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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Yunhang Shen Chaoyou Fu Peixian Chen Mengdan Zhang Ke Li Xing Sun Yunsheng Wu Shaohui Lin Rongrong Ji Tencent Youtu Lab School of Computer Science and Technology East China Normal University Shanghai China Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China Xiamen University P.R. China

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Vision foundation models have been explored recently to build general-purpose vision systems. However, predomi-nant paradigms, driven by casting instance-level tasks as an object-word alignment, bring heavy cross-modality in-teraction, which is not effective in prompting object detection and visual grounding. Another line of work that fo-cuses on pixel-level tasks often encounters a large annotation gap of things and stuff, and suffers from mutual inter-ference between foreground-object and background-class segmentation. In stark contrast to the prevailing methods, we present APE, a universal visual perception model for aligning and prompting everything all at once in an image to perform diverse tasks, i.e., detection, segmentation, and grounding, as an instance-level sentence-object matching paradigm. Specifically, APE advances the convergence of detection and grounding by reformulating language-guided grounding as open-vocabulary detection, which efficiently scales up model prompting to thousands of category vocab-ularies and region descriptions while maintaining the ef-fectiveness of cross-modality fusion. To bridge the granu-larity gap of different pixel-level tasks, APE equalizes se-mantic and panoptic segmentation to proxy instance learning by considering any isolated regions as individual in-stances. APE aligns vision and language representation on broad data with natural and challenging characteristics all at once without task-specific fine-tuning. The extensive ex-periments on over 160 datasets demonstrate that, with only one-suit of weights, APE outperforms (or is on par with) the state-of-the-art models, proving that an effective yet univer-sal perception for anything aligning and prompting is in-deed feasible. Codes and trained models are released at https://***/shenyunhang/APE.

关键词： Image segmentation Vocabulary Visualization Grounding Annotations Machine vision Object detection

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Improving adversarial robustness via information bottleneck distillation 23

Improving adversarial robustness via information bottleneck ...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Huafeng Kuang Hong Liu YongJian Wu Shin'ichi Satoh Rongrong Ji Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China Xiamen University P.R. China National Institute of Informatics Tokyo Japan Youtu Laboratory Tencent Shanghai China

Previous studies have shown that optimizing the information bottleneck can significantly improve the robustness of deep neural networks. Our study closely examines the information bottleneck principle and proposes an Information Bottleneck Distillation approach. This specially designed, robust distillation technique utilizes prior knowledge obtained from a robust pre-trained model to boost information bottlenecks. Specifically, we propose two distillation strategies that align with the two optimization processes of the information bottleneck. Firstly, we use a robust soft-label distillation method to increase the mutual information between latent features and output prediction. Secondly, we introduce an adaptive feature distillation method that automatically transfers relevant knowledge from the teacher model to the student model, thereby reducing the mutual information between the input and latent features. We conduct extensive experiments to evaluate our approach's robustness against state-of-the-art adversarial attackers such as PGD-attack and AutoAttack. Our experimental results demonstrate the effectiveness of our approach in significantly improving adversarial robustness. Our code is available at https://***/SkyKuang/IBD.

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PortraitBooth: A Versatile Portrait Model for Fast Identity-Preserved Personalization

PortraitBooth: A Versatile Portrait Model for Fast Identity-...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Xu Peng Junwei Zhu Boyuan Jiang Ying Tai Donghao Luo Jiangning Zhang Wei Lin Taisong Jin Chengjie Wang Rongrong Ji Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China Xiamen University China School of Informatics Xiamen University China Tencent Youtu Lab. School of Intelligence Science and Technology Nanjing University Suzhou China

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Recent advancements in personalized image generation using diffusion models have been noteworthy. However, existing methods suffer from inefficiencies due to the requirement for subject-specific fine-tuning. This computationally intensive process hinders efficient deployment, limiting practical usability. Moreover, these methods often grapple with identity distortion and limited expression diversity. In light of these challenges, we propose Portrait-Booth, an innovative approach designed for high efficiency, robust identity preservation, and expression-editable text-to-image generation, without the need for fine-tuning. Por-traitBooth leverages subject embeddingsfrom aface recognition model for personalized image generation without fine-tuning. It eliminates computational overhead and mitigates identity distortion. The introduced dynamic identity preservation strategy further ensures close resemblance to the original image identity. Moreover, PortraitBooth incorporates emotion-aware cross-attention control for diverse facial expressions in generated images, supporting text-driven expression editing. Its scalability enables efficient and high-quality image creation, including multi-subject generation. Extensive results demonstrate superior performance over other state-of-the-art methods in both single and multiple image generation scenarios. Our project page is at https://***.

关键词： Training Limiting Image recognition Image synthesis Scalability Face recognition Text to image

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RefBERT: A Two-Stage Pre-trained Framework for Automatic Rename Refactoring

arXiv

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arXiv 2023年

作者： Liu, Hao Wang, Yanlin Wei, Zhao Xu, Yong Wang, Juhong Li, Hui Ji, Rongrong Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China School of Informatics Xiamen University China School of Software Engineering Sun Yat-sen University China Tencent China

Refactoring is an indispensable practice of improving the quality and maintainability of source code in software evolution. Rename refactoring is the most frequently performed refactoring that suggests a new name for an identifier to enhance readability when the identifier is poorly named. However, most existing works only identify renaming activities between two versions of source code, while few works express concern about how to suggest a new name. In this paper, we study automatic rename refactoring on variable names, which is considered more challenging than other rename refactoring activities. We first point out the connections between rename refactoring and various prevalent learning paradigms and the difference between rename refactoring and general text generation in natural language processing. Based on our observations, we propose RefBERT, a two-stage pre-trained framework for rename refactoring on variable names. RefBERT first predicts the number of sub-tokens in the new name and then generates sub-tokens accordingly. Several techniques, including constrained masked language modeling, contrastive learning, and the bag-of-tokens loss, are incorporated into RefBERT to tailor it for automatic rename refactoring on variable names. Through extensive experiments on our constructed refactoring datasets, we show that the generated variable names of RefBERT are more accurate and meaningful than those produced by the existing method. Our implementation and data are available at https://***/KDEGroup/RefBERT. Copyright © 2023, The Authors. All rights reserved.

关键词： Modeling languages

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EALink: An efficient and Accurate Pre-Trained Framework for Issue-Commit Link Recovery

EALink: An Efficient and Accurate Pre-Trained Framework for ...

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IEEE International Conference on Automated Software Engineering (ASE)

作者： Chenyuan Zhang Yanlin Wang Zhao Wei Yong Xu Juhong Wang Hui Li Rongrong Ji Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China School of Informatics Xiamen University China School of Software Engineering Sun Yat-sen University China Tencent China

Issue-commit links, as a type of software traceability links, play a vital role in various software development and maintenance tasks. However, they are typically deficient, as developers often forget or fail to create tags when making commits. Existing studies have deployed deep learning techniques, including pretrained models, to improve automatic issue-commit link recovery. Despite their promising performance, we argue that previous approaches have four main problems, hindering them from recovering links in large software projects. To overcome these problems, we propose an efficient and accurate pre-trained framework called EALink for issue-commit link recovery. EALink requires much fewer model parameters than existing pre-trained methods, bringing efficient training and recovery. Moreover, we design various techniques to improve the recovery accuracy of EALink. We construct a large-scale dataset and conduct extensive experiments to demonstrate the power of EALink. Results show that EALink outperforms the state-of-the-art methods by a large margin (15.23%-408.65%) on various evaluation metrics. Meanwhile, its training and inference overhead is orders of magnitude lower than existing methods. We provide our implementation and data at https://***/KDEGroup/EALink.

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Feature Denoising Diffusion Model for Blind Image Quality Assessment 39

Feature Denoising Diffusion Model for Blind Image Quality As...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Li, Xudong Zhang, Yan Shen, Yunhang Li, Ke Hu, Runze Zheng, Xiawu Zhao, Sicheng Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China Xiamen University 361005 China Tencent Youtu Lab Shanghai China School of Information and Electronics Beijing Institute of Technology Beijing China BNRist Tsinghua University Beijing China

ISBN: (纸本)157735897X

Blind Image Quality Assessment (BIQA) aims to evaluate image quality in line with human perception, without reference benchmarks. Currently, deep learning BIQA methods typically depend on using features from high-level tasks for transfer learning. However, the inherent differences between BIQA and these high-level tasks inevitably introduce noise into the quality-aware features. In this paper, we take an initial step toward exploring the diffusion model for feature denoising in BIQA, namely Perceptual Feature Diffusion for IQA (PFD-IQA), which aims to remove noise from quality-aware features. Specifically, 1) we propose a Perceptual Prior Discovery and Aggregation module to establish two auxiliary tasks to discover potential low-level features in images that are used to aggregate perceptual textual prompt conditions for the diffusion model. 2) we propose a Perceptual Conditional Feature Refinement strategy, which matches noisy features to predefined denoising trajectories and then performs exact feature denoising based on textual prompt conditions. By incorporating a lightweight denoiser and requiring only a few feature denoising steps (e.g., just five iterations), our PFD-IQA framework achieves superior performance across eight standard BIQA datasets, validating its effectiveness. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Damage detection

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Towards Omni-supervised Referring Expression Segmentation

Towards Omni-supervised Referring Expression Segmentation

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IEEE International Conference on multimedia and Expo (ICME)

作者： Minglang Huang Yiyi Zhou Gen Luo Guannan Jiang Weilin Zhuang Xiaoshuai Sun Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China Xiamen University China Institute of Artificial Intelligence Xiamen University China Intelligent Manufacturing Department Contemporary Amperex Technology Co. Limited (CATL) China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Referring Expression Segmentation (RES) is a challenging task in computer vision that involves segmenting image instances using textual descriptions. Conventional approaches suffer from the high cost of acquiring segmentation labels. To overcome this, we propose a novel learning task called Omni-supervised Referring Expression Segmentation (Omni-RES) which leverages unlabeled, fully labeled, and weakly labeled data, such as referring points or bounding boxes, for efficient RES training. Our approach is based on a teacher-student learning framework, where weak labels guide the selection and refinement of high-quality pseudo-masks for training, rather than serving as direct supervision signals. We tested Omni-RES on various state-of-the-art RES models and datasets, demonstrating its superiority over fully-supervised and semi-supervised methods. Remarkably, with just 10% fully labeled data, Omni-RES can match the performance of 100% supervised training. Additionally, it enables using large-scale vision-language datasets like Visual Genome for cost-effective RES training, setting a new state-of-the-art performance in RES, such as 80.66 on RefCOCO. Our code is released at: https://***/nineblu/omni-res

关键词： Training Image segmentation Visualization Computer vision Costs Codes Grounding

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X-Mesh: Towards Fast and Accurate Text-driven 3D Stylization via Dynamic Textual Guidance

X-Mesh: Towards Fast and Accurate Text-driven 3D Stylization...

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International Conference on Computer Vision (ICCV)

作者： Yiwei Ma Haowei Wang Xiaoqing Zhang Guannan Jiang Xiaoshuai Sun Weilin Zhuang Jiayi Ji Rongrong Ji Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China Xiamen University P.R. China Contemporary Amperex Technology Co. Limited (CATL) Fujian China

Text-driven 3D stylization is a complex and crucial task in the fields of computer vision (CV) and computer graphics (CG), aimed at transforming a bare mesh to fit a tar-get text. Prior methods adopt text-independent multilayer perceptrons (MLPs) to predict the attributes of the target mesh with the supervision of CLIP loss. However, such text-independent architecture lacks textual guidance during predicting attributes, thus leading to unsatisfactory stylization and slow convergence. To address these limitations, we present X-Mesh, an innovative text-driven 3D stylization framework that incorporates a novel Text-guided Dynamic Attention Module (TDAM). The TDAM dynamically integrates the guidance of the target text by utilizing text-relevant spatial and channel-wise attentions during vertex feature extraction, resulting in more accurate attribute prediction and faster convergence speed. Furthermore, existing works lack standard benchmarks and automated metrics for evaluation, often relying on subjective and non-reproducible user studies to assess the quality of stylized 3D assets. To overcome this limitation, we introduce a new standard text-mesh benchmark, namely MIT-30, and two automated metrics, which will enable future research to achieve fair and objective comparisons. Our extensive qualitative and quantitative experiments demonstrate that X-Mesh outperforms previous state-of-the-art methods. Our codes and results are available at our project webpage: https://***/Projects/X-Mesh/

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EALink: An efficient and Accurate Pre-trained Framework for Issue-Commit Link Recovery

arXiv

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arXiv 2023年

作者： Zhang, Chenyuan Wang, Yanlin Wei, Zhao Xu, Yong Wang, Juhong Li, Hui Ji, Rongrong Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China School of Informatics Xiamen University China School of Software Engineering Sun Yat-sen University China Tencent China

Issue-commit links, as a type of software traceability links, play a vital role in various software development and maintenance tasks. However, they are typically deficient, as developers often forget or fail to create tags when making commits. Existing studies have deployed deep learning techniques, including pre-trained models, to improve automatic issue-commit link recovery. Despite their promising performance, we argue that previous approaches have four main problems, hindering them from recovering links in large software projects. To overcome these problems, we propose an efficient and accurate pre-trained framework called EALink for issue-commit link recovery. EALink requires much fewer model parameters than existing pre-trained methods, bringing efficient training and recovery. Moreover, we design various techniques to improve the recovery accuracy of EALink. We construct a large-scale dataset and conduct extensive experiments to demonstrate the power of EALink. Results show that EALink outperforms the state-of-the-art methods by a large margin (15.23%-408.65%) on various evaluation metrics. Meanwhile, its training and inference overhead is orders of magnitude lower than existing methods. We provide our implementation and data at https://***/KDEGroup/EALink. Copyright © 2023, The Authors. All rights reserved.

关键词： Recovery

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SLOPER4D: A Scene-Aware Dataset for Global 4D Human Pose Estimation in Urban Environments

SLOPER4D: A Scene-Aware Dataset for Global 4D Human Pose Est...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Yudi Dai Yitai Lin Xiping Lin Chenglu Wen Lan Xu Hongwei Yi Siqi Shen Yuexin Ma Cheng Wang Fujian Key Laboratory of Sensing and Computing for Smart Cities Xiamen University Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China School of Informatics Xiamen University ShanghaiTech University China Max Planck Institute for Intelligent Systems Germany

We present SLOPER4D, a novel scene-aware dataset collected in large urban environments to facilitate the research of global human pose estimation (GHPE) with human-scene interaction in the wild. Employing a head-mounted device integrated with a LiDAR and camera, we record 12 human subjects' activities over 10 diverse urban scenes from an egocentric view. Frame-wise annotations for 2D key points, 3D pose parameters, and global translations are provided, together with reconstructed scene point clouds. To obtain accurate 3D ground truth in such large dynamic scenes, we propose a joint optimization method to fit local SMPL meshes to the scene and fine-tune the camera calibration during dynamic motions frame by frame, resulting in plausible and scene-natural 3D human poses. Even-tually, SLOPER4D consists of 15 sequences of human motions, each of which has a trajectory length of more than 200 meters (up to 1,300 meters) and covers an area of more than 200 m 2 (up to 30,000 m 2 ), including more than 100k LiDAR frames, 300k video frames, and 500k IMU-based motion frames. With SLOPER4D, we provide a detailed and thorough analysis of two critical tasks, including camera-based 3D HPE and LiDAR-based 3D HPE in urban environments, and benchmark a new task, GHPE. The in-depth analysis demonstrates SLOPER4D poses significant challenges to existing methods and produces great research opportunities. The dataset and code are released at http://***/sloper4d/.

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