87 citations · 131 across the 22 of their papers we have counts for
12 papers · 1 filter
Panoptic Video Scene Graph Generation
Jingkang Yang, Wenxuan Peng, Xiangtai Li +8
Towards building comprehensive real-world visual perception systems, we propose and study a new problem called panoptic scene graph generation (PVSG). PVSG relates to the existing…
Hierarchical B-frame Video Coding Using Two-Layer CANF without Motion Coding
David Alexandre, Hsueh-Ming Hang, Wen-Hsiao Peng
Typical video compression systems consist of two main modules: motion coding and residual coding. This general architecture is adopted by classical coding schemes (such as internat…
Learned Hierarchical B-frame Coding with Adaptive Feature Modulation for YUV 4:2:0 Content
Mu-Jung Chen, Hong-Sheng Xie, Cheng Chien +2
This paper introduces a learned hierarchical B-frame coding scheme in response to the Grand Challenge on Neural Network-based Video Coding at ISCAS 2023. We address specifically th…
OpenOOD: Benchmarking Generalized Out-of-Distribution Detection
Jingkang Yang, Pengyun Wang, Dejian Zou +13
Out-of-distribution (OOD) detection is vital to safety-critical machine learning applications and has thus been extensively studied, with a plethora of methods developed in the lit…
CANF-VC: Conditional Augmented Normalizing Flows for Video Compression
Yung-Han Ho, Chih-Peng Chang, Peng-Yu Chen +2
This paper presents an end-to-end learning-based video compression system, termed CANF-VC, based on conditional augmented normalizing flows (CANF). Most learned video compression s…
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…
All about Structure: Adapting Structural Information across Domains for Boosting Semantic Segmentation
Wei-Lun Chang, Hui-Po Wang, Wen-Hsiao Peng +1
In this paper we tackle the problem of unsupervised domain adaptation for the task of semantic segmentation, where we attempt to transfer the knowledge learned upon synthetic datas…
An Autoencoder-based Learned Image Compressor: Description of Challenge Proposal by NCTU
David Alexandre, Chih-Peng Chang, Wen-Hsiao Peng +1
We propose a lossy image compression system using the deep-learning autoencoder structure to participate in the Challenge on Learned Image Compression (CLIC) 2018. Our autoencoder…