87 citations · 131 across the 22 of their papers we have counts for
6 papers · 1 filter
ANFIC: Image Compression Using Augmented Normalizing Flows
Yung-Han Ho, Chih-Chun Chan, Wen-Hsiao Peng +2
This paper introduces an end-to-end learned image compression system, termed ANFIC, based on Augmented Normalizing Flows (ANF). ANF is a new type of flow model, which stacks multip…
A Dual-Critic Reinforcement Learning Framework for Frame-level Bit Allocation in HEVC/H.265
Yung-Han Ho, Guo-Lun Jin, Yun Liang +2
This paper introduces a dual-critic reinforcement learning (RL) framework to address the problem of frame-level bit allocation in HEVC/H.265. The objective is to minimize the disto…
Weakly-Supervised Image Semantic Segmentation Using Graph Convolutional Networks
Shun-Yi Pan, Cheng-You Lu, Shih-Po Lee +1
This work addresses weakly-supervised image semantic segmentation based on image-level class labels. One common approach to this task is to propagate the activation scores of Class…
Video Rescaling Networks with Joint Optimization Strategies for Downscaling and Upscaling
Yan-Cheng Huang, Yi-Hsin Chen, Cheng-You Lu +3
This paper addresses the video rescaling task, which arises from the needs of adapting the video spatial resolution to suit individual viewing devices. We aim to jointly optimize v…
GSVNet: Guided Spatially-Varying Convolution for Fast Semantic Segmentation on Video
Shih-Po Lee, Si-Cun Chen, Wen-Hsiao Peng
This paper addresses fast semantic segmentation on video.Video segmentation often calls for real-time, or even fasterthan real-time, processing. One common recipe for conserving co…
Class-incremental Learning with Rectified Feature-Graph Preservation
Cheng-Hsun Lei, Yi-Hsin Chen, Wen-Hsiao Peng +1
In this paper, we address the problem of distillation-based class-incremental learning with a single head. A central theme of this task is to learn new classes that arrive in seque…