Search Results for author: Kun Wu

Found 21 papers, 9 papers with code

Unsupervised Domain Adaptation for Brain Vessel Segmentation through Transwarp Contrastive Learning

1 code implementation23 Feb 2024 Fengming Lin, Yan Xia, Michael MacRaild, Yash Deo, Haoran Dou, Qiongyao Liu, Kun Wu, Nishant Ravikumar, Alejandro F. Frangi

Unsupervised domain adaptation (UDA) aims to align the labelled source distribution with the unlabelled target distribution to obtain domain-invariant predictive models.

Contrastive Learning Unsupervised Domain Adaptation

SoMeLVLM: A Large Vision Language Model for Social Media Processing

no code implementations20 Feb 2024 Xinnong Zhang, Haoyu Kuang, Xinyi Mou, Hanjia Lyu, Kun Wu, Siming Chen, Jiebo Luo, Xuanjing Huang, Zhongyu Wei

The powerful Large Vision Language Models make it possible to handle a variety of tasks simultaneously, but even with carefully designed prompting methods, the general domain models often fall short in aligning with the unique speaking style and context of social media tasks.

Language Modelling

SWBT: Similarity Weighted Behavior Transformer with the Imperfect Demonstration for Robotic Manipulation

no code implementations17 Jan 2024 Kun Wu, Ning Liu, Zhen Zhao, Di Qiu, Jinming Li, Zhengping Che, Zhiyuan Xu, Qinru Qiu, Jian Tang

Imitation learning (IL), aiming to learn optimal control policies from expert demonstrations, has been an effective method for robot manipulation tasks.

Imitation Learning Robot Manipulation

An Efficient Generalizable Framework for Visuomotor Policies via Control-aware Augmentation and Privilege-guided Distillation

no code implementations17 Jan 2024 Yinuo Zhao, Kun Wu, Tianjiao Yi, Zhiyuan Xu, Xiaozhu Ju, Zhengping Che, Qinru Qiu, Chi Harold Liu, Jian Tang

Visuomotor policies, which learn control mechanisms directly from high-dimensional visual observations, confront challenges in adapting to new environments with intricate visual variations.

Data Augmentation Reinforcement Learning (RL) +1

Multi-Clue Reasoning with Memory Augmentation for Knowledge-based Visual Question Answering

no code implementations20 Dec 2023 Chengxiang Yin, Zhengping Che, Kun Wu, Zhiyuan Xu, Jian Tang

Visual Question Answering (VQA) has emerged as one of the most challenging tasks in artificial intelligence due to its multi-modal nature.

Question Answering Visual Question Answering

Cross-Modal Reasoning with Event Correlation for Video Question Answering

no code implementations20 Dec 2023 Chengxiang Yin, Zhengping Che, Kun Wu, Zhiyuan Xu, Qinru Qiu, Jian Tang

Video Question Answering (VideoQA) is a very attractive and challenging research direction aiming to understand complex semantics of heterogeneous data from two domains, i. e., the spatio-temporal video content and the word sequence in question.

Question Answering Video Question Answering

Hector: An Efficient Programming and Compilation Framework for Implementing Relational Graph Neural Networks in GPU Architectures

no code implementations16 Jan 2023 Kun Wu, Mert Hidayetoğlu, Xiang Song, Sitao Huang, Da Zheng, Israt Nisa, Wen-mei Hwu

Relational graph neural networks (RGNNs) are graph neural networks with dedicated structures for modeling the different types of nodes and edges in heterogeneous graphs.

8k C++ code +1

Continual Few-Shot Learning with Adversarial Class Storage

no code implementations10 Jul 2022 Kun Wu, Chengxiang Yin, Jian Tang, Zhiyuan Xu, Yanzhi Wang, Dejun Yang

In this paper, we define a new problem called continual few-shot learning, in which tasks arrive sequentially and each task is associated with a few training samples.

continual few-shot learning Few-Shot Learning +1

Lesion-Aware Contrastive Representation Learning for Histopathology Whole Slide Images Analysis

1 code implementation27 Jun 2022 Jun Li, Yushan Zheng, Kun Wu, Jun Shi, Fengying Xie, Zhiguo Jiang

In this paper, we proposed a novel contrastive representation learning framework named Lesion-Aware Contrastive Learning (LACL) for histopathology whole slide image analysis.

Contrastive Learning Representation Learning +1

Faster and Better Grammar-based Text-to-SQL Parsing via Clause-level Parallel Decoding and Alignment Loss

no code implementations26 Apr 2022 Kun Wu, Lijie Wang, Zhenghua Li, Xinyan Xiao

Grammar-based parsers have achieved high performance in the cross-domain text-to-SQL parsing task, but suffer from low decoding efficiency due to the much larger number of actions for grammar selection than that of tokens in SQL queries.

SQL Parsing Text-To-SQL

CADRE: A Cascade Deep Reinforcement Learning Framework for Vision-based Autonomous Urban Driving

1 code implementation17 Feb 2022 Yinuo Zhao, Kun Wu, Zhiyuan Xu, Zhengping Che, Qi Lu, Jian Tang, Chi Harold Liu

Vision-based autonomous urban driving in dense traffic is quite challenging due to the complicated urban environment and the dynamics of the driving behaviors.

reinforcement-learning Reinforcement Learning (RL)

Graph Neural Network Training with Data Tiering

no code implementations10 Nov 2021 Seung Won Min, Kun Wu, Mert Hidayetoğlu, JinJun Xiong, Xiang Song, Wen-mei Hwu

With our data tiering method, we additionally provide a new data placement and access strategy to further minimize the CPU-GPU communication overhead.

Fraud Detection

Human Pose Transfer with Augmented Disentangled Feature Consistency

no code implementations23 Jul 2021 Kun Wu, Chengxiang Yin, Zhengping Che, Bo Jiang, Jian Tang, Zheng Guan, Gangyi Ding

Deep generative models have made great progress in synthesizing images with arbitrary human poses and transferring poses of one person to others.

Data Augmentation Pose Transfer

Large Graph Convolutional Network Training with GPU-Oriented Data Communication Architecture

1 code implementation4 Mar 2021 Seung Won Min, Kun Wu, Sitao Huang, Mert Hidayetoğlu, JinJun Xiong, Eiman Ebrahimi, Deming Chen, Wen-mei Hwu

In this work, we propose a novel GPU-oriented data communication approach for GCN training, where GPU threads directly access sparse features in host memory through zero-copy accesses without much CPU help.

Recommendation Systems

PyTorch-Direct: Enabling GPU Centric Data Access for Very Large Graph Neural Network Training with Irregular Accesses

1 code implementation20 Jan 2021 Seung Won Min, Kun Wu, Sitao Huang, Mert Hidayetoğlu, JinJun Xiong, Eiman Ebrahimi, Deming Chen, Wen-mei Hwu

While this process accounts for a significant portion of the training time, we find existing GNN implementations using popular deep neural network (DNN) libraries such as PyTorch are limited to a CPU-centric approach for the entire data preparation step.

Hierarchical Graph Attention Network for Few-Shot Visual-Semantic Learning

no code implementations ICCV 2021 Chengxiang Yin, Kun Wu, Zhengping Che, Bo Jiang, Zhiyuan Xu, Jian Tang

Deep learning has made tremendous success in computer vision, natural language processing and even visual-semantic learning, which requires a huge amount of labeled training data.

Graph Attention Image Captioning +2

TEMPI: An Interposed MPI Library with a Canonical Representation of CUDA-aware Datatypes

1 code implementation28 Dec 2020 Carl Pearson, Kun Wu, I-Hsin Chung, JinJun Xiong, Wen-mei Hwu

MPI derived datatypes are an abstraction that simplifies handling of non-contiguous data in MPI applications.

Distributed, Parallel, and Cluster Computing

Knowledge Transfer in Multi-Task Deep Reinforcement Learning for Continuous Control

1 code implementation NeurIPS 2020 Zhiyuan Xu, Kun Wu, Zhengping Che, Jian Tang, Jieping Ye

While Deep Reinforcement Learning (DRL) has emerged as a promising approach to many complex tasks, it remains challenging to train a single DRL agent that is capable of undertaking multiple different continuous control tasks.

Continuous Control reinforcement-learning +2

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