no code implementations • 4 Mar 2024 • Ruiqi Zhu, Tianhong Dai, Oya Celiktutan
Unlike prior methods that rely on paired data, we propose a novel approach for learning the mapping functions between state and action spaces across domains using unpaired data.
no code implementations • 15 Oct 2023 • Wangyu Wu, Tianhong Dai, Xiaowei Huang, Fei Ma, Jimin Xiao
In this process, the existing images and image-level labels provide the necessary control information, where GPT is employed to enrich the prompts, leading to the generation of diverse backgrounds.
no code implementations • 15 Oct 2023 • Wangyu Wu, Tianhong Dai, Xiaowei Huang, Fei Ma, Jimin Xiao
In this paper, we introduce a novel ViT-based WSSS method named top-K pooling with patch contrastive learning (TKP-PCL), which employs a top-K pooling layer to alleviate the limitations of previous max pooling selection.
Contrastive Learning Weakly supervised Semantic Segmentation +1
1 code implementation • 6 Jul 2023 • Ruiqi Zhu, Siyuan Li, Tianhong Dai, Chongjie Zhang, Oya Celiktutan
Our method can endow agents with the ability to explore and acquire the required prior behaviours and then connect to the task-specific behaviours in the demonstration to solve sparse-reward tasks without requiring additional demonstration of the prior behaviours.
no code implementations • 20 Jun 2022 • Wei Li, Shuai Xiao, Tianhong Dai, Shanxin Yuan, Tao Wang, Cheng Li, Fenglong Song
To further leverage these two paradigms, we propose a selective and joint HDR and denoising (SJ-HD$^2$R) imaging framework, utilizing scenario-specific priors to conduct the path selection with an accuracy of more than 93. 3$\%$.
no code implementations • 25 May 2022 • Eduardo Pérez-Pellitero, Sibi Catley-Chandar, Richard Shaw, Aleš Leonardis, Radu Timofte, Zexin Zhang, Cen Liu, Yunbo Peng, Yue Lin, Gaocheng Yu, Jin Zhang, Zhe Ma, Hongbin Wang, Xiangyu Chen, Xintao Wang, Haiwei Wu, Lin Liu, Chao Dong, Jiantao Zhou, Qingsen Yan, Song Zhang, Weiye Chen, Yuhang Liu, Zhen Zhang, Yanning Zhang, Javen Qinfeng Shi, Dong Gong, Dan Zhu, Mengdi Sun, Guannan Chen, Yang Hu, Haowei Li, Baozhu Zou, Zhen Liu, Wenjie Lin, Ting Jiang, Chengzhi Jiang, Xinpeng Li, Mingyan Han, Haoqiang Fan, Jian Sun, Shuaicheng Liu, Juan Marín-Vega, Michael Sloth, Peter Schneider-Kamp, Richard Röttger, Chunyang Li, Long Bao, Gang He, Ziyao Xu, Li Xu, Gen Zhan, Ming Sun, Xing Wen, Junlin Li, Shuang Feng, Fei Lei, Rui Liu, Junxiang Ruan, Tianhong Dai, Wei Li, Zhan Lu, Hengyan Liu, Peian Huang, Guangyu Ren, Yonglin Luo, Chang Liu, Qiang Tu, Fangya Li, Ruipeng Gang, Chenghua Li, Jinjing Li, Sai Ma, Chenming Liu, Yizhen Cao, Steven Tel, Barthelemy Heyrman, Dominique Ginhac, Chul Lee, Gahyeon Kim, Seonghyun Park, An Gia Vien, Truong Thanh Nhat Mai, Howoon Yoon, Tu Vo, Alexander Holston, Sheir Zaheer, Chan Y. Park
The challenge is composed of two tracks with an emphasis on fidelity and complexity constraints: In Track 1, participants are asked to optimize objective fidelity scores while imposing a low-complexity constraint (i. e. solutions can not exceed a given number of operations).
1 code implementation • 17 Aug 2021 • Tianhong Dai, Hengyan Liu, Kai Arulkumaran, Guangyu Ren, Anil Anthony Bharath
We evaluate DTGSH on five challenging robotic manipulation tasks in simulated robot environments, where we show that our method can learn more quickly and reach higher performance than other state-of-the-art approaches on all tasks.
1 code implementation • 3 Aug 2021 • Tianhong Dai, Wei Li, Xilei Cao, Jianzhuang Liu, Xu Jia, Ales Leonardis, Youliang Yan, Shanxin Yuan
The frequency-guided upsampling module reconstructs details from multiple frequency-specific components with rich details.
no code implementations • 7 Jun 2021 • Guangyu Ren, Yanchu Xie, Tianhong Dai, Tania Stathaki
We further introduce a mask-guided refinement module(MGRM) to complement the high-level semantic features and reduce the irrelevant features from multi-scale fusion, leading to an overall refinement of detection.
1 code implementation • 26 Nov 2020 • Tianhong Dai, Hengyan Liu, Anil Anthony Bharath
The trajectory selection module is shown to prevent the agent learning undesirable hindsight experiences.
no code implementations • 30 Apr 2020 • Guangyu Ren, Tianhong Dai, Panagiotis Barmpoutis, Tania Stathaki
Salient object detection has achieved great improvement by using the Fully Convolution Network (FCN).
no code implementations • 18 Dec 2019 • Tianrui Liu, Wenhan Luo, Lin Ma, Jun-Jie Huang, Tania Stathaki, Tianhong Dai
Ablation studies have validated the effectiveness of both the proposed gated multi-layer feature extraction sub-network and the deformable occlusion handling sub-network.
no code implementations • 18 Dec 2019 • Tianhong Dai, Kai Arulkumaran, Tamara Gerbert, Samyakh Tukra, Feryal Behbahani, Anil Anthony Bharath
Furthermore, even with an improved saliency method introduced in this work, we show that qualitative studies may not always correspond with quantitative measures, necessitating the combination of inspection tools in order to provide sufficient insights into the behaviour of trained agents.
1 code implementation • NeurIPS 2019 • Yali Du, Lei Han, Meng Fang, Ji Liu, Tianhong Dai, DaCheng Tao
A great challenge in cooperative decentralized multi-agent reinforcement learning (MARL) is generating diversified behaviors for each individual agent when receiving only a team reward.
Multi-agent Reinforcement Learning reinforcement-learning +3
no code implementations • 25 Oct 2019 • Tianrui Liu, Jun-Jie Huang, Tianhong Dai, Guangyu Ren, Tania Stathaki
In this paper, we propose a gated multi-layer convolutional feature extraction method which can adaptively generate discriminative features for candidate pedestrian regions.