Search Results for author: Changlin Li

Found 14 papers, 11 papers with code

Continual Object Detection via Prototypical Task Correlation Guided Gating Mechanism

1 code implementation CVPR 2022 BinBin Yang, Xinchi Deng, Han Shi, Changlin Li, Gengwei Zhang, Hang Xu, Shen Zhao, Liang Lin, Xiaodan Liang

To make ROSETTA automatically determine which experience is available and useful, a prototypical task correlation guided Gating Diversity Controller(GDC) is introduced to adaptively adjust the diversity of gates for the new task based on class-specific prototypes.

Continual Learning object-detection +1

Look Back and Forth: Video Super-Resolution with Explicit Temporal Difference Modeling

no code implementations CVPR 2022 Takashi Isobe, Xu Jia, Xin Tao, Changlin Li, Ruihuang Li, Yongjie Shi, Jing Mu, Huchuan Lu, Yu-Wing Tai

Instead of directly feeding consecutive frames into a VSR model, we propose to compute the temporal difference between frames and divide those pixels into two subsets according to the level of difference.

Motion Compensation Optical Flow Estimation +1

Automated Progressive Learning for Efficient Training of Vision Transformers

1 code implementation CVPR 2022 Changlin Li, Bohan Zhuang, Guangrun Wang, Xiaodan Liang, Xiaojun Chang, Yi Yang

First, we develop a strong manual baseline for progressive learning of ViTs, by introducing momentum growth (MoGrow) to bridge the gap brought by model growth.

Beyond Fixation: Dynamic Window Visual Transformer

1 code implementation CVPR 2022 Pengzhen Ren, Changlin Li, Guangrun Wang, Yun Xiao, Qing Du, Xiaodan Liang, Xiaojun Chang

Recently, a surge of interest in visual transformers is to reduce the computational cost by limiting the calculation of self-attention to a local window.

Dynamic Slimmable Denoising Network

no code implementations17 Oct 2021 Zutao Jiang, Changlin Li, Xiaojun Chang, Jihua Zhu, Yi Yang

Here, we present dynamic slimmable denoising network (DDS-Net), a general method to achieve good denoising quality with less computational complexity, via dynamically adjusting the channel configurations of networks at test time with respect to different noisy images.

Fairness Image Denoising

DS-Net++: Dynamic Weight Slicing for Efficient Inference in CNNs and Transformers

1 code implementation21 Sep 2021 Changlin Li, Guangrun Wang, Bing Wang, Xiaodan Liang, Zhihui Li, Xiaojun Chang

Dynamic networks have shown their promising capability in reducing theoretical computation complexity by adapting their architectures to the input during inference.

Fairness Model Compression

Joint Depth and Normal Estimation from Real-world Time-of-flight Raw Data

no code implementations8 Aug 2021 Rongrong Gao, Na Fan, Changlin Li, Wentao Liu, Qifeng Chen

We present a novel approach to joint depth and normal estimation for time-of-flight (ToF) sensors.

Dynamic Slimmable Network

1 code implementation CVPR 2021 Changlin Li, Guangrun Wang, Bing Wang, Xiaodan Liang, Zhihui Li, Xiaojun Chang

Here, we explore a dynamic network slimming regime, named Dynamic Slimmable Network (DS-Net), which aims to achieve good hardware-efficiency via dynamically adjusting filter numbers of networks at test time with respect to different inputs, while keeping filters stored statically and contiguously in hardware to prevent the extra burden.

Fairness Model Compression

BossNAS: Exploring Hybrid CNN-transformers with Block-wisely Self-supervised Neural Architecture Search

1 code implementation ICCV 2021 Changlin Li, Tao Tang, Guangrun Wang, Jiefeng Peng, Bing Wang, Xiaodan Liang, Xiaojun Chang

In this work, we present Block-wisely Self-supervised Neural Architecture Search (BossNAS), an unsupervised NAS method that addresses the problem of inaccurate architecture rating caused by large weight-sharing space and biased supervision in previous methods.

Image Classification Neural Architecture Search

Density Map Guided Object Detection in Aerial Images

1 code implementation12 Apr 2020 Changlin Li, Taojiannan Yang, Sijie Zhu, Chen Chen, Shanyue Guan

Specifically, we propose a Density-Map guided object detection Network (DMNet), which is inspired from the observation that the object density map of an image presents how objects distribute in terms of the pixel intensity of the map.

Image Cropping object-detection +2

Blockwisely Supervised Neural Architecture Search with Knowledge Distillation

1 code implementation29 Nov 2019 Changlin Li, Jiefeng Peng, Liuchun Yuan, Guangrun Wang, Xiaodan Liang, Liang Lin, Xiaojun Chang

Moreover, we find that the knowledge of a network model lies not only in the network parameters but also in the network architecture.

 Ranked #1 on Neural Architecture Search on CIFAR-100 (Top-1 Error Rate metric)

Knowledge Distillation Neural Architecture Search

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