GenAI4ELab/papercli-papers huggingface dataset:
World4Drive: End-to-End Autonomous Driving via Intention-aware Physical Latent World Model
Authors: Yupeng Zheng; Pengxuan Yang; Zebin Xing; Qichao Zhang; Yuhang Zheng; Yinfeng Gao; Pengfei Li; Teng Zhang; Zhongpu Xia; Peng Jia; XianPeng Lang; Dongbin Zhao
Venue: ICCV 2025 (main)

Abstract: End-to-end autonomous driving directly generates planning trajectories from raw sensor data, yet it typically relies on costly perception supervision to extract scene information. A critical research challenge arises: constructing an informative driving world model to enable perception annotation-free, end-to-end planning via self-supervised learning. In this paper, we present World4Drive, an end-to-end autonomous driving framework that employs vision foundation models to build latent world models for generating and evaluating multi-modal planning trajectories. Specifically, World4Drive first extracts scene features, including driving intention and world latent representations enriched with spatial-semantic priors provided by vision foundation models. It then generates multi-modal planning trajectories based on current scene features and driving intentions and predicts multiple intention-driven future states within the latent space. Finally, it introduces a world model selector module to evaluate and select the best trajectory. We achieve perception annotation-free, end-to-end planning through self-supervised alignment between actual future observations and predicted observations reconstructed from the latent space. World4Drive achieves state-of-the-art performance without manual perception annotations on both the open-loop nuScenes and closed-loop NavSim benchmarks, demonstrating an 18.0% relative reduction in L2 error, 46.7% lower collision rate, and 3.75 faster training convergence. Codes will be accessed at https://github.com/ucaszyp/World4Drive.
Online Language Splatting
Authors: Saimouli Katragadda; Cho-Ying Wu; Yuliang Guo; Xinyu Huang; Guoquan Huang; Liu Ren
Venue: ICCV 2025 (main)

Abstract: To enable AI agents to interact seamlessly with both humans and 3D environments, they must not only perceive the 3D world accurately but also align human language with 3D spatial representations. While prior work has made significant progress by integrating language features into geometrically detailed 3D scene representations using 3D Gaussian Splatting (GS), these approaches rely on computationally intensive offline preprocessing of language features for each input image, limiting adaptability to new environments. In this work, we introduce Online Language Splatting, the first framework to achieve online, near real-time, open-vocabulary language mapping within a 3DGS-SLAM system without requiring pre-generated language features. The key challenge lies in efficiently fusing high-dimensional language features into 3D representations while balancing the computation speed, memory usage, rendering quality and open-vocabulary capability. To this end, we innovatively design: (1) a high-resolution CLIP embedding module capable of generating detailed language feature maps in 18ms per frame, (2) a two-stage online auto-encoder that compresses 768-dimensional CLIP features to 15 dimensions while preserving open-vocabulary capabilities, and (3) a color-language disentangled optimization approach to improve rendering quality. Experimental results show that our online method not only surpasses the state-of-the-art offline methods in accuracy but also achieves more than 40x efficiency boost, demonstrating the potential for dynamic and interactive AI applications.
Amodal Depth Anything: Amodal Depth Estimation in the Wild
Authors: Zhenyu Li; Mykola Lavreniuk; Jian Shi; Shariq Farooq Bhat; Peter Wonka
Venue: ICCV 2025 (main)

Abstract: Amodal depth estimation aims to predict the depth of occluded (invisible) parts of objects in a scene. This task addresses the question of whether models can effectively perceive the geometry of occluded regions based on visible cues. Prior methods primarily rely on synthetic datasets and focus on metric depth estimation, limiting their generalization to real-world settings due to domain shifts and scalability challenges. In this paper, we propose a novel formulation of amodal depth estimation in the wild, focusing on relative depth prediction to improve model generalization across diverse natural images. We introduce a new large-scale dataset, Amodal Depth In the Wild (ADIW), created using a scalable pipeline that leverages segmentation datasets and compositing techniques. Depth maps are generated using large pre-trained depth models, and a scale-and-shift alignment strategy is employed to refine and blend depth predictions, ensuring consistency in ground-truth annotations. To tackle the amodal depth task, we present two complementary frameworks: Amodal-DAV2, a deterministic model based on Depth Anything V2, and Amodal-DepthFM, a generative model that integrates conditional flow matching principles. Our proposed frameworks effectively leverage the capabilities of large pre-trained models with minimal modifications to achieve high-quality amodal depth predictions (Fig. 1). Experiments validate our design choices, demonstrating the flexibility of our models in generating diverse, plausible depth structures for occluded regions. Our method achieves a 50.7% improvement in RMSE over the previous SoTA on the ADIW dataset.
Hierarchical Variational Test-Time Prompt Generation for Zero-Shot Generalization
Authors: Zhaoyang Wu; Fang Liu; Licheng Jiao; Shuo Li; Lingling Li; Xu Liu; Puhua Chen; Wenping Ma
Venue: ICCV 2025 (main)

Abstract: Vision-language models like CLIP have demonstrated strong zero-shot generalization, making them valuable for various downstream tasks through prompt learning. However, existing test-time prompt tuning methods, such as entropy minimization, treat both text and visual prompts as fixed learnable parameters, limiting their adaptability to unseen domains. In contrast, we propose Hierarchical Variational Test-Time Prompt Generation, a novel approach where both text and visual prompts are dynamically generated via a HyperTransformer at inference time. This enables the model to produce data-specific prompts for each modality, significantly improving generalization. To further address template sensitivity and distribution shifts, we introduce variational prompt generation, leveraging variational inference to mitigate biases introduced by different prompt templates and data augmentations. Additionally, our hierarchical variational prompt generation conditions prompts at each layer on those from previous layers, allowing the model to capture deeper contextual dependencies and refine prompt interactions for robust adaptation. Extensive experiments on domain generalization benchmarks demonstrate that our method significantly outperforms existing prompt-learning techniques, achieving state-of-the-art zero-shot accuracy while maintaining efficiency.
VTimeCoT: Thinking by Drawing for Video Temporal Grounding and Reasoning
Authors: Jinglei Zhang; Yuanfan Guo; Rolandos Alexandros Potamias; Jiankang Deng; Hang Xu; Chao Ma
Venue: ICCV 2025 (main)

Abstract: In recent years, video question answering based on multimodal large language models (MLLM) has garnered considerable attention, due to the benefits from the substantial advancements in LLMs. However, these models have a notable deficiency in the domains of video temporal grounding and reasoning, posing challenges to the development of effective real-world video understanding systems. Inspired by how humans use video players to interact with the progress bar for video comprehension, we introduce VTimeCoT, a simple yet effective training-free framework, designed for high-performance video grounding and reasoning. The proposed framework incorporates two novel visual tools of the progress bar: a plug-and-play progress bar integration tool and a high-efficiency highlighting tool. In addition, to address the limitations of conventional text-based chain-of-thought (CoT) approaches, we introduce a visuotemporal CoT process that integrates cross-modality reasoning across both video and text. Our approach demonstrates significant performance improvements on both Qwen2VL-7B and GPT4o baselines in tasks of video temporal grounding and reasoning-based question answering. Finally, we showcase that the proposed framework achieves a compositional and interpretable reasoning process. Project page: \href https://vtimecot.github.io  https://vtimecot.github.io .
RA-BUSSeg: Relation-aware Semi-supervised Breast Ultrasound Image Segmentation via Adjacent Propagation and Cross-layer Alignment
Authors: Wanting Zhang; Zhenhui Ding; Guilian Chen; Huisi Wu; Jing Qin
Venue: ICCV 2025 (main)

Abstract: Accurate breast ultrasound (BUS) image segmentation is critical for diagnosis and surgical planning, but faces challenges due to limited labeled images. Semi-supervised methods show promise by leveraging pseudo-labels to mitigate reliance on large-scale annotations. However, their performance is highly dependent on the quality of pseudo-labels, which is difficult to guarantee in BUS images due to inherent complexities such as low contrast, speckle noise, and artifacts. Previous studies primarily focus on refining pseudo-labels in one way or the other, or introducing auxiliary supervision; yet they overlook the potential of harnessing intrinsic and inherent pixel relations to enhance the robustness of semi-supervised segmentation. In this paper, we present a novel relation-aware semi-supervised model for BUS image segmentation, which is composed of two innovative components: an adjacent relation propagation (ARP) module and a cross-layer relation alignment (CRA) module, for comprehensively explore pixel relations to improve segmentation performance. The ARP propagates relations among adjacent pixels to reinforce the collaborative prediction of correlated pixels and enhance the model's awareness of local semantic consistency. The CRA aligns cross-layer pixel relations, employing deep-layer guidance to rectify erroneous correlations in shallow layers for noise suppression, while integrating multi-scale contexts to enable robust segmentation of lesions with varying sizes. Extensive experiments on two representative BUS datasets under various labeling conditions demonstrate the superiority of our method to the SOTAs. Codes are available at https://github.com/dodooo1/RA-BUSSeg.
Factorized Learning for Temporally Grounded Video-Language Models
Authors: Wenzheng Zeng; Difei Gao; Mike Zheng Shou; Hwee Tou Ng
Venue: ICCV 2025 (main)

Abstract: Recent video-language models have shown great potential for video understanding, but still struggle with accurate temporal grounding for event-level perception. We observe that two main factors in video understanding (i.e., temporal grounding and textual response) form a logical hierarchy: accurate temporal evidence grounding lays the foundation for reliable textual response. However, existing works typically handle these two tasks in a coupled manner without a clear logical structure, leading to sub-optimal objectives. We address this from a factorized learning perspective. We first propose D2VLM, a framework that decouples the learning of these two tasks while also emphasizing their inherent dependency. We adopt a "grounding then answering with evidence referencing" paradigm and introduce evidence tokens for evidence grounding, which emphasize event-level visual semantic capture beyond the focus on timestamp representation in existing works. To further facilitate the learning of these two tasks, we introduce a novel factorized preference optimization (FPO) algorithm. Unlike standard preference optimization, FPO explicitly incorporates probabilistic temporal grounding modeling into the optimization objective, enabling preference learning for both temporal grounding and textual response. We also construct a synthetic dataset to address the lack of suitable datasets for factorized preference learning with explicit temporal grounding. Experiments on various tasks demonstrate the clear advantage of our approach. Our source code is available at https://github.com/nusnlp/d2vlm.
Decouple to Reconstruct: High Quality UHD Restoration via Active Feature Disentanglement and Reversible Fusion
Authors: Yidi Liu; Dong Li; Yuxin Ma; Jie Huang; Wenlong Zhang; Xueyang Fu; Zheng-Jun Zha
Venue: ICCV 2025 (main)

Abstract: Ultra-high-definition (UHD) image restoration often faces computational bottlenecks and information loss due to its extremely high resolution. Existing studies based on Variational Autoencoders (VAE) improve efficiency by transferring the image restoration process from pixel space to latent space. However, degraded components are inherently coupled with background elements in degraded images, both information loss during compression and information gain during compensation remain uncontrollable. These lead to restored images often exhibiting image detail loss and incomplete degradation removal. To address this issue, we propose a Controlled Differential Disentangled VAE, which utilizes Hierarchical Contrastive Disentanglement Learning and an Orthogonal Gated Projection Module to guide the VAE to actively discard easily recoverable background information while encoding more difficult-to-recover degraded information into the latent space. Additionally, we design a Complex Invertible Multiscale Fusion Network to handle background features, ensuring their consistency, and utilize a latent space restoration network to transform the degraded latent features, leading to more accurate restoration results. Extensive experimental results demonstrate that our method effectively alleviates the information loss problem in VAE models while ensuring computational efficiency, significantly improving the quality of UHD image restoration, and achieves state-of-the-art results in six UHD restoration tasks with only 1M parameters.Our code will be available here.
Generating Multi-Image Synthetic Data for Text-to-Image Customization
Authors: Nupur Kumari; Xi Yin; Jun-Yan Zhu; Ishan Misra; Samaneh Azadi
Venue: ICCV 2025 (main)

Abstract: Customization of text-to-image models enables users to insert new concepts or objects and generate them in unseen settings. Existing methods either rely on comparatively expensive test-time optimization or train encoders on single-image datasets without multi-image supervision, which can limit image quality. We propose a simple approach to address these challenges. We first leverage existing text-to-image models and 3D datasets to create a high-quality Synthetic Customization Dataset (SynCD) consisting of multiple images of the same object in different lighting, backgrounds, and poses. Using this dataset, we train an encoder-based model that incorporates fine-grained visual details from reference images via a shared attention mechanism. Finally, we propose an inference technique that normalizes text and image guidance vectors to mitigate overexposure issues in sampled images. Through extensive experiments, we show that our encoder-based model, trained on SynCD, and with the proposed inference algorithm, improves upon existing encoder-based methods on standard customization benchmarks.
WeaveSeg: Iterative Contrast-weaving and Spectral Feature-refining for Nuclei Instance Segmentation
Authors: Jiajia Li; Huisi Wu; Jing Qin
Venue: ICCV 2025 (main)

Abstract: histopathology images is a fundamental task in computational pathology. It is also a very challenging task due to complex nuclei morphologies, ambiguous boundaries, and staining variations. Existing methods often struggle to precisely delineate overlapping nuclei and handle class imbalance. We introduce WeaveSeg, a novel deep learning model for nuclei instance segmentation that significantly improves segmentation performance via synergistic integration of adaptive spectral feature refinement and iterative contrast-weaving. WeaveSeg features an adaptive spectral detail refinement (SAR) module for multi-scale feature enhancement via adaptive frequency component fusion, and an iterative contrast-weaving (ICW) module that progressively refines features through integrating contrastive attention, decoupled semantic context, and adaptive gating. Furthermore, we introduce a specialized uncertainty loss to explicitly model ambiguous regions, and a novel local contrast-based self-adaptive adjustment mechanism to accommodate dynamic feature distributions. Extensive experiments on MoNuSeg and CoNSeP demonstrate WeaveSeg's SOTA performance over existing models. Code will be publicly available.
Towards Long-Horizon Vision-Language-Action System: Reasoning, Acting and Memory
Authors: Daixun Li; Yusi Zhang; Mingxiang Cao; Donglai Liu; Weiying Xie; Tianlin Hui; Lunkai Lin; Zhiqiang Xie; Yunsong Li
Venue: ICCV 2025 (main)

Abstract: Vision-Language-Action (VLA) is crucial for autonomous decision-making in embodied systems. While current methods have advanced single-skill abilities, their short-horizon capability limits applicability in real-world scenarios. To address this challenge, we innovatively propose MindExplore, a general hierarchical VLA system with cross-skill for long-horizon tasks in highly dynamic sand. The key insight is to iteratively align the knowledge domain of task planning and action execution. Thus, this task-oriented action enables outstanding generalization across a wide range of real-world scenarios. In the reasoning layer, task-specific chains of thought (CoT) are designed for planning long-horizon task sequences and providing meta-action signals. In the acting layer, a simple but powerful Mixture of Policy Experts strategy is built inspired by signals and multimodal inputs for adaptively selecting skill experts and generating closed-loop action sequences. Also, it integrates a lightweight Multimodal Diffusion Policy (MMDP) to enhance spatial perception by fusing multi-visual modality features. Besides, the pioneering memory mechanism establishes feedback between the reasoning and acting layers, facilitating adaptive execution of long-horizon tasks and real-time replanning. Notably, we create SandGo-1k and SandThink-21k, the first expert-level multimodal embodied dataset and CoT dataset tailored for sandy environments. At a high execution frequency of 30 FPS, MindExplore is 3.01 times more successful than existing methods in unstructured and dynamic environments.
MR-FIQA: Face Image Quality Assessment with Multi-Reference Representations from Synthetic Data Generation
Authors: Fu-Zhao Ou; Chongyi Li; Shiqi Wang; Sam Kwong
Venue: ICCV 2025 (main)

Abstract: Recent advancements in Face Image Quality Assessment (FIQA) models trained on real large-scale face datasets are pivotal in guaranteeing precise face recognition in unrestricted scenarios. Regrettably, privacy concerns lead to the discontinuation of real datasets, underscoring the pressing need for a tailored synthetic dataset dedicated to the FIQA task. However, creating satisfactory synthetic datasets for FIQA is challenging. It requires not only controlling the intra-class degradation of different quality factors (e.g., pose, blur, occlusion) for the pseudo-identity generation but also designing an optimized quality characterization method for quality annotations. This paper undertakes the pioneering initiative to establish a Synthetic dataset for FIQA (SynFIQA) based on a hypothesis: accurate quality labelling can be achieved through the utilization of quality priors across the diverse domains involved in quality-controllable generation. To validate this, we tailor the generation of reference and degraded samples by aligning pseudo-identity image features in stable diffusion latent space, editing 3D facial parameters, and customizing dual text prompts and post-processing. Furthermore, we propose a novel quality characterization method that thoroughly examines the relationship of Multiple Reference representations among recognition embedding, spatial, and visual-language domains to acquire annotations essential for fitting FIQA models (MR-FIQA). Extensive experiments confirm the validity of our hypothesis and demonstrate the advantages of our SynFIQA data and MR-FIQA method.
IDFace: Face Template Protection for Efficient and Secure Identification
Authors: Sunpill Kim; Seunghun Paik; Chanwoo Hwang; Dongsoo Kim; Junbum Shin; Jae Hong Seo
Venue: ICCV 2025 (main)

Abstract: As face recognition systems (FRS) become more widely used, user privacy becomes more important. A key privacy issue in FRS is protecting the user's face template, as the characteristics of the user's face image can be recovered from the template. Although recent advances in cryptographic tools such as homomorphic encryption (HE) have provided opportunities for securing the FRS, HE cannot be used directly with FRS in an efficient plug-and-play manner. In particular, although HE is functionally complete for arbitrary programs, it is basically designed for algebraic operations on encrypted data of predetermined shape, such as a polynomial ring. Thus, a non-tailored combination of HE and the system can yield very inefficient performance, and many previous HE-based face template protection methods are hundreds of times slower than plain systems without protection. In this study, we propose \mathsf IDFace , a new HE-based secure and efficient face identification method with template protection. \mathsf IDFace  is designed on the basis of two novel techniques for efficient searching on a (homomorphically encrypted) biometric database with an angular metric. The first technique is a template representation transformation that sharply reduces the unit cost for the matching test. The second is a space-efficient encoding that reduces wasted space from the encryption algorithm, thus saving the number of operations on encrypted templates. Through experiments, we show that \mathsf IDFace  can identify a face template from among a database of 1M encrypted templates in 126ms, showing only 2xoverhead compared to the identification over plaintexts.
A Real-world Display Inverse Rendering Dataset
Authors: Seokjun Choi; Hoon-Gyu Chung; Yujin Jeon; Giljoo Nam; Seung-Hwan Baek
Venue: ICCV 2025 (main)

Abstract: Inverse rendering aims to reconstruct geometry and reflectance from captured images. Display-camera imaging systems offer unique advantages for this task: each pixel can easily function as a programmable point light source, and the polarized light emitted by LCD displays facilitates diffuse-specular separation. Despite these benefits, there is currently no public real-world dataset captured using display-camera systems, unlike other setups such as light stages. This absence hinders the development and evaluation of display-based inverse rendering methods. In this paper, we introduce the first real-world dataset for display-based inverse rendering. To achieve this, we construct and calibrate an imaging system comprising an LCD display and stereo polarization cameras. We then capture a diverse set of objects with diverse geometry and reflectance under one-light-at-a-time (OLAT) display patterns. We also provide high-quality ground-truth geometry. Our dataset enables the synthesis of captured images under arbitrary display patterns and different noise levels. Using this dataset, we evaluate the performance of existing photometric stereo and inverse rendering methods, and provide a simple, yet effective baseline for display inverse rendering, outperforming state-of-the-art inverse rendering methods.Code and dataset are available on our project page at https://michaelcsj.github.io/DIR/.