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Wenhang Ge

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4 papers

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NeurIPS Conference 2024 Conference Paper

X-Ray: A Sequential 3D Representation For Generation

Tao Hu
Wenhang Ge
Yuyang Zhao
Gim Hee Lee

We introduce X-Ray, a novel 3D sequential representation inspired by the penetrability of x-ray scans. X-Ray transforms a 3D object into a series of surface frames at different layers, making it suitable for generating 3D models from images. Our method utilizes ray casting from the camera center to capture geometric and textured details, including depth, normal, and color, across all intersected surfaces. This process efficiently condenses the whole 3D object into a multi-frame video format, motivating the utilize of a network architecture similar to those in video diffusion models. This design ensures an efficient 3D representation by focusing solely on surface information. Also, we propose a two-stage pipeline to generate 3D objects from X-Ray Diffusion Model and Upsampler. We demonstrate the practicality and adaptability of our X-Ray representation by synthesizing the complete visible and hidden surfaces of a 3D object from a single input image. Experimental results reveal the state-of-the-art superiority of our representation in enhancing the accuracy of 3D generation, paving the way for new 3D representation research and practical applications. Our project page is in \url{https: //tau-yihouxiang. github. io/projects/X-Ray/X-Ray. html}.

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ECAI Conference 2023 Conference Paper

Online Privacy Preservation for Camera-Incremental Person Re-Identification

Sheng Wu
Wenhang Ge
Jiong Wu
Jingke Meng
Huang Zhang

Task-incremental person re-identification aims to train a model with consecutively available cross-camera annotated data in the current task and a small number of saved data in preceding tasks, which may lead to individual privacy disclosure due to data storage and annotation. In this work, we investigate a more realistic online privacy preservation scenario for camera-incremental person re-identification, where data storage in preceding cameras is not allowed, while data in the current camera are intra-camera annotated online by a pedestrian tracking algorithm without cross-camera annotation. In this setup, the missing data of previous cameras not only results in catastrophic forgetting as task-incremental learning, but also makes the cross-camera association infeasible, which further leads to the incapability of person matching across cameras due to the camera-wise domain gap. To solve these problems, we propose an Online Privacy Preservation (OPP) framework based on the generated exemplars of previous cameras by DeepInversion, where generated exemplars used as supplements to alleviate forgetting and enable cross-camera association to be feasible for camera-wise domain shift mitigation, meanwhile further improving the cross-camera matching capability. Specifically, we propose to mine underlying cross-camera positive pairs between samples of the current camera and exemplars of previous cameras by similarity cues. Furthermore, we introduce a mixup learning strategy to handle the domain gap with mixed samples and labels. Finally, intra-camera incremental learning and cross-camera incremental learning are aggregated into the OPP framework. Extensive experiments on Re-ID benchmarks validate the superiority of the OPP framework as compared with state-of-the-art methods.

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AAAI Conference 2022 Conference Paper

Lifelong Person Re-identification by Pseudo Task Knowledge Preservation

Wenhang Ge
Junlong Du
Ancong Wu
Yuqiao Xian
Ke Yan
Feiyue Huang
Wei-Shi Zheng

In real world, training data for person re-identification (Re- ID) is collected discretely with spatial and temporal variations, which requires a model to incrementally learn new knowledge without forgetting old knowledge. This problem is called lifelong person re-identification (LReID). Variations of illumination and background for images of each task exhibit task-specific image style and lead to task-wise domain gap. In addition to missing data from the old tasks, task-wise domain gap is a key factor for catastrophic forgetting in LReID, which is ignored in existing approaches for LReID. The model tends to learn task-specific knowledge with task-wise domain gap, which results in stability and plasticity dilemma. To overcome this problem, we cast LReID as a domain adaptation problem and propose a pseudo task knowledge preservation framework to alleviate the domain gap. Our framework is based on a pseudo task transformation module which maps the features of the new task into the feature space of the old tasks to complement the limited saved exemplars of the old tasks. With extra transformed features in the task-specific feature space, we propose a task-specific domain consistency loss to implicitly alleviate the task-wise domain gap for learning task-shared knowledge instead of task-specific one. Furthermore, to guide knowledge preservation with the feature distributions of the old tasks, we propose to preserve knowledge on extra pseudo tasks which jointly distills knowledge and discriminates identity, in order to achieve a better tradeoff between stability and plasticity for lifelong learning with task-wise domain gap. Extensive experiments demonstrate the superiority of our method 1 as compared with the stateof-the-art lifelong learning and LReID methods.

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NeurIPS Conference 2022 Conference Paper

Text-Adaptive Multiple Visual Prototype Matching for Video-Text Retrieval

Chengzhi Lin
Ancong Wu
Junwei Liang
Jun Zhang
Wenhang Ge
Wei-Shi Zheng
Chunhua Shen

Cross-modal retrieval between videos and texts has gained increasing interest because of the rapid emergence of videos on the web. Generally, a video contains rich instance and event information and the query text only describes a part of the information. Thus, a video can have multiple different text descriptions and queries. We call it the Video-Text Correspondence Ambiguity problem. Current techniques mostly concentrate on mining local or multi-level alignment between contents of video and text (e. g. , object to entity and action to verb). It is difficult for these methods to alleviate video-text correspondence ambiguity by describing a video using only one feature, which is required to be matched with multiple different text features at the same time. To address this problem, we propose a Text-Adaptive Multiple Visual Prototype Matching Model. It automatically captures multiple prototypes to describe a video by adaptive aggregation on video token features. Given a query text, the similarity is determined by the most similar prototype to find correspondence in the video, which is called text-adaptive matching. To learn diverse prototypes for representing the rich information in videos, we propose a variance loss to encourage different prototypes to attend to different contents of the video. Our method outperforms the state-of-the-art methods on four public video retrieval datasets.

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