GenAI4ELab/papercli-papers huggingface dataset:
RIOcc: Efficient Cross-Modal Fusion Transformer with Collaborative Feature Refinement for 3D Semantic Occupancy Prediction
Authors: Baojie Fan; Xiaotian Li; Yuhan Zhou; Yuyu Jiang; Jiandong Tian; Huijie Fan
Venue: ICCV 2025 (main)

Abstract: The multi-modal 3D semantic occupancy task provides a comprehensive understanding of the scene and has received considerable attention in the field of autonomous driving. However, existing methods mainly focus on processing large-scale voxels, which bring high computational costs and degrade details. Additionally, they struggle to accurately capture occluded targets and distant information. In this paper, we propose a novel LiDAR-Camera 3D semantic occupancy prediction framework called RIOcc, with collaborative feature refinement and multi-scale cross-modal fusion transformer. Specifically, RIOcc encodes multi-modal data into a unified Bird's Eye View (BEV) space, which reduces computational complexity and enhances the efficiency of feature alignment. Then, multi-scale feature processing substantially expands the receptive fields. Meanwhile, in the LiDAR branch, we design the Dual-branch Pooling (DBP) to adaptively enhance geometric features across both the Channel and Grid dimensions. In the camera branch, the Wavelet and Semantic Encoders are developed to extract high-level semantic features with abundant edge and structural information. Finally, to facilitate effective cross-modal complementarity, we develop the Deformable Dual-Attention (DDA) module. Extensive experiments demonstrate that RIOcc achieves state-of-the-art performance, with 54.2 mIoU and 25.9 mIoU on the Occ3D-nuScenes and nuScenes-Occupancy datasets, respectively.
Golden Noise for Diffusion Models: A Learning Framework
Authors: Zikai Zhou; Shitong Shao; Lichen Bai; Shufei Zhang; Zhiqiang Xu; Bo Han; Zeke Xie
Venue: ICCV 2025 (main)

Abstract: Text-to-image diffusion model is a popular paradigm that synthesizes personalized images by providing a text prompt and a random Gaussian noise. While people observe that some noises are "golden noises" that can achieve better text-image alignment and higher human preference than others, we still lack a machine learning framework to obtain those golden noises. To learn golden noises for diffusion sampling, we mainly make three contributions in this paper. First, we identify a new concept termed the noise prompt, which aims at turning a random Gaussian noise into a golden noise by adding a small desirable perturbation derived from the text prompt. Following the concept, we first formulate the noise prompt learning framework that systematically learns "prompted" golden noise associated with a text prompt for diffusion models. Second, we design a noise prompt data collection pipeline and collect a large-scale noise prompt dataset (NPD) that contains 100k pairs of random noises and golden noises with the associated text prompts. With the prepared NPD as the training dataset, we trained a small noise prompt network (NPNet) that can directly learn to transform a random noise into a golden noise. The learned golden noise perturbation can be considered as a kind of prompt for noise, as it is rich in semantic information and tailored to the given text prompt. Third, our extensive experiments demonstrate the impressive effectiveness and generalization of NPNet on improving the quality of synthesized images across various diffusion models, including SDXL, DreamShaper-xl-v2-turbo, and Hunyuan-DiT. Moreover, NPNet is a small and efficient controller that acts as a plug-and-play module with very limited additional inference and computational costs, as it just provides a golden noise instead of a random noise without accessing the original pipeline.
CharaConsist: Fine-Grained Consistent Character Generation
Authors: Mengyu Wang; Henghui Ding; Jianing Peng; Yao Zhao; Yunpeng Chen; Yunchao Wei
Venue: ICCV 2025 (main)

Abstract: In text-to-image generation, producing a series of consistent contents that preserve the same identity is highly valuable for real-world applications. Although a few works have explored training-free methods to enhance the consistency of generated subjects, we observe that they suffer from the following problems.First, they fail to maintain consistent background details, which limits their applicability. Furthermore, when the foreground character undergoes large motion variations, inconsistencies in identity and clothing details become evident. To address these problems, we propose CharaConsist, which employs point-tracking attention and adaptive token merge along with decoupled control of the foreground and background.CharaConsist enables fine-grained consistency for both foreground and background, supporting the generation of one character in continuous shots within a fixed scene or in discrete shots across different scenes.Moreover, CharaConsist is the first consistent generation method tailored for text-to-image DiT model. Its ability to maintain fine-grained consistency, combined with the larger capacity of latest base model, enables it to produce high-quality visual outputs, broadening its applicability to a wider range of real-world scenarios.
RetinexMCNet: A Memory Controller Dominated Network for Low-Light Video Enhancement Based on Retinex
Authors: Meiao Wang; Xuejing Kang; Yaxi Lu; Jie Xu
Venue: ICCV 2025 (main)

Abstract: Low-light video enhancement (LLVE) aims to restore videos degraded by insufficient illumination.While existing methods have demonstrated their effectiveness, they often face challenges with intra-frame noise, overexposure, and inter-frame inconsistency since they fail to exploit the temporal continuity across frames.Inspired by the progressive video understanding mechanism of human, we propose a novel end-to-end two-stage memory controller (MC) dominated network (RetinexMCNet). Specifically, we first define the overall optimization objective for Retinex-based LLVE, and accordingly design our framework.In stage one, aided by a dual-perspective Lightness-Texture Stability (LTS) loss, we perform per-frame enhancement without the MC, which uses a channel-aware Illumination Adjustment Module (IAM) and an illumination-guided Reflectance Denoising Module (RDM) based on Retinex theory to mitigate intra-frame noise and overexposure.In stage two, we activate the MC to simulate human temporal memory and integrate it with high-quality single frames for global consistency.Extensive qualitative and quantitative experiments on common low-light sRGB datasets demonstrate our method significantly outperforms state-of-the-art approaches. Code is available at xxx/xxx/xxx.
Learnable Fractional Reaction-Diffusion Dynamics for Under-Display ToF Imaging and Beyond
Authors: Xin Qiao; Matteo Poggi; Xing Wei; Pengchao Deng; Yanhui Zhou; Stefano Mattoccia
Venue: ICCV 2025 (main)

Abstract: Under-display ToF imaging aims to achieve accurate depth sensing through a ToF camera placed beneath a screen panel. However, transparent OLED (TOLED) layers introduce severe degradations--such as signal attenuation, multi-path interference (MPI), and temporal noise--that significantly compromise depth quality. To alleviate this drawback, we propose Learnable Fractional Reaction-Diffusion Dynamics (LFRD^2), a hybrid framework that combines the expressive power of neural networks with the interpretability of physical modeling. Specifically, we implement a time-fractional reaction-diffusion module that enables iterative depth refinement with dynamically generated differential orders, capturing long-term dependencies. In addition, we introduce an efficient continuous convolution operator via coefficient prediction and repeated differentiation to further improve restoration quality. Experiments on four benchmark datasets demonstrate the effectiveness of our approach. The code is publicly available at https://github.com/wudiqx106/LFRD2.
Anomaly Detection of Integrated Circuits Package Substrates Using the Large Vision Model SAIC: Dataset Construction, Methodology, and Application
Authors: Ruiyun Yu; Bingyang Guo; Haoyuan Li
Venue: ICCV 2025 (main)

Abstract: Anomaly detection plays a crucial role in the industrial sector, especially in ensuring the quality of integrated circuits (IC), which are critical for product reliability and performance. With increasing demands for higher quality standards, anomaly detection during the IC manufacturing process has become a significant research focus. However, the progress of IC anomaly detection is hampered by the scarcity of defective samples and the shortage of well-defined annotations. To address this challenge, this paper focuses on the research in the field of IC, especially on ceramic package substrates (CPS). We construct a systematic automated optical inspection (AOI) equipment, and based on this, collected large-scale CPS 2D images to build a novel anomaly detection dataset (CPS2D-AD), which offers copious samples with precise annotations, including category, mask, and bounding box. To the best of our knowledge, CPS2D-AD is the largest dataset in the field of IC. Meanwhile, we conduct an extensive benchmark of CPS2D-AD, intending to supplement existing research by providing a baseline for the detection and localization of anomalies in high-resolution data of ceramic package substrates. In addition, we have developed a novel large vision model, Segment Any Integrated Circuits (SAIC), by embedding-based distillation mechanism based on CPS2D-AD datasets. Our CPS2D-AD is the first open-source anomaly detection dataset about ceramic package substrates, which can be accessed at https://github.com/Bingyang0410/CPS2D-AD.
Refer to Any Segmentation Mask Group With Vision-Language Prompts
Authors: Shengcao Cao; Zijun Wei; Jason Kuen; Kangning Liu; Lingzhi Zhang; Jiuxiang Gu; HyunJoon Jung; Liang-Yan Gui; Yu-Xiong Wang
Venue: ICCV 2025 (main)

Abstract: Recent image segmentation models have advanced to segment images into high-quality masks for visual entities, and yet they cannot provide comprehensive semantic understanding for complex queries based on both language and vision. This limitation reduces their effectiveness in applications that require user-friendly interactions driven by vision-language prompts. To bridge this gap, we introduce a novel task of omnimodal referring expression segmentation (ORES). In this task, a model produces a group of masks based on arbitrary prompts specified by text only or text plus reference visual entities. To address this new challenge, we propose a novel framework to "Refer to Any Segmentation Mask Group" (RAS), which augments segmentation models with complex multimodal interactions and comprehension via a mask-centric large multimodal model. For training and benchmarking ORES models, we create datasets MaskGroups-2M and MaskGroups-HQ to include diverse mask groups specified by text and reference entities. Through extensive evaluation, we demonstrate superior performance of RAS on our new ORES task, as well as classic referring expression segmentation (RES) and generalized referring expression segmentation (GRES) tasks. Project page: https://Ref2Any.github.io.
Integrating Visual Interpretation and Linguistic Reasoning for Geometric Problem Solving
Authors: Zixian Guo; Ming Liu; Qilong Wang; Zhilong Ji; Jinfeng Bai; Lei Zhang; Wangmeng Zuo
Venue: ICCV 2025 (main)

Abstract: Current large vision-language models (LVLMs) typically employ a connector module to link visual features with text embeddings of large language models (LLMs) and use end-to-end training to achieve multi-modal understanding in a unified process. Well alignment needs high-quality pre-training data and a carefully designed training process. Current LVLMs face challenges when addressing complex vision-language reasoning tasks, with their reasoning capabilities notably lagging behind those of LLMs. This paper proposes a paradigm shift: instead of training end-to-end vision-language reasoning models, we advocate for developing a decoupled reasoning framework based on existing visual interpretation specialists and text-based reasoning LLMs. Our approach leverages (1) a dedicated vision-language model to transform the visual content of images into textual descriptions and (2) an LLM to perform reasoning according to the visual-derived text and the original question. This method presents a cost-efficient solution for multi-modal model development by optimizing existing models to work collaboratively, avoiding end-to-end development of vision-language models from scratch. By transforming images into language model-compatible text representations, it facilitates future low-cost and flexible upgrades to upcoming powerful LLMs. We introduce an outcome-rewarded joint-tuning strategy to optimize the cooperation between the visual interpretation and linguistic reasoning model. Evaluation results on vision-language benchmarks demonstrate that the decoupled reasoning framework outperforms recent LVLMs. Our approach yields particularly significant performance gains on visually intensive geometric mathematics problems. The code is available: https://github.com/guozix/DVLR.
Dual-Rate Dynamic Teacher for Source-Free Domain Adaptive Object Detection
Authors: Qi He; Xiao Wu; Jun-Yan He; Shuai Li
Venue: ICCV 2025 (main)

Abstract: Source-Free Domain Adaptive Object Detection transfers knowledge from a labeled source domain to an unlabeled target domain while preserving data privacy by restricting access to source data during adaptation. Existing approaches predominantly leverage the Mean Teacher framework for self-training in the target domain. The exponential moving average (EMA) mechanism in the Mean Teacher stabilizes the training by averaging the student weights over training steps. However, in domain adaptation, its inherent lag in responding to emerging knowledge can hinder the rapid adaptation of the student to target-domain shifts. To address this challenge, Dual-rate Dynamic Teacher (DDT) with Asynchronous EMA (AEMA) is proposed, which implements group-wise parameter updates. In contrast to traditional EMA, which simultaneously updates all parameters, AEMA dynamically decomposes teacher parameters into two functional groups based on their contributions to capture the domain shift. By applying a distinct smoothing coefficient to two groups, AEMA simultaneously enables fast adaptation and historical knowledge retention. Comprehensive experiments carried out on three widely used traffic benchmarks have demonstrated that the proposed DDT achieves superior performance, outperforming SOTA methods by a clear margin. The codes are available at https://github.com/qih96/DDT.
How Do Multimodal Large Language Models Handle Complex Multimodal Reasoning? Placing Them in An Extensible Escape Game
Authors: Ziyue Wang; Yurui Dong; Fuwen Luo; Minyuan Ruan; Zhili Cheng; Chi Chen; Peng Li; Yang Liu
Venue: ICCV 2025 (main)

Abstract: The rapid advancing of Multimodal Large Language Models (MLLMs) has spurred interest in complex multimodal reasoning tasks in the real-world and virtual environment, which require coordinating multiple abilities, including visual perception, visual reasoning, spatial awareness, and target deduction. However, existing evaluations primarily assess the final task completion, often degrading assessments to isolated abilities such as visual grounding and visual question answering. Less attention is given to comprehensively and quantitatively analyzing reasoning process in multimodal environments, which is crucial for understanding model behaviors and underlying reasoning mechanisms beyond merely task success. To address this, we introduce MM-Escape, an extensible benchmark for investigating multimodal reasoning, inspired by real-world escape games. MM-Escape emphasizes intermediate model behaviors alongside final task completion. To achieve this, we develop EscapeCraft, a customizable and open environment that enables models to engage in free-form exploration for assessing multimodal reasoning. Extensive experiments show that MLLMs, regardless of scale, can successfully complete the simplest room escape tasks, with some exhibiting human-like exploration strategies. Yet, performance dramatically drops as task difficulty increases. Moreover, we observe that models severely suffer from accidental success, and that performance bottlenecks vary across models, revealing distinct failure modes and limitations in their multimodal reasoning abilities, such as repetitive trajectories without adaptive exploration, getting stuck in corners due to poor visual spatial awareness, and ineffective use of acquired props, such as the key. We hope our work sheds light on new challenges in multimodal reasoning, and uncovers potential improvements in MLLMs capabilities.
UST-SSM: Unified Spatio-Temporal State Space Models for Point Cloud Video Modeling
Authors: Peiming Li; Ziyi Wang; Yulin Yuan; Hong Liu; Xiangming Meng; Junsong Yuan; Mengyuan Liu
Venue: ICCV 2025 (main)

Abstract: Point cloud videos capture dynamic 3D motion while reducing the effects of lighting and viewpoint variations, making them highly effective for recognizing subtle and continuous human actions. Although Selective State Space Models (SSMs) have shown good performance in sequence modeling with linear complexity, the spatio-temporal disorder of point cloud videos hinders their unidirectional modeling when directly unfolding the point cloud video into a 1D sequence through temporally sequential scanning. To address this challenge, we propose the Unified Spatio-Temporal State Space Model (UST-SSM), which extends the latest advancements in SSMs to point cloud videos. Specifically, we introduce Spatial-Temporal Selection Scanning (STSS), which reorganizes unordered points into semantic-aware sequences through prompt-guided clustering, thereby enabling the effective utilization of points that are spatially and temporally distant yet similar within the sequence. For missing 4D geometric and motion details, Spatio-Temporal Structure Aggregation (STSA) aggregates spatio-temporal features and compensates. To improve temporal interaction within the sampled sequence, Temporal Interaction Sampling (TIS) enhances fine-grained temporal dependencies through non-anchor frame utilization and expanded receptive fields. Experimental results on the MSR-Action3D, NTU RGB+D, and Synthia 4D datasets validate the effectiveness of our method. Our code is available at https://github.com/wangzy01/UST-SSM.
Recover Biological Structure from Sparse-View Diffraction Images with Neural Volumetric Prior
Authors: Renzhi He; Haowen Zhou; Yubei Chen; Yi Xue
Venue: ICCV 2025 (main)

Abstract: Volumetric reconstruction of label-free living cells from non-destructive optical microscopic images reveals cellular metabolism in native environments. However, current optical tomography techniques require hundreds of 2D images to reconstruct a 3D volume, hindering them from intravital imaging of biological samples undergoing rapid dynamics. This poses a challenge of reconstructing the entire volume of semi-transparent biological samples from sparse views due to the restricted viewing angles of microscopes and the limited number of measurements. In this work, we develop Neural Volumetric Prior (NVP) for high-fidelity volumetric reconstruction of semi-transparent biological samples from sparse-view microscopic images. NVP integrates explicit and implicit neural representations and incorporates the physical prior of diffractive optics. We validate NVP on both simulated data and experimentally captured microscopic images. Compared to previous methods, NVP significantly reduces the required number of images by nearly 50-fold and processing time by 3-fold while maintaining state-of-the-art performance.NVP is the first technique to enable volumetric reconstruction of label-free biological samples from sparse-view microscopic images, paving the way for real-time 3D imaging of dynamically changing biological samples.
PropVG: End-to-End Proposal-Driven Visual Grounding with Multi-Granularity Discrimination
Authors: Ming Dai; Wenxuan Cheng; Jiedong Zhuang; Jiang-jiang Liu; Hongshen Zhao; Zhenhua Feng; Wankou Yang
Venue: ICCV 2025 (main)

Abstract: Recent advances in visual grounding have largely shifted away from traditional proposal-based two-stage frameworks due to their inefficiency and high computational complexity, favoring end-to-end direct reference paradigms. However, these methods rely exclusively on the referred target for supervision, overlooking the potential benefits of prominent prospective targets. Moreover, existing approaches often fail to incorporate multi-granularity discrimination, which is crucial for robust object identification in complex scenarios. To address these limitations, we propose PropVG, an end-to-end proposal-based framework that, to the best of our knowledge, is the first to seamlessly integrate foreground object proposal generation with referential object comprehension without requiring additional detectors. Furthermore, we introduce a Contrastive-based Refer Scoring (CRS) module, which employs contrastive learning at both sentence and word levels to enhance the model's capability in understanding and distinguishing referred objects. Additionally, we design a Multi-granularity Target Discrimination (MTD) module that fuses object- and semantic-level information to improve the recognition of absent targets. Extensive experiments on gRefCOCO (GREC/GRES), Ref-ZOM, R-RefCOCO/+/g, and RefCOCO/+/g (REC/RES) benchmarks demonstrate the effectiveness of PropVG.
StyleSRN: Scene Text Image Super-Resolution with Text Style Embedding
Authors: Shengrong Yuan; Runmin Wang; Ke Hao; Xuqi Ma; Changxin Gao; Li Liu; Nong Sang
Venue: ICCV 2025 (main)

Abstract: Scene text image super-resolution (STISR) focuses on enhancing the clarity and readability of low-resolution text images. Existing methods often rely on text probability distribution priors derived from text recognizers to guide the super-resolution process. While effective in capturing general structural information of text, these priors lack the ability to preserve specific text style details, such as font, stereoscopic effect and spatial transformation, leading to a loss of visual quality and stylistic consistency in the super-resolved images. To address these limitations, we propose a Style embedding-based scene text image Super-Resolution Network (StyleSRN), which introduces a text style embedding mechanism to preserve and enhance text style features during the super-resolution process. The proposed architecture includes a Style Enhancement Block for capturing multi-scale cross-channel dependencies, and a Style Content Fusion Block that effectively integrates text content with style information, ensuring that the structure and style of the restored text are not distorted. Furthermore, we introduce a Text Style Loss based on the Gram matrix to supervise the reconstruction process at the style level, thereby maintaining the stylistic consistency of the restored text images. Extensive experiments on the TextZoom dataset and five scene text recognition benchmarks demonstrate the superiority of our method. The code will be released in the future.