GenAI4ELab/papercli-papers huggingface dataset:
FonTS: Text Rendering With Typography and Style Controls
Authors: Wenda Shi; Yiren Song; Dengming Zhang; Jiaming Liu; Xingxing Zou
Venue: ICCV 2025 (main)

Abstract: Visual text rendering are widespread in various real-world applications, requiring careful font selection and typographic choices. Recent progress in diffusion transformer (DiT)-based text-to-image (T2I) models show promise in automating these processes. However, these methods still encounter challenges like inconsistent fonts, style variation, and limited fine-grained control, particularly at the word-level. This paper proposes a two-stage DiT-based pipeline to address these problems by enhancing controllability over typography and style in text rendering. We introduce typography control fine-tuning (TC-FT), an parameter-efficient fine-tuning method (on 5% key parameters) with enclosing typography control tokens (ETC-tokens), which enables precise word-level application of typographic features. To further address style inconsistency in text rendering, we propose a text-agnostic style control adapter (SCA) that prevents content leakage while enhancing style consistency. To implement TC-FT and SCA effectively, we incorporated HTML-render into the data synthesis pipeline and proposed the first word-level controllable dataset. Through comprehensive experiments, we demonstrate the effectiveness of our approach in achieving superior word-level typographic control, font consistency, and style consistency in text rendering tasks. Our project page is available at this site.
Improving SAM for Camouflaged Object Detection via Dual Stream Adapters
Authors: Jiaming Liu; Linghe Kong; Guihai Chen
Venue: ICCV 2025 (main)

Abstract: Segment anything model (SAM) has shown impressive general-purpose segmentation performance on natural images, but its performance on camouflaged object detection (COD) is unsatisfactory. In this paper, we propose SAM-DSA that performs COD for RGB-D inputs via Dual Stream Adapters. While keeping the SAM architecture intact, dual stream adapters are expanded on the image encoder to learn potential complementary information from RGB images and depth images, and fine-tune the mask decoder and its depth-aware replica to perform dual-stream mask prediction. In practice, the dual stream adapters are embedded into the attention block of the image encoder in a parallel manner to facilitate the refinement and correction of the two types of image embeddings. To mitigate channel discrepancies arising from dual stream embeddings that do not directly interact with each other, we augment the association of dual stream embeddings using bidirectional knowledge distillation including a model distiller and a modal distiller. In addition, to predict the masks for RGB and depth attention maps, we integrate the two types of image embeddings which are jointly learned with the prompt embeddings to update the initial prompt, and then feed them into the mask decoders to synchronize the consistency of image embeddings and prompt embeddings. Experimental results on four COD benchmarks show that our SAM-DSA achieves excellent detection performance gains over SAM and achieves state-of-the-art results with a given fine-tuning paradigm.
DiSCO-3D : Discovering and Segmenting Sub-Concepts from Open-vocabulary Queries in NeRF
Authors: Doriand Petit; Steve Bourgeois; Vincent Gay-Bellile; Florian Chabot; Loïc Barthe
Venue: ICCV 2025 (main)

Abstract: 3D semantic segmentation provides high-level scene understanding for applications in robotics, autonomous systems, etc. Traditional methods adapt exclusively to either task-specific goals (open-vocabulary segmentation) or scene content (unsupervised semantic segmentation). We propose DiSCO-3D, the first method addressing the broader problem of 3D Open-Vocabulary Sub-concepts Discovery, which aims to provide a 3D semantic segmentation that adapts to both the scene and user queries. We build DiSCO-3D on Neural Fields representations, combining unsupervised segmentation with weak open-vocabulary guidance. Our evaluations demonstrate that DiSCO-3D achieves effective performance in Open-Vocabulary Sub-concepts Discovery and exhibits state-of-the-art results in the edge cases of both open-vocabulary and unsupervised segmentation.
PRIMAL: Physically Reactive and Interactive Motor Model for Avatar Learning
Authors: Yan Zhang; Yao Feng; Alpár Cseke; Nitin Saini; Nathan Bajandas; Nicolas Heron; Michael J. Black
Venue: ICCV 2025 (main)

Abstract: We formulate the motor system of an interactive avatar as a generative motion model that can drive the body to move through 3D space in a perpetual, realistic, controllable, and responsive manner. Although human motion generation has been extensively studied, many existing methods lack the responsiveness and realism of real human movements. Inspired by recent advances in foundation models, we propose PRIMAL, which is learned with a two-stage paradigm. In the pretraining stage, the model learns body movements from a large number of sub-second motion segments, providing a generative foundation from which more complex motions are built. This training is fully unsupervised without annotations. Given a single-frame initial state during inference, the pretrained model not only generates unbounded, realistic, and controllable motion, but also enables the avatar to be responsive to induced impulses in real time. In the adaptation phase, we employ a novel ControlNet-like adaptor to fine-tune the base model efficiently, adapting it to new tasks such as few-shot personalized action generation and spatial target reaching. Evaluations show that our proposed method outperforms state-of-the-art baselines. We leverage the model to create a real-time character animation system in Unreal Engine that feels highly responsive and natural.
VCA: Video Curious Agent for Long Video Understanding
Authors: Zeyuan Yang; Delin Chen; Xueyang Yu; Maohao Shen; Chuang Gan
Venue: ICCV 2025 (main)

Abstract: Long video understanding poses unique challenges due to its temporal complexity and low information density. Recent works address this task by sampling numerous frames or incorporating auxiliary tools using LLMs, both of which result in high computational costs. In this work, we introduce a curiosity-driven video agent with self-exploration capability, dubbed as "VCA". Built upon VLMs, VCA autonomously navigates video segments and efficiently builds a comprehensive understanding of complex video sequences.Instead of directly sampling frames, VCA employs a tree-search structure to explore video segments and collect frames. Rather than relying on external feedback or reward, VCA leverages VLM's self-generated intrinsic reward to guide its exploration, enabling it to capture the most crucial information for reasoning. Experimental results on multiple long video benchmarks demonstrate our approach's superior effectiveness and efficiency.
Self-Reinforcing Prototype Evolution with Dual-Knowledge Cooperation for Semi-Supervised Lifelong Person Re-Identification
Authors: Kunlun Xu; Fan Zhuo; Jiangmeng Li; Xu Zou; Jiahuan Zhou
Venue: ICCV 2025 (main)

Abstract: Current lifelong person re-identification (LReID) methods predominantly rely on fully labeled data streams. However, in real-world scenarios where annotation resources are limited, a vast amount of unlabeled data coexists with scarce labeled samples, leading to the Semi-Supervised LReID (Semi-LReID) problem where LReID methods suffer severe performance degradation. Existing LReID methods, even when combined with semi-supervised strategies, suffer from limited long-term adaptation performance due to struggling with the noisy knowledge occurring during unlabeled data utilization. In this paper, we pioneer the investigation of Semi-LReID, introducing a novel Self-Reinforcing PRototype Evolution with Dual-Knowledge Cooperation framework (SPRED). Our key innovation lies in establishing a self-reinforcing cycle between dynamic prototype-guided pseudo-label generation and new-old knowledge collaborative purification to enhance the utilization of unlabeled data. Specifically, learnable identity prototypes are introduced to dynamically capture the identity distributions and generate high-quality pseudo-labels. Then, the dual-knowledge cooperation scheme integrates current model specialization and historical model generalization, refining noisy pseudo-labels. Through this cyclic design, reliable pseudo-labels are progressively mined to improve current-stage learning and ensure positive knowledge propagation over long-term learning. Experiments on the established Semi-LReID benchmarks show that our SPRED achieves state-of-the-art performance. Our source code is available at https://github.com/zhoujiahuan1991/ICCV2025-SPRED
What to Distill? Fast Knowledge Distillation with Adaptive Sampling
Authors: Byungchul Chae; Seonyeong Heo
Venue: ICCV 2025 (main)

Abstract: Knowledge Distillation (KD) has been established as an effective technique for reducing the resource requirements of models when tackling computer vision tasks. Prior work has studied how to distill the knowledge of a teacher model better, but it overlooks how data affects the distillation result. This work examines the impact of data in knowledge distillation from two perspectives: (i) quantity of knowledge and (ii) quality of knowledge. Our examination finds that faster knowledge distillation can be achieved by using data with a large amount of high-quality knowledge in distillation. Based on the findings, this work proposes an efficient adaptive sampling method called KDAS for faster knowledge distillation, which enhances the distillation efficiency by selecting and applying 'good' samples for the distillation. This work shows that our adaptive sampling methods can effectively accelerate the training efficiency of a student model when combined with existing KD methods.
Revisiting Pool-based Prompt Learning for Few-shot Class-incremental Learning
Authors: Yongwei Jiang; Yixiong Zou; Yuhua Li; Ruixuan Li
Venue: ICCV 2025 (main)

Abstract: Few-Shot Class-Incremental Learning (FSCIL) faces dual challenges of data scarcity and incremental learning in real-world scenarios. While pool-based prompting methods have demonstrated success in traditional incremental learning, their effectiveness in FSCIL settings remains unexplored. This paper presents the first study of current prompt pool methods in FSCIL tasks, revealing an unanticipated performance degradation in incremental sessions. Through comprehensive analysis, we identify that this phenomenon stems from token-dimension saturation: with limited data, excessive prompts compete for task-relevant information, leading to model overfitting. Based on this finding, we propose LGSP-Prompt (Local-Global Spatial Prompting), which innovatively shifts pool-based prompt learning from the token dimension to the spatial dimension. LGSP-Prompt generates spatial prompts by synergistically combining local spatial features and global frequency-domain representations to highlight key patterns in input images. We construct two spatial prompt pools enabling dynamic prompt selection to maintain acquired knowledge while effectively learning novel sessions. Extensive experiments demonstrate that our approach achieves state-of-the-art performance across multiple FSCIL benchmarks, showing significant advantages in both base knowledge preservation and incremental learning. Our implementation is available at https://github.com/Jywsuperman/LGSP.
HOMO-Feature: Cross-Arbitrary-Modal Image Matching with Homomorphism of Organized Major Orientation
Authors: Chenzhong Gao; Wei Li; Desheng Weng
Venue: ICCV 2025 (main)

Abstract: An exploration of cross-arbitrary-modal image invariant feature extraction and matching is made, with a purely handcrafted full-chain algorithm, Homomorphism of Organized Major Orientation (HOMO), being proposed. Instead of using deep models to conduct data-driven black-box learning, we introduce a Major Orientation Map (MOM), effectively combating image modal differences. Considering rotation, scale, and texture diversities in cross-modal images, HOMO incorporates a novel, universally designed Generalized-Polar descriptor (GPolar) and a Multi-scale Strategy (MsS) to gain well-rounded capacities. HOMO achieves the best comprehensive performance in feature matching on several generally cross-modal datasets, challenging compared with a set of state-of-the-art methods including 7 traditional algorithms and 10 deep network models. A dataset named General Cross-modal Zone (GCZ) is proposed, which shows practical values. Codes with datasets are available at https://github.com/MrPingQi/HOMO_Feature_ImgMatching.
ForeSight: Multi-View Streaming Joint Object Detection and Trajectory Forecasting
Authors: Sandro Papais; Letian Wang; Brian Cheong; Steven L. Waslander
Venue: ICCV 2025 (main)

Abstract: We introduce ForeSight, a novel joint detection and forecasting framework for vision-based 3D perception in autonomous vehicles. Traditional approaches treat detection and forecasting as separate sequential tasks, limiting their ability to leverage temporal cues. ForeSight addresses this limitation with a multi-task streaming and bidirectional learning approach, allowing detection and forecasting to share query memory and propagate information seamlessly. The forecast-aware detection transformer enhances spatial reasoning by integrating trajectory predictions from a multiple hypothesis forecast memory queue, while the streaming forecast transformer improves temporal consistency using past forecasts and refined detections. Unlike tracking-based methods, ForeSight eliminates the need for explicit object association, reducing error propagation with a tracking-free model that efficiently scales across multi-frame sequences. Experiments on the nuScenes dataset show that ForeSight achieves state-of-the-art performance, achieving an EPA of 54.9%, surpassing previous methods by 9.3%, while also attaining the best mAP and minADE among multi-view detection and forecasting models.
SG-LDM: Semantic-Guided LiDAR Generation via Latent-Aligned Diffusion
Authors: Zhengkang Xiang; Zizhao Li; Amir Khodabandeh; Kourosh Khoshelham
Venue: ICCV 2025 (main)

Abstract: Lidar point cloud synthesis based on generative models offers a promising solution to augment deep learning pipelines, particularly when real-world data is scarce or lacks diversity. By enabling flexible object manipulation, this synthesis approach can significantly enrich training datasets and enhance discriminative models. However, existing methods focus on unconditional lidar point cloud generation, overlooking their potential for real-world applications. In this paper, we propose SG-LDM, a Semantic-Guided Lidar Diffusion Model that employs latent alignment to enable robust semantic-to-lidar synthesis. By directly operating in the native lidar space and leveraging explicit semantic conditioning, SG-LDM achieves state-of-the-art performance in generating high-fidelity lidar point clouds guided by semantic labels. Moreover, we propose the first diffusion-based lidar translation framework based on SG-LDM, which enables cross-domain translation as a domain adaptation strategy to enhance downstream perception performance. Systematic experiments demonstrate that SG-LDM significantly outperforms existing lidar diffusion models and the proposed lidar translation framework further improves data augmentation performance in the downstream lidar segmentation task.
Dynamic Multi-Layer Null Space Projection for Vision-Language Continual Learning
Authors: Borui Kang; Lei Wang; Zhiping Wu; Tao Feng; Yawen Li; Yang Gao; Wenbin Li
Venue: ICCV 2025 (main)

Abstract: Vision-Language Models (VLM) have emerged as a highly promising approach for Continual Learning (CL) due to their powerful generalized features. While adapter-based VLM can exploit both task-specific and task-agnostic features, current CL methods have largely overlooked the distinct and evolving parameter distributions in visual and language modalities, which are found crucial for effectively mitigating catastrophic forgetting.In this study, we find that the visual modality experiences a broader parameter distribution and greater variance during class increments than the textual modality, leading to higher vulnerability to forgetting.Consequently, we handle the branches of the two modalities asymmetrically. Specifically, we propose a Dynamic Multi-layer Null Space Projection (DMNSP) strategy and apply it only to the visual modality branch, while optimizing the language branch according to the original optimizer. DMNSP can restrict the update of visual parameters within the common subspace of multiple null spaces, further limiting the impact of non-zero residual terms. Simultaneously, combined with a dynamic projection coefficient, we can precisely control the magnitude of gradient projection to the null space, endowing the model with good stability and plasticity.Extensive experiments on TinyImageNet, CIFAR100 and ImageNet-R demonstrate that our method outperforms current approaches in accuracy and knowledge retention, setting a new standard for state-of-the-art performance in class incremental learning.
AFUNet: Cross-Iterative Alignment-Fusion Synergy for HDR Reconstruction via Deep Unfolding Paradigm
Authors: Xinyue Li; Zhangkai Ni; Wenhan Yang
Venue: ICCV 2025 (main)

Abstract: Existing learning-based methods effectively reconstruct HDR images from multi-exposure LDR inputs with extended dynamic range and improved detail, but often rely on empirical design rather than a theoretical foundation, which can impact their reliability. To address these limitations, we propose the cross-iterative Alignment and Fusion deep Unfolding Network (AFUNet), where HDR reconstruction is systematically decoupled into two interleaved subtasks--alignment and fusion--optimized through alternating refinement, achieving synergy between the two subtasks to enhance the overall performance. Our method formulates multi-exposure HDR reconstruction from a Maximum A Posteriori (MAP) estimation perspective, explicitly incorporating spatial correspondence priors across LDR images and naturally bridging the alignment and fusion subproblems through joint constraints. Building on the mathematical foundation, we reimagine traditional iterative optimization through unfolding--transforming the conventional solution process into an end-to-end trainable AFUNet with carefully designed modules that work progressively. Specifically, each iteration of AFUNet incorporates an Alignment-Fusion Module (AFM) that alternates between a Spatial Alignment Module (SAM) for alignment and a Channel Fusion Module (CFM) for adaptive feature fusion, progressively bridging misaligned content and exposure discrepancies. Extensive qualitative and quantitative evaluations demonstrate AFUNet's superior performance, consistently surpassing state-of-the-art methods. Our code is available at: https://github.com/eezkni/AFUNet.
DiT4SR: Taming Diffusion Transformer for Real-World Image Super-Resolution
Authors: Zheng-Peng Duan; Jiawei Zhang; Xin Jin; Ziheng Zhang; Zheng Xiong; Dongqing Zou; Jimmy S. Ren; Chunle Guo; Chongyi Li
Venue: ICCV 2025 (main)

Abstract: Large-scale pre-trained diffusion models are becoming increasingly popular in solving the Real-World Image Super-Resolution (Real-ISR) problem because of their rich generative priors. The recent development of diffusion transformer (DiT) has witnessed overwhelming performance over the traditional UNet-based architecture in image generation, which also raises the question: Can we adopt the advanced DiT-based diffusion model for Real-ISR?To this end, we propose our DiT4SR, one of the pioneering works to tame the large-scale DiT model for Real-ISR.Instead of directly injecting embeddings extracted from low-resolution (LR) images like ControlNet, we integrate the LR embeddings into the original attention mechanism of DiT, allowing for the bidirectional flow of information between the LR latent and the generated latent. The sufficient interaction of these two streams allows the LR stream to evolve with the diffusion process, producing progressively refined guidance that better aligns with the generated latent at each diffusion step. Additionally, the LR guidance is injected into the generated latent via a cross-stream convolution layer, compensating for DiT's limited ability to capture local information. These simple but effective designs endow the DiT model with superior performance in Real-ISR, which is demonstrated by extensive experiments.
Balanced Sharpness-Aware Minimization for Imbalanced Regression
Authors: Yahao Liu; Qin Wang; Lixin Duan; Wen Li
Venue: ICCV 2025 (main)

Abstract: Regression is fundamental in computer vision and is widely used in various tasks including age estimation, depth estimation, target localization, etc. However, real-world data often exhibits imbalanced distribution, making regression models perform poorly especially for target values with rare observations (known as the imbalanced regression problem). In this paper, we reframe imbalanced regression as an imbalanced generalization problem. To tackle that, we look into the loss sharpness property for measuring the generalization ability of regression models in the observation space. Namely, given a certain perturbation on the model parameters, we check how model performance changes according to the loss values of different target observations. We propose a simple yet effective approach called Balanced Sharpness-Aware Minimization (BSAM) to enforce the uniform generalization ability of regression models for the entire observation space. In particular, we start from the traditional sharpness-aware minimization and then introduce a novel targeted reweighting strategy to homogenize the generalization ability across the observation space, which guarantees a theoretical generalization bound. Extensive experiments on multiple vision regression tasks, including age and depth estimation, demonstrate that our BSAM method consistently outperforms existing approaches. The code is available here.
Dynamic-DINO: Fine-Grained Mixture of Experts Tuning for Real-time Open-Vocabulary Object Detection
Authors: Yehao Lu; Minghe Weng; Zekang Xiao; Rui Jiang; Wei Su; Guangcong Zheng; Ping Lu; Xi Li
Venue: ICCV 2025 (main)

Abstract: The Mixture of Experts (MoE) architecture has excelled in Large Vision-Language Models (LVLMs), yet its potential in real-time open-vocabulary object detectors, which also leverage large-scale vision-language datasets but smaller models, remains unexplored. This work investigates this domain, revealing intriguing insights. In the shallow layers, experts tend to cooperate with diverse peers to expand the search space. While in the deeper layers, fixed collaborative structures emerge, where each expert maintains 2-3 fixed partners and distinct expert combinations are specialized in processing specific patterns. Concretely, we propose Dynamic-DINO, which extends Grounding DINO 1.5 Edge from a dense model to a dynamic inference framework via an efficient MoE-Tuning strategy. Additionally, we design a granularity decomposition mechanism to decompose the Feed-Forward Network (FFN) of base model into multiple smaller expert networks, expanding the subnet search space. To prevent performance degradation at the start of fine-tuning, we further propose a pre-trained weight allocation strategy for the experts, coupled with a specific router initialization. During inference, only the input-relevant experts are activated to form a compact subnet. Experiments show that, pretrained with merely 1.56M open-source data, Dynamic-DINO outperforms Grounding DINO 1.5 Edge, pretrained on the private Grounding20M dataset.