Search Results for author: Yandong Li

Found 28 papers, 12 papers with code

DreamInpainter: Text-Guided Subject-Driven Image Inpainting with Diffusion Models

no code implementations5 Dec 2023 Shaoan Xie, Yang Zhao, Zhisheng Xiao, Kelvin C. K. Chan, Yandong Li, Yanwu Xu, Kun Zhang, Tingbo Hou

Our extensive experiments demonstrate the superior performance of our method in terms of visual quality, identity preservation, and text control, showcasing its effectiveness in the context of text-guided subject-driven image inpainting.

Image Inpainting

Identity Encoder for Personalized Diffusion

no code implementations14 Apr 2023 Yu-Chuan Su, Kelvin C. K. Chan, Yandong Li, Yang Zhao, Han Zhang, Boqing Gong, Huisheng Wang, Xuhui Jia

Our approach greatly reduces the overhead for personalized image generation and is more applicable in many potential applications.

Image Enhancement Image Generation

What's in a Name? Beyond Class Indices for Image Recognition

no code implementations5 Apr 2023 Kai Han, Yandong Li, Sagar Vaze, Jie Li, Xuhui Jia

In this paper, we reconsider the recognition problem and task a vision-language model to assign class names to images given only a large and essentially unconstrained vocabulary of categories as prior information.

Language Modelling Object Recognition

Learning to Adapt to Online Streams with Distribution Shifts

no code implementations2 Mar 2023 Chenyan Wu, Yimu Pan, Yandong Li, James Z. Wang

Test-time adaptation (TTA) is a technique used to reduce distribution gaps between the training and testing sets by leveraging unlabeled test data during inference.

Benchmarking Meta-Learning +3

Train-Once-for-All Personalization

no code implementations CVPR 2023 Hong-You Chen, Yandong Li, Yin Cui, Mingda Zhang, Wei-Lun Chao, Li Zhang

We study the problem of how to train a "personalization-friendly" model such that given only the task descriptions, the model can be adapted to different end-users' needs, e. g., for accurately classifying different subsets of objects.

MUG: Multi-human Graph Network for 3D Mesh Reconstruction from 2D Pose

no code implementations25 May 2022 Chenyan Wu, Yandong Li, Xianfeng Tang, James Wang

Our method works like the following: First, to model the multi-human environment, it processes multi-human 2D poses and builds a novel heterogeneous graph, where nodes from different people and within one person are connected to capture inter-human interactions and draw the body geometry (i. e., skeleton and mesh structure).

3D Multi-Person Human Pose Estimation 3D Multi-Person Pose Estimation

Dir-MUSIC Algorithm for DOA Estimation of Partial Discharge Based on Signal Strength represented by Antenna Gain Array Manifold

no code implementations19 Apr 2022 Wencong Xu, Yandong Li, Bingshu Chen, Yue Hu, Jianxu Li, Zijing Zeng

The experimental results show that the PD direction-finding error is 3. 39{\deg}, which can meet the need for Partial discharge DOA estimation using inspection robots in substations.

Rethinking Deep Face Restoration

no code implementations CVPR 2022 Yang Zhao, Yu-Chuan Su, Chun-Te Chu, Yandong Li, Marius Renn, Yukun Zhu, Changyou Chen, Xuhui Jia

While existing approaches for face restoration make significant progress in generating high-quality faces, they often fail to preserve facial features and cannot authentically reconstruct the faces.

Face Generation Face Reconstruction

On Model Calibration for Long-Tailed Object Detection and Instance Segmentation

1 code implementation NeurIPS 2021 Tai-Yu Pan, Cheng Zhang, Yandong Li, Hexiang Hu, Dong Xuan, Soravit Changpinyo, Boqing Gong, Wei-Lun Chao

We propose NorCal, Normalized Calibration for long-tailed object detection and instance segmentation, a simple and straightforward recipe that reweighs the predicted scores of each class by its training sample size.

Instance Segmentation Long-tailed Object Detection +4

MoViNets: Mobile Video Networks for Efficient Video Recognition

3 code implementations CVPR 2021 Dan Kondratyuk, Liangzhe Yuan, Yandong Li, Li Zhang, Mingxing Tan, Matthew Brown, Boqing Gong

We present Mobile Video Networks (MoViNets), a family of computation and memory efficient video networks that can operate on streaming video for online inference.

Action Classification Action Recognition +3

MosaicOS: A Simple and Effective Use of Object-Centric Images for Long-Tailed Object Detection

1 code implementation ICCV 2021 Cheng Zhang, Tai-Yu Pan, Yandong Li, Hexiang Hu, Dong Xuan, Soravit Changpinyo, Boqing Gong, Wei-Lun Chao

Many objects do not appear frequently enough in complex scenes (e. g., certain handbags in living rooms) for training an accurate object detector, but are often found frequently by themselves (e. g., in product images).

Imputation Instance Segmentation +4

Ranking Neural Checkpoints

1 code implementation CVPR 2021 Yandong Li, Xuhui Jia, Ruoxin Sang, Yukun Zhu, Bradley Green, Liqiang Wang, Boqing Gong

This paper is concerned with ranking many pre-trained deep neural networks (DNNs), called checkpoints, for the transfer learning to a downstream task.

Transfer Learning

Neural Networks Are More Productive Teachers Than Human Raters: Active Mixup for Data-Efficient Knowledge Distillation from a Blackbox Model

1 code implementation CVPR 2020 Dongdong Wang, Yandong Li, Liqiang Wang, Boqing Gong

The other is that the number of images used for the knowledge distillation should be small; otherwise, it violates our expectation of reducing the dependence on large-scale datasets.

Active Learning Knowledge Distillation

BachGAN: High-Resolution Image Synthesis from Salient Object Layout

1 code implementation CVPR 2020 Yandong Li, Yu Cheng, Zhe Gan, Licheng Yu, Liqiang Wang, Jingjing Liu

We propose a new task towards more practical application for image generation - high-quality image synthesis from salient object layout.

Image Generation Retrieval +1

Attacking Lifelong Learning Models with Gradient Reversion

no code implementations ICLR 2020 Yunhui Guo, Mingrui Liu, Yandong Li, Liqiang Wang, Tianbao Yang, Tajana Rosing

We evaluate the effectiveness of traditional attack methods such as FGSM and PGD. The results show that A-GEM still possesses strong continual learning ability in the presence of adversarial examples in the memory and simple defense techniques such as label smoothing can further alleviate the adversarial effects.

Continual Learning

AdaFilter: Adaptive Filter Fine-tuning for Deep Transfer Learning

no code implementations21 Nov 2019 Yunhui Guo, Yandong Li, Liqiang Wang, Tajana Rosing

Fine-tuning is a popular transfer learning technique for deep neural networks where a few rounds of training are applied to the parameters of a pre-trained model to adapt them to a new task.

General Classification Image Classification +1

Transferring Robustness for Graph Neural Network Against Poisoning Attacks

1 code implementation20 Aug 2019 Xianfeng Tang, Yandong Li, Yiwei Sun, Huaxiu Yao, Prasenjit Mitra, Suhang Wang

To optimize PA-GNN for a poisoned graph, we design a meta-optimization algorithm that trains PA-GNN to penalize perturbations using clean graphs and their adversarial counterparts, and transfers such ability to improve the robustness of PA-GNN on the poisoned graph.

Node Classification Transfer Learning

NATTACK: Learning the Distributions of Adversarial Examples for an Improved Black-Box Attack on Deep Neural Networks

1 code implementation1 May 2019 Yandong Li, Lijun Li, Liqiang Wang, Tong Zhang, Boqing Gong

Powerful adversarial attack methods are vital for understanding how to construct robust deep neural networks (DNNs) and for thoroughly testing defense techniques.

Adversarial Attack Test

Joint Modeling of Dense and Incomplete Trajectories for Citywide Traffic Volume Inference

no code implementations25 Feb 2019 Xianfeng Tang, Boqing Gong, Yanwei Yu, Huaxiu Yao, Yandong Li, Haiyong Xie, Xiaoyu Wang

In this paper, we propose a novel framework for the citywide traffic volume inference using both dense GPS trajectories and incomplete trajectories captured by camera surveillance systems.

Graph Embedding

Depthwise Convolution is All You Need for Learning Multiple Visual Domains

1 code implementation3 Feb 2019 Yunhui Guo, Yandong Li, Rogerio Feris, Liqiang Wang, Tajana Rosing

A model aware of the relationships between different domains can also be trained to work on new domains with less resources.

Continual Learning

StNet: Local and Global Spatial-Temporal Modeling for Action Recognition

7 code implementations5 Nov 2018 Dongliang He, Zhichao Zhou, Chuang Gan, Fu Li, Xiao Liu, Yandong Li, Li-Min Wang, Shilei Wen

In this paper, in contrast to the existing CNN+RNN or pure 3D convolution based approaches, we explore a novel spatial temporal network (StNet) architecture for both local and global spatial-temporal modeling in videos.

Action Recognition Temporal Action Localization

VQS: Linking Segmentations to Questions and Answers for Supervised Attention in VQA and Question-Focused Semantic Segmentation

1 code implementation ICCV 2017 Chuang Gan, Yandong Li, Haoxiang Li, Chen Sun, Boqing Gong

Many seemingly distant annotations (e. g., semantic segmentation and visual question answering (VQA)) are inherently connected in that they reveal different levels and perspectives of human understandings about the same visual scenes --- and even the same set of images (e. g., of COCO).

Language Modelling Multiple-choice +5

Revisiting the Effectiveness of Off-the-shelf Temporal Modeling Approaches for Large-scale Video Classification

no code implementations12 Aug 2017 Yunlong Bian, Chuang Gan, Xiao Liu, Fu Li, Xiang Long, Yandong Li, Heng Qi, Jie zhou, Shilei Wen, Yuanqing Lin

Experiment results on the challenging Kinetics dataset demonstrate that our proposed temporal modeling approaches can significantly improve existing approaches in the large-scale video recognition tasks.

Action Classification General Classification +2

Temporal Modeling Approaches for Large-scale Youtube-8M Video Understanding

1 code implementation14 Jul 2017 Fu Li, Chuang Gan, Xiao Liu, Yunlong Bian, Xiang Long, Yandong Li, Zhichao Li, Jie zhou, Shilei Wen

This paper describes our solution for the video recognition task of the Google Cloud and YouTube-8M Video Understanding Challenge that ranked the 3rd place.

Test Video Recognition +1

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