87 citations · 131 across the 22 of their papers we have counts for
22 papers
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…
MoTIF: Learning Motion Trajectories with Local Implicit Neural Functions for Continuous Space-Time Video Super-Resolution
Yi-Hsin Chen, Si-Cun Chen, Yen-Yu Lin +1
This work addresses continuous space-time video super-resolution (C-STVSR) that aims to up-scale an input video both spatially and temporally by any scaling factors. One key challe…
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…
Content-Adaptive Motion Rate Adaption for Learned Video Compression
Chih-Hsuan Lin, Yi-Hsin Chen, Wen-Hsiao Peng
This paper introduces an online motion rate adaptation scheme for learned video compression, with the aim of achieving content-adaptive coding on individual test sequences to mitig…
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…
Learned Video Compression for YUV 4:2:0 Content Using Flow-based Conditional Inter-frame Coding
Yung-Han Ho, Chih-Hsuan Lin, Peng-Yu Chen +4
This paper proposes a learning-based video compression framework for variable-rate coding on YUV 4:2:0 content. Most existing learning-based video compression models adopt the trad…
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…
Neural Frank-Wolfe Policy Optimization for Region-of-Interest Intra-Frame Coding with HEVC/H.265
Yung-Han Ho, Chia-Hao Kao, Wen-Hsiao Peng +1
This paper presents a reinforcement learning (RL) framework that utilizes Frank-Wolfe policy optimization to solve Coding-Tree-Unit (CTU) bit allocation for Region-of-Interest (ROI…
B-CANF: Adaptive B-frame Coding with Conditional Augmented Normalizing Flows
Mu-Jung Chen, Yi-Hsin Chen, Wen-Hsiao Peng
Over the past few years, learning-based video compression has become an active research area. However, most works focus on P-frame coding. Learned B-frame coding is under-explored…
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…
Action-Constrained Reinforcement Learning for Frame-Level Bit Allocation in HEVC/H.265 through Frank-Wolfe Policy Optimization
Yung-Han Ho, Yun Liang, Chia-Hao Kao +1
This paper presents a reinforcement learning (RL) framework that leverages Frank-Wolfe policy optimization to address frame-level bit allocation for HEVC/H.265. Most previous RL-ba…
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…
Learning Priors for Adversarial Autoencoders
Hui-Po Wang, Wen-Hsiao Peng, Wei-Jan Ko
Most deep latent factor models choose simple priors for simplicity, tractability or not knowing what prior to use. Recent studies show that the choice of the prior may have a profo…
Learning Goal-Oriented Visual Dialog Agents: Imitating and Surpassing Analytic Experts
Yen-Wei Chang, Wen-Hsiao Peng
This paper tackles the problem of learning a questioner in the goal-oriented visual dialog task. Several previous works adopt model-free reinforcement learning. Most pretrain the m…
Learned Image Compression with Soft Bit-based Rate-Distortion Optimization
David Alexandre, Chih-Peng Chang, Wen-Hsiao Peng +1
This paper introduces the notion of soft bits to address the rate-distortion optimization for learning-based image compression. Recent methods for such compression train an autoenc…
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…