Search Results for author: Zechen Bai

Found 9 papers, 6 papers with code

Hallucination of Multimodal Large Language Models: A Survey

1 code implementation29 Apr 2024 Zechen Bai, Pichao Wang, Tianjun Xiao, Tong He, Zongbo Han, Zheng Zhang, Mike Zheng Shou

By drawing the granular classification and landscapes of hallucination causes, evaluation benchmarks, and mitigation methods, this survey aims to deepen the understanding of hallucinations in MLLMs and inspire further advancements in the field.

Hallucination

Bring Your Own Character: A Holistic Solution for Automatic Facial Animation Generation of Customized Characters

1 code implementation21 Feb 2024 Zechen Bai, Peng Chen, Xiaolan Peng, Lu Liu, Hui Chen, Mike Zheng Shou, Feng Tian

In our solution, a deep learning model was first trained to retarget the facial expression from input face images to virtual human faces by estimating the blendshape coefficients.

Unity

Skip \n: A Simple Method to Reduce Hallucination in Large Vision-Language Models

2 code implementations2 Feb 2024 Zongbo Han, Zechen Bai, Haiyang Mei, Qianli Xu, Changqing Zhang, Mike Zheng Shou

Recent advancements in large vision-language models (LVLMs) have demonstrated impressive capability in visual information understanding with human language.

Hallucination

Unsupervised Open-Vocabulary Object Localization in Videos

no code implementations ICCV 2023 Ke Fan, Zechen Bai, Tianjun Xiao, Dominik Zietlow, Max Horn, Zixu Zhao, Carl-Johann Simon-Gabriel, Mike Zheng Shou, Francesco Locatello, Bernt Schiele, Thomas Brox, Zheng Zhang, Yanwei Fu, Tong He

In this paper, we show that recent advances in video representation learning and pre-trained vision-language models allow for substantial improvements in self-supervised video object localization.

Object Object Localization +1

Object-Centric Multiple Object Tracking

1 code implementation ICCV 2023 Zixu Zhao, Jiaze Wang, Max Horn, Yizhuo Ding, Tong He, Zechen Bai, Dominik Zietlow, Carl-Johann Simon-Gabriel, Bing Shuai, Zhuowen Tu, Thomas Brox, Bernt Schiele, Yanwei Fu, Francesco Locatello, Zheng Zhang, Tianjun Xiao

Unsupervised object-centric learning methods allow the partitioning of scenes into entities without additional localization information and are excellent candidates for reducing the annotation burden of multiple-object tracking (MOT) pipelines.

Multiple Object Tracking Object +3

Unsupervised Multi-Source Domain Adaptation for Person Re-Identification

1 code implementation CVPR 2021 Zechen Bai, Zhigang Wang, Jian Wang, Di Hu, Errui Ding

Although achieving great success, most of them only use limited data from a single-source domain for model pre-training, making the rich labeled data insufficiently exploited.

Person Re-Identification Unsupervised Domain Adaptation

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