out high-frequency anomalies via learnable wavelet filters and reserving structural information by keeping global consistency across samples using Skinhorn Distance. Subsequently, the velocity field of original trace is aligned with the one of sub-trace through KV-Injection Attention mechanism. The model is forced to generate normal details from corresponding low-dimensional contexts via Alignment Mask. Experimental results on several benchmarks have demonstrated the effectiveness of the proposed method.
EDM: Efficient Deep Feature Matching
Authors: Xi Li; Tong Rao; Cihui Pan
Venue: ICCV 2025 (main)

Abstract: Recent feature matching methods have achieved remarkable performance but lack efficiency consideration. In this paper, we revisit the mainstream detector-free matching pipeline and improve all its stages considering both accuracy and efficiency. We propose an Efficient Deep feature Matching network, EDM. We first adopt a deeper CNN with fewer dimensions to extract multi-level features. Then we present a Correlation Injection Module that conducts feature transformation on high-level deep features, and progressively injects feature correlations from global to local for efficient multi-scale feature aggregation, improving both speed and performance. In the refinement stage, a novel lightweight bidirectional axis-based regression head is designed to directly predict subpixel-level correspondences from latent features, avoiding the significant computational cost of explicitly locating keypoints on high-resolution local feature heatmaps. Moreover, effective selection strategies are introduced to enhance matching accuracy. Extensive experiments show that our EDM achieves competitive matching accuracy on various benchmarks and exhibits excellent efficiency, offering valuable best practices for real-world applications. The code is available at https://github.com/chicleee/EDM.