Search Results for author: Tianhong Dai

Found 14 papers, 5 papers with code

GPT-Prompt Controlled Diffusion for Weakly-Supervised Semantic Segmentation

no code implementations15 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.

Image Augmentation Segmentation +2

Top-K Pooling with Patch Contrastive Learning for Weakly-Supervised Semantic Segmentation

no code implementations15 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

Learning to Solve Tasks with Exploring Prior Behaviours

1 code implementation6 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.

SJ-HD^2R: Selective Joint High Dynamic Range and Denoising Imaging for Dynamic Scenes

no code implementations20 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$\%$.

Denoising

NTIRE 2022 Challenge on High Dynamic Range Imaging: Methods and Results

no code implementations25 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).

Image Restoration Vocal Bursts Intensity Prediction

Diversity-based Trajectory and Goal Selection with Hindsight Experience Replay

1 code implementation17 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.

Point Processes

Wavelet-Based Network For High Dynamic Range Imaging

1 code implementation3 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.

Optical Flow Estimation Vocal Bursts Intensity Prediction

Progressive Multi-scale Fusion Network for RGB-D Salient Object Detection

no code implementations7 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.

object-detection RGB-D Salient Object Detection +1

Episodic Self-Imitation Learning with Hindsight

1 code implementation26 Nov 2020 Tianhong Dai, Hengyan Liu, Anil Anthony Bharath

The trajectory selection module is shown to prevent the agent learning undesirable hindsight experiences.

Continuous Control Imitation Learning

Coupled Network for Robust Pedestrian Detection with Gated Multi-Layer Feature Extraction and Deformable Occlusion Handling

no code implementations18 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.

Occlusion Handling Pedestrian Detection

Analysing Deep Reinforcement Learning Agents Trained with Domain Randomisation

no code implementations18 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.

reinforcement-learning Reinforcement Learning (RL)

LIIR: Learning Individual Intrinsic Reward in Multi-Agent Reinforcement Learning

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

Gated Multi-layer Convolutional Feature Extraction Network for Robust Pedestrian Detection

no code implementations25 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.

Pedestrian Detection

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