activity
20182021
most citedJoint Pruning & Quantization for Extremely Sparse Neural Networks

10 citations · 20 across the 7 of their papers we have counts for

collaborators

14 papers

cs.CV20211 cited

Interactive Object Segmentation with Dynamic Click Transform

Chun-Tse Lin, Wei-Chih Tu, Chih-Ting Liu +1

In the interactive segmentation, users initially click on the target object to segment the main body and then provide corrections on mislabeled regions to iteratively refine the se…

cs.CV20211 cited

Hard Samples Rectification for Unsupervised Cross-domain Person Re-identification

Chih-Ting Liu, Man-Yu Lee, Tsai-Shien Chen +1

Person re-identification (re-ID) has received great success with the supervised learning methods. However, the task of unsupervised cross-domain re-ID is still challenging. In this…

cs.CV2021

Video-based Person Re-identification without Bells and Whistles

Chih-Ting Liu, Jun-Cheng Chen, Chu-Song Chen +1

Video-based person re-identification (Re-ID) aims at matching the video tracklets with cropped video frames for identifying the pedestrians under different cameras. However, there…

eess.IV2020

How to Exploit the Transferability of Learned Image Compression to Conventional Codecs

Jan P. Klopp, Keng-Chi Liu, Liang-Gee Chen +1

Lossy image compression is often limited by the simplicity of the chosen loss measure. Recent research suggests that generative adversarial networks have the ability to overcome th…

cs.CV2020

Viewpoint-Aware Channel-Wise Attentive Network for Vehicle Re-Identification

Tsai-Shien Chen, Man-Yu Lee, Chih-Ting Liu +1

Vehicle re-identification (re-ID) matches images of the same vehicle across different cameras. It is fundamentally challenging because the dramatically different appearance caused…

cs.CV202010 cited

Joint Pruning & Quantization for Extremely Sparse Neural Networks

Po-Hsiang Yu, Sih-Sian Wu, Jan P. Klopp +2

We investigate pruning and quantization for deep neural networks. Our goal is to achieve extremely high sparsity for quantized networks to enable implementation on low cost and low…