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Jiyang Qi

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3 papers

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TMLR Journal 2023 Journal Article

Understanding Self-Supervised Pretraining with Part-Aware Representation Learning

Jie Zhu
Jiyang Qi
Mingyu Ding
Xiaokang Chen
Ping Luo
Xinggang Wang
Wenyu Liu
Leye Wang

In this paper, we are interested in understanding self-supervised pretraining through studying the capability that self-supervised methods learn part-aware representations. The study is mainly motivated by that random views, used in contrastive learning, and random masked (visible) patches, used in masked image modeling, are often about object parts. We explain that contrastive learning is a part-to-whole task: the projection layer hallucinates the whole object representation from the object part representation learned from the encoder, and that masked image modeling is a part-to-part task: the masked patches of the object are hallucinated from the visible patches. The explanation suggests that the self-supervised pretrained encoder leans toward understanding the object part. We empirically compare the off-the-shelf encoders pretrained with several representative methods on object-level recognition and part-level recognition. The results show that the fully-supervised model outperforms self-supervised models for object-level recognition, and most self-supervised contrastive learning and masked image modeling methods outperform the fully-supervised method for part-level recognition. It is observed that the combination of contrastive learning and masked image modeling further improves the performance.

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NeurIPS Conference 2021 Conference Paper

Occluded Video Instance Segmentation: Dataset and ICCV 2021 Challenge

Jiyang Qi
Yan Gao
Yao Hu
Xinggang Wang
Xiaoyu Liu
Xiang Bai
Serge Belongie
Alan Yuille

Although deep learning methods have achieved advanced video object recognition performance in recent years, perceiving heavily occluded objects in a video is still a very challenging task. To promote the development of occlusion understanding, we collect a large-scale dataset called OVIS for video instance segmentation in the occluded scenario. OVIS consists of 296k high-quality instance masks and 901 occluded scenes. While our human vision systems can perceive those occluded objects by contextual reasoning and association, our experiments suggest that current video understanding systems cannot. On the OVIS dataset, all baseline methods encounter a significant performance degradation of about 80\% in the heavily occluded object group, which demonstrates that there is still a long way to go in understanding obscured objects and videos in a complex real-world scenario. To facilitate the research on new paradigms for video understanding systems, we launched a challenge basing on the OVIS dataset. The submitted top-performing algorithms have achieved much higher performance than our baselines. In this paper, we will introduce the OVIS dataset and further dissect it by analyzing the results of baselines and submitted methods. The OVIS dataset and challenge information can be found at \url{http: //songbai. site/ovis}.

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NeurIPS Conference 2021 Conference Paper

You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection

Yuxin Fang
Bencheng Liao
Xinggang Wang
Jiemin Fang
Jiyang Qi
Rui Wu
Jianwei Niu
Wenyu Liu

Can Transformer perform $2\mathrm{D}$ object- and region-level recognition from a pure sequence-to-sequence perspective with minimal knowledge about the $2\mathrm{D}$ spatial structure? To answer this question, we present You Only Look at One Sequence (YOLOS), a series of object detection models based on the vanilla Vision Transformer with the fewest possible modifications, region priors, as well as inductive biases of the target task. We find that YOLOS pre-trained on the mid-sized ImageNet-$1k$ dataset only can already achieve quite competitive performance on the challenging COCO object detection benchmark, e. g. , YOLOS-Base directly adopted from BERT-Base architecture can obtain $42. 0$ box AP on COCO val. We also discuss the impacts as well as limitations of current pre-train schemes and model scaling strategies for Transformer in vision through YOLOS. Code and pre-trained models are available at https: //github. com/hustvl/YOLOS.

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