Search Results for author: Jianchao Tan

Found 19 papers, 9 papers with code

Hand-Transformer: Non-Autoregressive Structured Modeling for 3D Hand Pose Estimation

no code implementations ECCV 2020 Lin Huang, Jianchao Tan, Ji Liu, Junsong Yuan

To address this issue, we connect this structured output learning problem with the structured modeling framework in sequence transduction field.

3D Hand Pose Estimation

ASP: Automatic Selection of Proxy dataset for efficient AutoML

no code implementations17 Oct 2023 Peng Yao, Chao Liao, Jiyuan Jia, Jianchao Tan, Bin Chen, Chengru Song, Di Zhang

Deep neural networks have gained great success due to the increasing amounts of data, and diverse effective neural network designs.

Neural Architecture Search

USDC: Unified Static and Dynamic Compression for Visual Transformer

no code implementations17 Oct 2023 Huan Yuan, Chao Liao, Jianchao Tan, Peng Yao, Jiyuan Jia, Bin Chen, Chengru Song, Di Zhang

To alleviate two disadvantages of two categories of methods, we propose to unify the static compression and dynamic compression techniques jointly to obtain an input-adaptive compressed model, which can further better balance the total compression ratios and the model performances.

Model Compression

Unified Language-Vision Pretraining in LLM with Dynamic Discrete Visual Tokenization

1 code implementation9 Sep 2023 Yang Jin, Kun Xu, Liwei Chen, Chao Liao, Jianchao Tan, Quzhe Huang, Bin Chen, Chenyi Lei, An Liu, Chengru Song, Xiaoqiang Lei, Di Zhang, Wenwu Ou, Kun Gai, Yadong Mu

Specifically, we introduce a well-designed visual tokenizer to translate the non-linguistic image into a sequence of discrete tokens like a foreign language that LLM can read.

Language Modelling Large Language Model +1

SHARK: A Lightweight Model Compression Approach for Large-scale Recommender Systems

no code implementations18 Aug 2023 Beichuan Zhang, Chenggen Sun, Jianchao Tan, Xinjun Cai, Jun Zhao, Mengqi Miao, Kang Yin, Chengru Song, Na Mou, Yang song

Increasing the size of embedding layers has shown to be effective in improving the performance of recommendation models, yet gradually causing their sizes to exceed terabytes in industrial recommender systems, and hence the increase of computing and storage costs.

Model Compression Quantization +1

Resource Constrained Model Compression via Minimax Optimization for Spiking Neural Networks

1 code implementation9 Aug 2023 Jue Chen, Huan Yuan, Jianchao Tan, Bin Chen, Chengru Song, Di Zhang

We propose an improved end-to-end Minimax optimization method for this sparse learning problem to better balance the model performance and the computation efficiency.

Model Compression Sparse Learning

PA&DA: Jointly Sampling PAth and DAta for Consistent NAS

1 code implementation CVPR 2023 Shun Lu, Yu Hu, Longxing Yang, Zihao Sun, Jilin Mei, Jianchao Tan, Chengru Song

Our method only requires negligible computation cost for optimizing the sampling distributions of path and data, but achieves lower gradient variance during supernet training and better generalization performance for the supernet, resulting in a more consistent NAS.

PaletteNeRF: Palette-based Color Editing for NeRFs

no code implementations25 Dec 2022 Qiling Wu, Jianchao Tan, Kun Xu

Instead of predicting pixel colors as in vanilla NeRFs, our method predicts additive weights.

E^2VTS: Energy-Efficient Video Text Spotting from Unmanned Aerial Vehicles

1 code implementation5 Jun 2022 Zhenyu Hu, Zhenyu Wu, Pengcheng Pi, Yunhe Xue, Jiayi Shen, Jianchao Tan, Xiangru Lian, Zhangyang Wang, Ji Liu

Unmanned Aerial Vehicles (UAVs) based video text spotting has been extensively used in civil and military domains.

Text Spotting

LPFS: Learnable Polarizing Feature Selection for Click-Through Rate Prediction

1 code implementation1 Jun 2022 Yi Guo, Zhaocheng Liu, Jianchao Tan, Chao Liao, Sen yang, Lei Yuan, Dongying Kong, Zhi Chen, Ji Liu

When training is finished, some gates are exact zero, while others are around one, which is particularly favored by the practical hot-start training in the industry, due to no damage to the model performance before and after removing the features corresponding to exact-zero gates.

Click-Through Rate Prediction feature selection

TNASP: A Transformer-based NAS Predictor with a Self-evolution Framework

no code implementations NeurIPS 2021 Shun Lu, Jixiang Li, Jianchao Tan, Sen yang, Ji Liu

Predictor-based Neural Architecture Search (NAS) continues to be an important topic because it aims to mitigate the time-consuming search procedure of traditional NAS methods.

Neural Architecture Search

GDP: Stabilized Neural Network Pruning via Gates with Differentiable Polarization

no code implementations ICCV 2021 Yi Guo, Huan Yuan, Jianchao Tan, Zhangyang Wang, Sen yang, Ji Liu

During the training process, the polarization effect will drive a subset of gates to smoothly decrease to exact zero, while other gates gradually stay away from zero by a large margin.

Model Compression Network Pruning

Hand Image Understanding via Deep Multi-Task Learning

1 code implementation ICCV 2021 Xiong Zhang, Hongsheng Huang, Jianchao Tan, Hongmin Xu, Cheng Yang, Guozhu Peng, Lei Wang, Ji Liu

To further improve the performance of these tasks, we propose a novel Hand Image Understanding (HIU) framework to extract comprehensive information of the hand object from a single RGB image, by jointly considering the relationships between these tasks.

3D Hand Pose Estimation Multi-Task Learning +1

ResRep: Lossless CNN Pruning via Decoupling Remembering and Forgetting

6 code implementations ICCV 2021 Xiaohan Ding, Tianxiang Hao, Jianchao Tan, Ji Liu, Jungong Han, Yuchen Guo, Guiguang Ding

Via training with regular SGD on the former but a novel update rule with penalty gradients on the latter, we realize structured sparsity.

Learning Color Compatibility in Fashion Outfits

no code implementations5 Jul 2020 Heming Zhang, Xuewen Yang, Jianchao Tan, Chi-Hao Wu, Jue Wang, C. -C. Jay Kuo

Color compatibility is important for evaluating the compatibility of a fashion outfit, yet it was neglected in previous studies.

graph construction

DCNAS: Densely Connected Neural Architecture Search for Semantic Image Segmentation

no code implementations CVPR 2021 Xiong Zhang, Hongmin Xu, Hong Mo, Jianchao Tan, Cheng Yang, Lei Wang, Wenqi Ren

Neural Architecture Search (NAS) has shown great potentials in automatically designing scalable network architectures for dense image predictions.

Ranked #13 on Semantic Segmentation on Cityscapes test (using extra training data)

Image Segmentation Neural Architecture Search +1

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