no code implementations • 17 Apr 2024 • Hao-Wei Chen, Yu-Syuan Xu, Kelvin C. K. Chan, Hsien-Kai Kuo, Chun-Yi Lee, Ming-Hsuan Yang
Towards this goal, we propose AdaIR, a novel framework that enables low storage cost and efficient training without sacrificing performance.
1 code implementation • 16 Mar 2024 • Li-Yuan Tsao, Yi-Chen Lo, Chia-Che Chang, Hao-Wei Chen, Roy Tseng, Chien Feng, Chun-Yi Lee
This prior is a latent code predicted by our proposed latent module conditioned on the low-resolution image, which is then transformed by the flow model into an SR image.
Ranked #3 on Image Super-Resolution on DIV2K val - 4x upscaling (using extra training data)
1 code implementation • 1 Feb 2024 • Chia-Cheng Chiang, Li-Cheng Lan, Wei-Fang Sun, Chien Feng, Cho-Jui Hsieh, Chun-Yi Lee
In this paper, we focus on single-demonstration imitation learning (IL), a practical approach for real-world applications where obtaining numerous expert demonstrations is costly or infeasible.
1 code implementation • 28 Sep 2023 • Yuhang Song, Anh Nguyen, Chun-Yi Lee
This paper tackles the critical challenge of object navigation in autonomous navigation systems, particularly focusing on the problem of target approach and episode termination in environments with long optimal episode length in Deep Reinforcement Learning (DRL) based methods.
no code implementations • 17 Sep 2023 • Hsuan-Kung Yang, Tsung-Chih Chiang, Ting-Ru Liu, Chun-Wei Huang, Jou-Min Liu, Chun-Yi Lee
The challenge of navigation in environments with dynamic objects continues to be a central issue in the study of autonomous agents.
1 code implementation • 18th International Conference on Machine Vision and Applications (MVA) 2023 • Hao-Yu Hou, Mu-Yi Shen, Chia-Chi Hsu, En-Ming Huang, Yu-Chen Huang, Yu-Cheng Xia, Chien-Yao Wang, Chun-Yi Lee
Detecting small objects is often impeded by blurriness and low resolution, which poses substantial challenges for accurately detecting and localizing such objects.
Ranked #1 on Small Object Detection on SOD4SB Public Test (using extra training data)
1 code implementation • 18 Jul 2023 • Yuki Kondo, Norimichi Ukita, Takayuki Yamaguchi, Hao-Yu Hou, Mu-Yi Shen, Chia-Chi Hsu, En-Ming Huang, Yu-Chen Huang, Yu-Cheng Xia, Chien-Yao Wang, Chun-Yi Lee, Da Huo, Marc A. Kastner, TingWei Liu, Yasutomo Kawanishi, Takatsugu Hirayama, Takahiro Komamizu, Ichiro Ide, Yosuke Shinya, Xinyao Liu, Guang Liang, Syusuke Yasui
Small Object Detection (SOD) is an important machine vision topic because (i) a variety of real-world applications require object detection for distant objects and (ii) SOD is a challenging task due to the noisy, blurred, and less-informative image appearances of small objects.
Ranked #2 on Small Object Detection on SOD4SB Public Test (using extra training data)
1 code implementation • 4 Jun 2023 • Wei-Fang Sun, Cheng-Kuang Lee, Simon See, Chun-Yi Lee
In fully cooperative multi-agent reinforcement learning (MARL) settings, environments are highly stochastic due to the partial observability of each agent and the continuously changing policies of other agents.
Ranked #1 on SMAC on SMAC 26m_vs_30m
no code implementations • 25 May 2023 • Tsu-Ching Hsiao, Hao-Wei Chen, Hsuan-Kung Yang, Chun-Yi Lee
Addressing pose ambiguity in 6D object pose estimation from single RGB images presents a significant challenge, particularly due to object symmetries or occlusions.
1 code implementation • CVPR 2023 • Hao-Wei Chen, Yu-Syuan Xu, Min-Fong Hong, Yi-Min Tsai, Hsien-Kai Kuo, Chun-Yi Lee
Implicit neural representation has recently shown a promising ability in representing images with arbitrary resolutions.
2 code implementations • CVPR 2023 • Jie-En Yao, Li-Yuan Tsao, Yi-Chen Lo, Roy Tseng, Chia-Che Chang, Chun-Yi Lee
Flow-based methods have demonstrated promising results in addressing the ill-posed nature of super-resolution (SR) by learning the distribution of high-resolution (HR) images with the normalizing flow.
Ranked #5 on Image Super-Resolution on DIV2K val - 4x upscaling (using extra training data)
no code implementations • 5 Mar 2023 • Hsuan-Kung Yang, Tsung-Chih Chiang, Ting-Ru Liu, Chun-Wei Huang, Jou-Min Liu, Chun-Yi Lee
In the context of autonomous navigation, effectively conveying abstract navigational cues to agents in dynamic environments poses challenges, particularly when the navigation information is multimodal.
1 code implementation • 16 Nov 2022 • Ting-Hsuan Liao, Huang-Ru Liao, Shan-Ya Yang, Jie-En Yao, Li-Yuan Tsao, Hsu-Shen Liu, Bo-Wun Cheng, Chen-Hao Chao, Chia-Che Chang, Yi-Chen Lo, Chun-Yi Lee
Despite their effectiveness, using depth as domain invariant information in UDA tasks may lead to multiple issues, such as excessively high extraction costs and difficulties in achieving a reliable prediction quality.
1 code implementation • 26 Sep 2022 • Chen-Hao Chao, Wei-Fang Sun, Bo-Wun Cheng, Chun-Yi Lee
In addition, we show that USBMs' inability to preserve the property of conservativeness may lead to degraded performance in practice.
no code implementations • 18 Aug 2022 • Hao-Wei Chen, Ting-Hsuan Liao, Hsuan-Kung Yang, Chun-Yi Lee
This paper introduces pixel-wise prediction based visual odometry (PWVO), which is a dense prediction task that evaluates the values of translation and rotation for every pixel in its input observations.
2 code implementations • ICLR 2022 • Chen-Hao Chao, Wei-Fang Sun, Bo-Wun Cheng, Yi-Chen Lo, Chia-Che Chang, Yu-Lun Liu, Yu-Lin Chang, Chia-Ping Chen, Chun-Yi Lee
These methods facilitate the training procedure of conditional score models, as a mixture of scores can be separately estimated using a score model and a classifier.
no code implementations • 9 Mar 2022 • Hsuan-Kung Yang, Tsu-Ching Hsiao, Ting-Hsuan Liao, Hsu-Shen Liu, Li-Yuan Tsao, Tzu-Wen Wang, Shan-Ya Yang, Yu-Wen Chen, Huang-Ru Liao, Chun-Yi Lee
In this paper, we introduce a new concept of incorporating factorized flow maps as mid-level representations, for bridging the perception and the control modules in modular learning based robotic frameworks.
1 code implementation • 11 Aug 2021 • Chin-Jui Chang, Chun-Yi Lee, Yi-Hsuan Yang
This paper proposes a new self-attention based model for music score infilling, i. e., to generate a polyphonic music sequence that fills in the gap between given past and future contexts.
no code implementations • 30 May 2021 • Chin-Jui Chang, Yu-Wei Chu, Chao-Hsien Ting, Hao-Kang Liu, Zhang-Wei Hong, Chun-Yi Lee
Deep reinforcement learning (DRL) has been demonstrated to provide promising results in several challenging decision making and control tasks.
1 code implementation • 29 Apr 2021 • Chen-Hao Chao, Bo-Wun Cheng, Chun-Yi Lee
Recent researches on unsupervised domain adaptation (UDA) have demonstrated that end-to-end ensemble learning frameworks serve as a compelling option for UDA tasks.
Ranked #13 on Unsupervised Domain Adaptation on SYNTHIA-to-Cityscapes
1 code implementation • 16 Feb 2021 • Wei-Fang Sun, Cheng-Kuang Lee, Chun-Yi Lee
In fully cooperative multi-agent reinforcement learning (MARL) settings, the environments are highly stochastic due to the partial observability of each agent and the continuously changing policies of the other agents.
Ranked #1 on SMAC on SMAC 27m_vs_30m
no code implementations • 1 Jan 2021 • Chen-Hao Chao, Bo-Wun Cheng, Chien Feng, Chun-Yi Lee
In this paper, we propose a pseudo label fusion framework (PLF), a learning framework developed to deal with the domain gap between a source domain and a target domain for performing semantic segmentation based UDA in the unseen target domain.
1 code implementation • 1 Jan 2021 • Yu Ming Chen, Kuan-Yu Chang, Chien Liu, Tsu-Ching Hsiao, Zhang-Wei Hong, Chun-Yi Lee
Macro actions have been demonstrated to be beneficial for the learning processes of an agent.
no code implementations • 16 Jul 2020 • Po-Han Chiang, Hsuan-Kung Yang, Zhang-Wei Hong, Chun-Yi Lee
Nevertheless, integrating step returns into a single target sacrifices the diversity of the advantages offered by different step return targets.
no code implementations • 5 Aug 2019 • Yi-Hsiang Chang, Kuan-Yu Chang, Henry Kuo, Chun-Yi Lee
However, by using a proper macro action, defined as a sequence of primitive actions, an agent is able to bypass intermediate states to a farther state and facilitate its learning procedure.
1 code implementation • 24 May 2019 • Hsuan-Kung Yang, Po-Han Chiang, Min-Fong Hong, Chun-Yi Lee
Exploration bonuses derived from the novelty of observations in an environment have become a popular approach to motivate exploration for reinforcement learning (RL) agents in the past few years.
no code implementations • ICLR 2019 • Zhang-Wei Hong, Tsu-Jui Fu, Tzu-Yun Shann, Yi-Hsiang Chang, Chun-Yi Lee
Our framework consists of a deep reinforcement learning (DRL) agent and an inverse dynamics model contesting with each other.
no code implementations • ICLR 2019 • Hsin-Wei Yu, Po-Yu Wu, Chih-An Tsao, You-An Shen, Shih-Hsuan Lin, Zhang-Wei Hong, Yi-Hsiang Chang, Chun-Yi Lee
In this paper, we propose a modular approach which separates the instruction-to-action mapping procedure into two separate stages.
no code implementations • 24 Jan 2019 • Hsuan-Kung Yang, Po-Han Chiang, Kuan-Wei Ho, Min-Fong Hong, Chun-Yi Lee
We propose to employ optical flow estimation errors to examine the novelty of new observations, such that agents are able to memorize and understand the visited states in a more comprehensive fashion.
no code implementations • 9 Sep 2018 • Hsuan-Kung Yang, An-Chieh Cheng, Kuan-Wei Ho, Tsu-Jui Fu, Chun-Yi Lee
The additional depth prediction path supplements the relationship prediction model in a way that bounding boxes or segmentation masks are unable to deliver.
no code implementations • ICLR 2019 • Zhang-Wei Hong, Tsu-Jui Fu, Tzu-Yun Shann, Yi-Hsiang Chang, Chun-Yi Lee
Our framework consists of a deep reinforcement learning (DRL) agent and an inverse dynamics model contesting with each other.
no code implementations • CVPR 2018 • Yu-Syuan Xu, Tsu-Jui Fu, Hsuan-Kung Yang, Chun-Yi Lee
We explore the use of a decision network to adaptively assign different frame regions to different networks based on a metric called expected confidence score.
no code implementations • NeurIPS 2018 • Zhang-Wei Hong, Tzu-Yun Shann, Shih-Yang Su, Yi-Hsiang Chang, Chun-Yi Lee
Efficient exploration remains a challenging research problem in reinforcement learning, especially when an environment contains large state spaces, deceptive local optima, or sparse rewards.
no code implementations • 1 Feb 2018 • Zhang-Wei Hong, Chen Yu-Ming, Shih-Yang Su, Tzu-Yun Shann, Yi-Hsiang Chang, Hsuan-Kung Yang, Brian Hsi-Lin Ho, Chih-Chieh Tu, Yueh-Chuan Chang, Tsu-Ching Hsiao, Hsin-Wei Hsiao, Sih-Pin Lai, Chun-Yi Lee
Collecting training data from the physical world is usually time-consuming and even dangerous for fragile robots, and thus, recent advances in robot learning advocate the use of simulators as the training platform.
no code implementations • 21 Dec 2017 • Zhang-Wei Hong, Shih-Yang Su, Tzu-Yun Shann, Yi-Hsiang Chang, Chun-Yi Lee
DPIQN incorporates the learned policy features as a hidden vector into its own deep Q-network (DQN), such that it is able to predict better Q values for the controllable agents than the state-of-the-art deep reinforcement learning models.