Search Results for author: Xingyu Liu

Found 28 papers, 15 papers with code

Mesure de similarité textuelle pour l’évaluation automatique de copies d’étudiants (Textual similarity measurement for automatic evaluation of students’ answers)

no code implementations JEP/TALN/RECITAL 2021 Xiaoou Wang, Xingyu Liu, Yimei Yue

Cet article décrit la participation de l’équipe Nantalco à la tâche 2 du Défi Fouille de Textes 2021 (DEFT) : évaluation automatique de copies d’après une référence existante.

Sentence Embeddings

Classification automatique de questions spontanées vs. préparées dans des transcriptions de l’oral (Automatic Classification of Spontaneous vs)

no code implementations JEP/TALN/RECITAL 2022 Iris Eshkol-Taravella, Angèle Barbedette, Xingyu Liu, Valentin-Gabriel Soumah

Ce travail a pour objectif de développer un modèle linguistique pour classifier automatiquement des questions issues de transcriptions d’enregistrements provenant des corpus ESLO2 et ACSYNT en deux catégories “spontané” et “préparé”.


FinEval: A Chinese Financial Domain Knowledge Evaluation Benchmark for Large Language Models

1 code implementation19 Aug 2023 Liwen Zhang, Weige Cai, Zhaowei Liu, Zhi Yang, Wei Dai, Yujie Liao, Qianru Qin, Yifei Li, Xingyu Liu, Zhiqiang Liu, Zhoufan Zhu, Anbo Wu, Xin Guo, Yun Chen

Our work offers a more comprehensive financial knowledge evaluation benchmark, utilizing data of mock exams and covering a wide range of evaluated LLMs.


A Survey on Graph Classification and Link Prediction based on GNN

no code implementations3 Jul 2023 Xingyu Liu, Juan Chen, Quan Wen

Traditional convolutional neural networks are limited to handling Euclidean space data, overlooking the vast realm of real-life scenarios represented as graph data, including transportation networks, social networks, and reference networks.

Graph Classification Link Prediction +1

Knowledge Distillation for Efficient Sequences of Training Runs

no code implementations11 Mar 2023 Xingyu Liu, Alex Leonardi, Lu Yu, Chris Gilmer-Hill, Matthew Leavitt, Jonathan Frankle

We find that augmenting future runs with KD from previous runs dramatically reduces the time necessary to train these models, even taking into account the overhead of KD.

Knowledge Distillation

Parallel Attention Interaction Network for Few-Shot Skeleton-Based Action Recognition

no code implementations ICCV 2023 Xingyu Liu, Sanping Zhou, Le Wang, Gang Hua

Learning discriminative features from very few labeled samples to identify novel classes has received increasing attention in skeleton-based action recognition.

Action Recognition Skeleton Based Action Recognition

HERD: Continuous Human-to-Robot Evolution for Learning from Human Demonstration

no code implementations8 Dec 2022 Xingyu Liu, Deepak Pathak, Kris M. Kitani

The ability to learn from human demonstration endows robots with the ability to automate various tasks.

CATRE: Iterative Point Clouds Alignment for Category-level Object Pose Refinement

1 code implementation17 Jul 2022 Xingyu Liu, Gu Wang, Yi Li, Xiangyang Ji

While category-level 9DoF object pose estimation has emerged recently, previous correspondence-based or direct regression methods are both limited in accuracy due to the huge intra-category variances in object shape and color, etc.

Pose Estimation

Learning from Temporal Spatial Cubism for Cross-Dataset Skeleton-based Action Recognition

1 code implementation17 Jul 2022 Yansong Tang, Xingyu Liu, Xumin Yu, Danyang Zhang, Jiwen Lu, Jie zhou

Different from the conventional adversarial learning-based approaches for UDA, we utilize a self-supervision scheme to reduce the domain shift between two skeleton-based action datasets.

Action Recognition Self-Supervised Learning +2

Tripartite: Tackle Noisy Labels by a More Precise Partition

no code implementations19 Feb 2022 Xuefeng Liang, Longshan Yao, Xingyu Liu, Ying Zhou

Instead, we propose a Tripartite solution to partition training data more precisely into three subsets: hard, noisy, and clean.

Self-Supervised Learning

REvolveR: Continuous Evolutionary Models for Robot-to-robot Policy Transfer

1 code implementation10 Feb 2022 Xingyu Liu, Deepak Pathak, Kris M. Kitani

We interpolate between the source robot and the target robot by finding a continuous evolutionary change of robot parameters.

Imitation Learning

V-MAO: Generative Modeling for Multi-Arm Manipulation of Articulated Objects

no code implementations7 Nov 2021 Xingyu Liu, Kris M. Kitani

Manipulating articulated objects requires multiple robot arms in general.

Sequential Voting with Relational Box Fields for Active Object Detection

1 code implementation CVPR 2022 Qichen Fu, Xingyu Liu, Kris M. Kitani

While our voting function is able to improve the bounding box of the active object, one round of voting is typically not enough to accurately localize the active object.

Active Object Detection Imitation Learning +3

Ego4D: Around the World in 3,000 Hours of Egocentric Video

3 code implementations CVPR 2022 Kristen Grauman, Andrew Westbury, Eugene Byrne, Zachary Chavis, Antonino Furnari, Rohit Girdhar, Jackson Hamburger, Hao Jiang, Miao Liu, Xingyu Liu, Miguel Martin, Tushar Nagarajan, Ilija Radosavovic, Santhosh Kumar Ramakrishnan, Fiona Ryan, Jayant Sharma, Michael Wray, Mengmeng Xu, Eric Zhongcong Xu, Chen Zhao, Siddhant Bansal, Dhruv Batra, Vincent Cartillier, Sean Crane, Tien Do, Morrie Doulaty, Akshay Erapalli, Christoph Feichtenhofer, Adriano Fragomeni, Qichen Fu, Abrham Gebreselasie, Cristina Gonzalez, James Hillis, Xuhua Huang, Yifei HUANG, Wenqi Jia, Weslie Khoo, Jachym Kolar, Satwik Kottur, Anurag Kumar, Federico Landini, Chao Li, Yanghao Li, Zhenqiang Li, Karttikeya Mangalam, Raghava Modhugu, Jonathan Munro, Tullie Murrell, Takumi Nishiyasu, Will Price, Paola Ruiz Puentes, Merey Ramazanova, Leda Sari, Kiran Somasundaram, Audrey Southerland, Yusuke Sugano, Ruijie Tao, Minh Vo, Yuchen Wang, Xindi Wu, Takuma Yagi, Ziwei Zhao, Yunyi Zhu, Pablo Arbelaez, David Crandall, Dima Damen, Giovanni Maria Farinella, Christian Fuegen, Bernard Ghanem, Vamsi Krishna Ithapu, C. V. Jawahar, Hanbyul Joo, Kris Kitani, Haizhou Li, Richard Newcombe, Aude Oliva, Hyun Soo Park, James M. Rehg, Yoichi Sato, Jianbo Shi, Mike Zheng Shou, Antonio Torralba, Lorenzo Torresani, Mingfei Yan, Jitendra Malik

We introduce Ego4D, a massive-scale egocentric video dataset and benchmark suite.

De-identification Ethics

KDFNet: Learning Keypoint Distance Field for 6D Object Pose Estimation

no code implementations21 Sep 2021 Xingyu Liu, Shun Iwase, Kris M. Kitani

To address this problem, we propose a novel continuous representation called Keypoint Distance Field (KDF) for projected 2D keypoint locations.

6D Pose Estimation using RGB

RePOSE: Fast 6D Object Pose Refinement via Deep Texture Rendering

1 code implementation ICCV 2021 Shun Iwase, Xingyu Liu, Rawal Khirodkar, Rio Yokota, Kris M. Kitani

Furthermore, we utilize differentiable Levenberg-Marquardt (LM) optimization to refine a pose fast and accurately by minimizing the feature-metric error between the input and rendered image representations without the need of zooming in.

6D Pose Estimation 6D Pose Estimation using RGB

KeyPose: Multi-View 3D Labeling and Keypoint Estimation for Transparent Objects

2 code implementations CVPR 2020 Xingyu Liu, Rico Jonschkowski, Anelia Angelova, Kurt Konolige

We address two problems: first, we establish an easy method for capturing and labeling 3D keypoints on desktop objects with an RGB camera; and second, we develop a deep neural network, called $KeyPose$, that learns to accurately predict object poses using 3D keypoints, from stereo input, and works even for transparent objects.

3D Pose Estimation Keypoint Estimation +1

Learning Video Representations from Correspondence Proposals

2 code implementations CVPR 2019 Xingyu Liu, Joon-Young Lee, Hailin Jin

In particular, it can effectively learn representations for videos by mixing appearance and long-range motion with an RGB-only input.

Action Recognition In Videos

FlowNet3D: Learning Scene Flow in 3D Point Clouds

10 code implementations CVPR 2019 Xingyu Liu, Charles R. Qi, Leonidas J. Guibas

In this work, we propose a novel deep neural network named $FlowNet3D$ that learns scene flow from point clouds in an end-to-end fashion.

Motion Segmentation

Efficient Sparse-Winograd Convolutional Neural Networks

1 code implementation ICLR 2018 Xingyu Liu, Jeff Pool, Song Han, William J. Dally

First, we move the ReLU operation into the Winograd domain to increase the sparsity of the transformed activations.

Network Pruning

Exploring the Regularity of Sparse Structure in Convolutional Neural Networks

no code implementations24 May 2017 Huizi Mao, Song Han, Jeff Pool, Wenshuo Li, Xingyu Liu, Yu Wang, William J. Dally

Since memory reference is more than two orders of magnitude more expensive than arithmetic operations, the regularity of sparse structure leads to more efficient hardware design.

EIE: Efficient Inference Engine on Compressed Deep Neural Network

4 code implementations4 Feb 2016 Song Han, Xingyu Liu, Huizi Mao, Jing Pu, Ardavan Pedram, Mark A. Horowitz, William J. Dally

EIE has a processing power of 102GOPS/s working directly on a compressed network, corresponding to 3TOPS/s on an uncompressed network, and processes FC layers of AlexNet at 1. 88x10^4 frames/sec with a power dissipation of only 600mW.

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