Search Results for author: Xiaolong Liu

Found 25 papers, 14 papers with code

Instruction-based Hypergraph Pretraining

no code implementations28 Mar 2024 Mingdai Yang, Zhiwei Liu, Liangwei Yang, Xiaolong Liu, Chen Wang, Hao Peng, Philip S. Yu

However, the gap between training objectives and the discrepancy between data distributions in pretraining and downstream tasks hinders the transfer of the pretrained knowledge.

Graph Learning Link Prediction +1

Augmentation-Free Dense Contrastive Knowledge Distillation for Efficient Semantic Segmentation

1 code implementation7 Dec 2023 Jiawei Fan, Chao Li, Xiaolong Liu, Meina Song, Anbang Yao

In order to address this problem, we present Augmentation-free Dense Contrastive Knowledge Distillation (Af-DCD), a new contrastive distillation learning paradigm to train compact and accurate deep neural networks for semantic segmentation applications.

Contrastive Learning Data Augmentation +6

Multi-view Graph Convolution for Participant Recommendation

no code implementations20 Nov 2023 Xiaolong Liu, Liangwei Yang, Chen Wang, Mingdai Yang, Zhiwei Liu, Philip S. Yu

Participant recommendation, a fundamental problem emerging together with GB, aims to find the participants for a launched group buying process with an initiator and a target item to increase the GB success rate.

Group-Aware Interest Disentangled Dual-Training for Personalized Recommendation

1 code implementation16 Nov 2023 Xiaolong Liu, Liangwei Yang, Zhiwei Liu, Xiaohan Li, Mingdai Yang, Chen Wang, Philip S. Yu

The users' group participation on social platforms reveals their interests and can be utilized as side information to mitigate the data sparsity and cold-start problem in recommender systems.

Informativeness Recommendation Systems

Knowledge Graph Context-Enhanced Diversified Recommendation

1 code implementation20 Oct 2023 Xiaolong Liu, Liangwei Yang, Zhiwei Liu, Mingdai Yang, Chen Wang, Hao Peng, Philip S. Yu

Collectively, our contributions signify a substantial stride towards augmenting the panorama of recommendation diversity within the realm of KG-informed RecSys paradigms.

Knowledge Graphs Recommendation Systems

Collaborative Semantic Alignment in Recommendation Systems

no code implementations13 Oct 2023 Chen Wang, Liangwei Yang, Zhiwei Liu, Xiaolong Liu, Mingdai Yang, Yueqing Liang, Philip S. Yu

However, PLMs often overlook the vital collaborative filtering signals, leading to challenges in merging collaborative and semantic representation spaces and fine-tuning semantic representations for better alignment with warm-start conditions.

Collaborative Filtering Language Modelling +1

Graph-based Alignment and Uniformity for Recommendation

1 code implementation18 Aug 2023 Liangwei Yang, Zhiwei Liu, Chen Wang, Mingdai Yang, Xiaolong Liu, Jing Ma, Philip S. Yu

To address this issue, we propose a novel approach, graph-based alignment and uniformity (GraphAU), that explicitly considers high-order connectivities in the user-item bipartite graph.

Collaborative Filtering Recommendation Systems +1

Dimension Independent Mixup for Hard Negative Sample in Collaborative Filtering

1 code implementation28 Jun 2023 Xi Wu, Liangwei Yang, Jibing Gong, Chao Zhou, Tianyu Lin, Xiaolong Liu, Philip S. Yu

To address this limitation, we propose Dimension Independent Mixup for Hard Negative Sampling (DINS), which is the first Area-wise sampling method for training CF-based models.

Collaborative Filtering

NORM: Knowledge Distillation via N-to-One Representation Matching

1 code implementation23 May 2023 Xiaolong Liu, Lujun Li, Chao Li, Anbang Yao

By sequentially splitting the expanded student representation into N non-overlapping feature segments having the same number of feature channels as the teacher's, they can be readily forced to approximate the intact teacher representation simultaneously, formulating a novel many-to-one representation matching mechanism conditioned on a single teacher-student layer pair.

Knowledge Distillation

SOOD: Towards Semi-Supervised Oriented Object Detection

1 code implementation CVPR 2023 Wei Hua, Dingkang Liang, Jingyu Li, Xiaolong Liu, Zhikang Zou, Xiaoqing Ye, Xiang Bai

Semi-Supervised Object Detection (SSOD), aiming to explore unlabeled data for boosting object detectors, has become an active task in recent years.

Object object-detection +4

Multi-modal Expression Recognition with Ensemble Method

no code implementations17 Mar 2023 Chuanhe Liu, Xinjie Zhang, Xiaolong Liu, Tenggan Zhang, Liyu Meng, Yuchen Liu, Yuanyuan Deng, Wenqiang Jiang

This paper presents our submission to the Expression Classification Challenge of the fifth Affective Behavior Analysis in-the-wild (ABAW) Competition.

DGRec: Graph Neural Network for Recommendation with Diversified Embedding Generation

1 code implementation18 Nov 2022 Liangwei Yang, Shengjie Wang, Yunzhe Tao, Jiankai Sun, Xiaolong Liu, Philip S. Yu, Taiqing Wang

Graph Neural Network (GNN) based recommender systems have been attracting more and more attention in recent years due to their excellent performance in accuracy.

Recommendation Systems

Ranking-based Group Identification via Factorized Attention on Social Tripartite Graph

1 code implementation2 Nov 2022 Mingdai Yang, Zhiwei Liu, Liangwei Yang, Xiaolong Liu, Chen Wang, Hao Peng, Philip S. Yu

PA layers efficiently learn the relatedness of non-neighbor nodes to improve the information propagation to users.

Multi-Task Learning Framework for Emotion Recognition in-the-wild

1 code implementation19 Jul 2022 Tenggan Zhang, Chuanhe Liu, Xiaolong Liu, Yuchen Liu, Liyu Meng, Lei Sun, Wenqiang Jiang, Fengyuan Zhang, Jinming Zhao, Qin Jin

This paper presents our system for the Multi-Task Learning (MTL) Challenge in the 4th Affective Behavior Analysis in-the-wild (ABAW) competition.

Emotion Recognition Multi-Task Learning +1

An Empirical Study of End-to-End Temporal Action Detection

1 code implementation CVPR 2022 Xiaolong Liu, Song Bai, Xiang Bai

Rather than end-to-end learning, most existing methods adopt a head-only learning paradigm, where the video encoder is pre-trained for action classification, and only the detection head upon the encoder is optimized for TAD.

Action Classification Action Detection +2

Multi-modal Emotion Estimation for in-the-wild Videos

no code implementations24 Mar 2022 Liyu Meng, Yuchen Liu, Xiaolong Liu, Zhaopei Huang, Yuan Cheng, Meng Wang, Chuanhe Liu, Qin Jin

In this paper, we briefly introduce our submission to the Valence-Arousal Estimation Challenge of the 3rd Affective Behavior Analysis in-the-wild (ABAW) competition.

Arousal Estimation

End-to-end Temporal Action Detection with Transformer

1 code implementation18 Jun 2021 Xiaolong Liu, Qimeng Wang, Yao Hu, Xu Tang, Shiwei Zhang, Song Bai, Xiang Bai

Temporal action detection (TAD) aims to determine the semantic label and the temporal interval of every action instance in an untrimmed video.

Action Detection Temporal Action Localization +1

Localization and Control of Magnetic Suture Needles in Cluttered Surgical Site with Blood and Tissue

no code implementations20 May 2021 Will Pryor, Yotam Barnoy, Suraj Raval, Xiaolong Liu, Lamar Mair, Daniel Lerner, Onder Erin, Gregory D. Hager, Yancy Diaz-Mercado, Axel Krieger

Our localization method combines neural network-based segmentation and classical techniques, and we are able to consistently locate our needle with 0. 73 mm RMS error in clean environments and 2. 72 mm RMS error in challenging environments with blood and occlusion.

Position Visual Localization

CASNet: Common Attribute Support Network for image instance and panoptic segmentation

no code implementations17 Jul 2020 Xiaolong Liu, Yuqing Hou, Anbang Yao, Yurong Chen, Keqiang Li

Given the insight that pixels belonging to one instance have one or more common attributes of current instance, we bring up an one-stage instance segmentation network named Common Attribute Support Network (CASNet), which realizes instance segmentation by predicting and clustering common attributes.

Attribute Clustering +5

Recent progress in semantic image segmentation

no code implementations20 Sep 2018 Xiaolong Liu, Zhidong Deng, Yuhan Yang

In this paper, we divide semantic image segmentation methods into two categories: traditional and recent DNN method.

Image Segmentation Segmentation +1

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