Search Results for author: Yunshan Zhong

Found 14 papers, 11 papers with code

Learning Image Demoireing from Unpaired Real Data

1 code implementation5 Jan 2024 Yunshan Zhong, Yuyao Zhou, Yuxin Zhang, Fei Chao, Rongrong Ji

The proposed method, referred to as Unpaired Demoireing (UnDeM), synthesizes pseudo moire images from unpaired datasets, generating pairs with clean images for training demoireing models.

I&S-ViT: An Inclusive & Stable Method for Pushing the Limit of Post-Training ViTs Quantization

1 code implementation16 Nov 2023 Yunshan Zhong, Jiawei Hu, Mingbao Lin, Mengzhao Chen, Rongrong Ji

Albeit the scalable performance of vision transformers (ViTs), the dense computational costs (training & inference) undermine their position in industrial applications.

Quantization

Spatial Re-parameterization for N:M Sparsity

no code implementations9 Jun 2023 Yuxin Zhang, Mingbao Lin, Yunshan Zhong, Mengzhao Chen, Fei Chao, Rongrong Ji

This paper presents a Spatial Re-parameterization (SpRe) method for the N:M sparsity in CNNs.

MultiQuant: A Novel Multi-Branch Topology Method for Arbitrary Bit-width Network Quantization

1 code implementation14 May 2023 Yunshan Zhong, Mingbao Lin, Yuyao Zhou, Mengzhao Chen, Yuxin Zhang, Fei Chao, Rongrong Ji

However, in this paper, we investigate existing methods and observe a significant accumulation of quantization errors caused by frequent bit-width switching of weights and activations, leading to limited performance.

Quantization

Distribution-Flexible Subset Quantization for Post-Quantizing Super-Resolution Networks

1 code implementation10 May 2023 Yunshan Zhong, Mingbao Lin, Jingjing Xie, Yuxin Zhang, Fei Chao, Rongrong Ji

Compared to the common iterative exhaustive search algorithm, our strategy avoids the enumeration of all possible combinations in the universal set, reducing the time complexity from exponential to linear.

Quantization Super-Resolution

Bi-directional Masks for Efficient N:M Sparse Training

1 code implementation13 Feb 2023 Yuxin Zhang, Yiting Luo, Mingbao Lin, Yunshan Zhong, Jingjing Xie, Fei Chao, Rongrong Ji

We focus on addressing the dense backward propagation issue for training efficiency of N:M fine-grained sparsity that preserves at most N out of M consecutive weights and achieves practical speedups supported by the N:M sparse tensor core.

Shadow Removal by High-Quality Shadow Synthesis

1 code implementation8 Dec 2022 Yunshan Zhong, Lizhou You, Yuxin Zhang, Fei Chao, Yonghong Tian, Rongrong Ji

Specifically, the encoder extracts the shadow feature of a region identity which is then paired with another region identity to serve as the generator input to synthesize a pseudo image.

Image Generation Shadow Removal +1

Exploiting the Partly Scratch-off Lottery Ticket for Quantization-Aware Training

1 code implementation12 Nov 2022 Yunshan Zhong, Gongrui Nan, Yuxin Zhang, Fei Chao, Rongrong Ji

In QAT, the contemporary experience is that all quantized weights are updated for an entire training process.

Quantization

Dynamic Dual Trainable Bounds for Ultra-low Precision Super-Resolution Networks

1 code implementation8 Mar 2022 Yunshan Zhong, Mingbao Lin, Xunchao Li, Ke Li, Yunhang Shen, Fei Chao, Yongjian Wu, Rongrong Ji

However, these methods suffer from severe performance degradation when quantizing the SR models to ultra-low precision (e. g., 2-bit and 3-bit) with the low-cost layer-wise quantizer.

Quantization Super-Resolution

IntraQ: Learning Synthetic Images with Intra-Class Heterogeneity for Zero-Shot Network Quantization

1 code implementation CVPR 2022 Yunshan Zhong, Mingbao Lin, Gongrui Nan, Jianzhuang Liu, Baochang Zhang, Yonghong Tian, Rongrong Ji

In this paper, we observe an interesting phenomenon of intra-class heterogeneity in real data and show that existing methods fail to retain this property in their synthetic images, which causes a limited performance increase.

Quantization

Fine-grained Data Distribution Alignment for Post-Training Quantization

1 code implementation9 Sep 2021 Yunshan Zhong, Mingbao Lin, Mengzhao Chen, Ke Li, Yunhang Shen, Fei Chao, Yongjian Wu, Rongrong Ji

While post-training quantization receives popularity mostly due to its evasion in accessing the original complete training dataset, its poor performance also stems from scarce images.

Quantization

Lottery Jackpots Exist in Pre-trained Models

2 code implementations18 Apr 2021 Yuxin Zhang, Mingbao Lin, Yunshan Zhong, Fei Chao, Rongrong Ji

Existing studies achieve the sparsity of neural networks via time-consuming weight training or complex searching on networks with expanded width, which greatly limits the applications of network pruning.

Network Pruning

Re-ID Driven Localization Refinement for Person Search

no code implementations ICCV 2019 Chuchu Han, Jiacheng Ye, Yunshan Zhong, Xin Tan, Chi Zhang, Changxin Gao, Nong Sang

The state-of-the-art methods train the detector individually, and the detected bounding boxes may be sub-optimal for the following re-ID task.

Person Re-Identification Person Search

Re-Identification Supervised Texture Generation

no code implementations CVPR 2019 Jian Wang, Yunshan Zhong, Yachun Li, Chi Zhang, Yichen Wei

The estimation of 3D human body pose and shape from a single image has been extensively studied in recent years.

Person Re-Identification Texture Synthesis

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