Search Results for author: Yutian Lin

Found 10 papers, 4 papers with code

Improving Bird's Eye View Semantic Segmentation by Task Decomposition

no code implementations CVPR 2024 Tianhao Zhao, Yongcan Chen, Yu Wu, Tianyang Liu, Bo Du, Peilun Xiao, Shi Qiu, Hongda Yang, Guozhen Li, Yi Yang, Yutian Lin

In the first stage, we train a BEV autoencoder to reconstruct the BEV segmentation maps given corrupted noisy latent representation, which urges the decoder to learn fundamental knowledge of typical BEV patterns.

Autonomous Driving Bird's-Eye View Semantic Segmentation +2

Visual Imitation Learning with Calibrated Contrastive Representation

no code implementations21 Jan 2024 Yunke Wang, Linwei Tao, Bo Du, Yutian Lin, Chang Xu

Adversarial Imitation Learning (AIL) allows the agent to reproduce expert behavior with low-dimensional states and actions.

Contrastive Learning Imitation Learning

Omni-Q: Omni-Directional Scene Understanding for Unsupervised Visual Grounding

no code implementations CVPR 2024 Sai Wang, Yutian Lin, Yu Wu

Unsupervised visual grounding methods alleviate the issue of expensive manual annotation of image-query pairs by generating pseudo-queries.

Scene Understanding Visual Grounding

Visible-Infrared Person Re-Identification via Patch-Mixed Cross-Modality Learning

no code implementations16 Feb 2023 Zhihao Qian, Yutian Lin, Bo Du

In this paper, we propose a Patch-Mixed Cross-Modality framework (PMCM), where two images of the same person from two modalities are split into patches and stitched into a new one for model learning.

Image Generation Person Re-Identification +2

MixSiam: A Mixture-based Approach to Self-supervised Representation Learning

no code implementations4 Nov 2021 Xiaoyang Guo, Tianhao Zhao, Yutian Lin, Bo Du

In this way, the model could access more variant data samples of an instance and keep predicting invariant discriminative representations for them.

Contrastive Learning Representation Learning

Unsupervised Person Re-identification with Stochastic Training Strategy

2 code implementations16 Aug 2021 Tianyang Liu, Yutian Lin, Bo Du

State-of-the-art unsupervised re-ID methods usually follow a clustering-based strategy, which generates pseudo labels by clustering and maintains a memory to store instance features and represent the centroid of the clusters for contrastive learning.

Clustering Contrastive Learning +1

Re-identification = Retrieval + Verification: Back to Essence and Forward with a New Metric

1 code implementation23 Nov 2020 Zheng Wang, Xin Yuan, Toshihiko Yamasaki, Yutian Lin, Xin Xu, Wenjun Zeng

In essence, current re-ID overemphasizes the importance of retrieval but underemphasizes that of verification, \textit{i. e.}, all returned images are considered as the target.

Image Retrieval Retrieval

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