35 citations · 118 across the 20 of their papers we have counts for
13 papers · 1 filter
FedFR: Joint Optimization Federated Framework for Generic and Personalized Face Recognition
Chih-Ting Liu, Chien-Yi Wang, Shao-Yi Chien +1
Current state-of-the-art deep learning based face recognition (FR) models require a large number of face identities for central training. However, due to the growing privacy awaren…
Learning Discriminative Shrinkage Deep Networks for Image Deconvolution
Pin-Hung Kuo, Jinshan Pan, Shao-Yi Chien +1
Most existing methods usually formulate the non-blind deconvolution problem into a maximum-a-posteriori framework and address it by manually designing kinds of regularization terms…
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…
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…
Incremental False Negative Detection for Contrastive Learning
Tsai-Shien Chen, Wei-Chih Hung, Hung-Yu Tseng +2
Self-supervised learning has recently shown great potential in vision tasks through contrastive learning, which aims to discriminate each image, or instance, in the dataset. Howeve…
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…
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…
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…
Semantics-Guided Clustering with Deep Progressive Learning for Semi-Supervised Person Re-identification
Chih-Ting Liu, Yu-Jhe Li, Shao-Yi Chien +1
Person re-identification (re-ID) requires one to match images of the same person across camera views. As a more challenging task, semi-supervised re-ID tackles the problem that onl…
Orientation-aware Vehicle Re-identification with Semantics-guided Part Attention Network
Tsai-Shien Chen, Chih-Ting Liu, Chih-Wei Wu +1
Vehicle re-identification (re-ID) focuses on matching images of the same vehicle across different cameras. It is fundamentally challenging because differences between vehicles are…
Spatially and Temporally Efficient Non-local Attention Network for Video-based Person Re-Identification
Chih-Ting Liu, Chih-Wei Wu, Yu-Chiang Frank Wang +1
Video-based person re-identification (Re-ID) aims at matching video sequences of pedestrians across non-overlapping cameras. It is a practical yet challenging task of how to embed…
Computation-Performance Optimization of Convolutional Neural Networks with Redundant Kernel Removal
Chih-Ting Liu, Yi-Heng Wu, Yu-Sheng Lin +1
Deep Convolutional Neural Networks (CNNs) are widely employed in modern computer vision algorithms, where the input image is convolved iteratively by many kernels to extract the kn…
Learning to Compose with Professional Photographs on the Web
Yi-Ling Chen, Jan Klopp, Min Sun +2
Photo composition is an important factor affecting the aesthetics in photography. However, it is a highly challenging task to model the aesthetic properties of good compositions du…