Search Results for author: Michael Bi Mi

Found 9 papers, 7 papers with code

An Implicit Alignment for Video Super-Resolution

1 code implementation29 Apr 2023 Kai Xu, Ziwei Yu, Xin Wang, Michael Bi Mi, Angela Yao

Video super-resolution commonly uses a frame-wise alignment to support the propagation of information over time.

Video Super-Resolution

DepGraph: Towards Any Structural Pruning

1 code implementation CVPR 2023 Gongfan Fang, Xinyin Ma, Mingli Song, Michael Bi Mi, Xinchao Wang

Structural pruning enables model acceleration by removing structurally-grouped parameters from neural networks.

Network Pruning Neural Network Compression

Bias-Compensated Integral Regression for Human Pose Estimation

no code implementations25 Jan 2023 Kerui Gu, Linlin Yang, Michael Bi Mi, Angela Yao

Experimental results on both the human body and hand benchmarks show that BCIR is faster to train and more accurate than the original integral regression, making it competitive with state-of-the-art detection methods.

Hand Pose Estimation regression

Improving Deep Regression with Ordinal Entropy

1 code implementation21 Jan 2023 Shihao Zhang, Linlin Yang, Michael Bi Mi, Xiaoxu Zheng, Angela Yao

In computer vision, it is often observed that formulating regression problems as a classification task often yields better performance.

Classification Crowd Counting +2

PoseTriplet: Co-evolving 3D Human Pose Estimation, Imitation, and Hallucination under Self-supervision

1 code implementation CVPR 2022 Kehong Gong, Bingbing Li, Jianfeng Zhang, Tao Wang, Jing Huang, Michael Bi Mi, Jiashi Feng, Xinchao Wang

Existing self-supervised 3D human pose estimation schemes have largely relied on weak supervisions like consistency loss to guide the learning, which, inevitably, leads to inferior results in real-world scenarios with unseen poses.

3D Human Pose Estimation

Point2Seq: Detecting 3D Objects as Sequences

1 code implementation CVPR 2022 Yujing Xue, Jiageng Mao, Minzhe Niu, Hang Xu, Michael Bi Mi, Wei zhang, Xiaogang Wang, Xinchao Wang

We further propose a lightweight scene-to-sequence decoder that can auto-regressively generate words conditioned on features from a 3D scene as well as cues from the preceding words.

3D Object Detection object-detection

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