Search Results for author: Mike Zheng Shou

Found 68 papers, 45 papers with code

Bridging Sensor Gaps via Single-Direction Tuning for Hyperspectral Image Classification

1 code implementation22 Sep 2023 Xizhe Xue, Haokui Zhang, Ying Li, Liuwei Wan, Zongwen Bai, Mike Zheng Shou

In this paper, aiming to solve this problem, we propose the single-direction tuning (SDT) strategy, which serves as a bridge, allowing us to leverage existing labeled HSI datasets even RGB datasets to enhance the performance on new HSI datasets with limited samples.

Hyperspectral Image Classification Representation Learning

Unsupervised Open-Vocabulary Object Localization in Videos

no code implementations ICCV 2023 Ke Fan, Zechen Bai, Tianjun Xiao, Dominik Zietlow, Max Horn, Zixu Zhao, Carl-Johann Simon-Gabriel, Mike Zheng Shou, Francesco Locatello, Bernt Schiele, Thomas Brox, Zheng Zhang, Yanwei Fu, Tong He

In this paper, we show that recent advances in video representation learning and pre-trained vision-language models allow for substantial improvements in self-supervised video object localization.

Object Localization Representation Learning

Spiking-LEAF: A Learnable Auditory front-end for Spiking Neural Networks

no code implementations18 Sep 2023 Zeyang Song, Jibin Wu, Malu Zhang, Mike Zheng Shou, Haizhou Li

Brain-inspired spiking neural networks (SNNs) have demonstrated great potential for temporal signal processing.

Keyword Spotting Speaker Identification

SCT: A Simple Baseline for Parameter-Efficient Fine-Tuning via Salient Channels

2 code implementations15 Sep 2023 Henry Hengyuan Zhao, Pichao Wang, Yuyang Zhao, Hao Luo, Fan Wang, Mike Zheng Shou

Recently, many parameter-efficient fine-tuning (PEFT) methods have been proposed, and their experiments demonstrate that tuning only 1% of extra parameters could surpass full fine-tuning in low-data resource scenarios.

Domain Generalization Few-Shot Learning

Dataset Condensation via Generative Model

no code implementations14 Sep 2023 David Junhao Zhang, Heng Wang, Chuhui Xue, Rui Yan, Wenqing Zhang, Song Bai, Mike Zheng Shou

Dataset condensation aims to condense a large dataset with a lot of training samples into a small set.

Dataset Condensation

ViT-Lens: Towards Omni-modal Representations

1 code implementation20 Aug 2023 Weixian Lei, Yixiao Ge, Jianfeng Zhang, Dylan Sun, Kun Yi, Ying Shan, Mike Zheng Shou

A well-trained lens with a ViT backbone has the potential to serve as one of these foundation models, supervising the learning of subsequent modalities.

 Ranked #1 on Zero-shot 3D classification on Objaverse LVIS (using extra training data)

3D Classification Question Answering +4

Recap: Detecting Deepfake Video with Unpredictable Tampered Traces via Recovering Faces and Mapping Recovered Faces

no code implementations19 Aug 2023 Juan Hu, Xin Liao, Difei Gao, Satoshi Tsutsui, Qian Wang, Zheng Qin, Mike Zheng Shou

In the recovering stage, the model focuses on randomly masking regions of interest (ROIs) and reconstructing real faces without unpredictable tampered traces, resulting in a relatively good recovery effect for real faces while a poor recovery effect for fake faces.

DeepFake Detection Face Swapping

Free-ATM: Exploring Unsupervised Learning on Diffusion-Generated Images with Free Attention Masks

no code implementations13 Aug 2023 David Junhao Zhang, Mutian Xu, Chuhui Xue, Wenqing Zhang, Xiaoguang Han, Song Bai, Mike Zheng Shou

Despite the rapid advancement of unsupervised learning in visual representation, it requires training on large-scale datasets that demand costly data collection, and pose additional challenges due to concerns regarding data privacy.

Contrastive Learning Image Classification +2

Revisiting Vision Transformer from the View of Path Ensemble

no code implementations ICCV 2023 Shuning Chang, Pichao Wang, Hao Luo, Fan Wang, Mike Zheng Shou

Therefore, we propose the path pruning and EnsembleScale skills for improvement, which cut out the underperforming paths and re-weight the ensemble components, respectively, to optimize the path combination and make the short paths focus on providing high-quality representation for subsequent paths.

DatasetDM: Synthesizing Data with Perception Annotations Using Diffusion Models

1 code implementation11 Aug 2023 Weijia Wu, Yuzhong Zhao, Hao Chen, YuChao Gu, Rui Zhao, Yefei He, Hong Zhou, Mike Zheng Shou, Chunhua Shen

To showcase the power of the proposed approach, we generate datasets with rich dense pixel-wise labels for a wide range of downstream tasks, including semantic segmentation, instance segmentation, and depth estimation.

Depth Estimation Domain Generalization +4

UniVTG: Towards Unified Video-Language Temporal Grounding

1 code implementation ICCV 2023 Kevin Qinghong Lin, Pengchuan Zhang, Joya Chen, Shraman Pramanick, Difei Gao, Alex Jinpeng Wang, Rui Yan, Mike Zheng Shou

Most methods in this direction develop taskspecific models that are trained with type-specific labels, such as moment retrieval (time interval) and highlight detection (worthiness curve), which limits their abilities to generalize to various VTG tasks and labels.

 Ranked #1 on Highlight Detection on QVHighlights (using extra training data)

Highlight Detection Moment Retrieval +2

BoxDiff: Text-to-Image Synthesis with Training-Free Box-Constrained Diffusion

2 code implementations ICCV 2023 Jinheng Xie, Yuexiang Li, Yawen Huang, Haozhe Liu, Wentian Zhang, Yefeng Zheng, Mike Zheng Shou

As such paired data is time-consuming and labor-intensive to acquire and restricted to a closed set, this potentially becomes the bottleneck for applications in an open world.


EgoVLPv2: Egocentric Video-Language Pre-training with Fusion in the Backbone

no code implementations ICCV 2023 Shraman Pramanick, Yale Song, Sayan Nag, Kevin Qinghong Lin, Hardik Shah, Mike Zheng Shou, Rama Chellappa, Pengchuan Zhang

Video-language pre-training (VLP) has become increasingly important due to its ability to generalize to various vision and language tasks.

GroundNLQ @ Ego4D Natural Language Queries Challenge 2023

1 code implementation27 Jun 2023 Zhijian Hou, Lei Ji, Difei Gao, Wanjun Zhong, Kun Yan, Chao Li, Wing-Kwong Chan, Chong-Wah Ngo, Nan Duan, Mike Zheng Shou

Motivated by this, we leverage a two-stage pre-training strategy to train egocentric feature extractors and the grounding model on video narrations, and further fine-tune the model on annotated data.

Natural Language Queries

TaCA: Upgrading Your Visual Foundation Model with Task-agnostic Compatible Adapter

no code implementations22 Jun 2023 Binjie Zhang, Yixiao Ge, Xuyuan Xu, Ying Shan, Mike Zheng Shou

In situations involving system upgrades that require updating the upstream foundation model, it becomes essential to re-train all downstream modules to adapt to the new foundation model, which is inflexible and inefficient.

Question Answering Retrieval +5

Too Large; Data Reduction for Vision-Language Pre-Training

2 code implementations ICCV 2023 Alex Jinpeng Wang, Kevin Qinghong Lin, David Junhao Zhang, Stan Weixian Lei, Mike Zheng Shou

Specifically, TL;DR can compress the mainstream VLP datasets at a high ratio, e. g., reduce well-cleaned CC3M dataset from 2. 82M to 0. 67M ($\sim$24\%) and noisy YFCC15M from 15M to 2. 5M ($\sim$16. 7\%).

VisorGPT: Learning Visual Prior via Generative Pre-Training

1 code implementation23 May 2023 Jinheng Xie, Kai Ye, Yudong Li, Yuexiang Li, Kevin Qinghong Lin, Yefeng Zheng, Linlin Shen, Mike Zheng Shou

Experimental results demonstrate that VisorGPT can effectively model the visual prior, which can be employed for many vision tasks, such as customizing accurate human pose for conditional image synthesis models like ControlNet.

Image Generation Language Modelling +1

A Large Cross-Modal Video Retrieval Dataset with Reading Comprehension

1 code implementation5 May 2023 Weijia Wu, Yuzhong Zhao, Zhuang Li, Jiahong Li, Hong Zhou, Mike Zheng Shou, Xiang Bai

Most existing cross-modal language-to-video retrieval (VR) research focuses on single-modal input from video, i. e., visual representation, while the text is omnipresent in human environments and frequently critical to understand video.

Reading Comprehension Retrieval +1

HOSNeRF: Dynamic Human-Object-Scene Neural Radiance Fields from a Single Video

no code implementations ICCV 2023 Jia-Wei Liu, Yan-Pei Cao, Tianyuan Yang, Eric Zhongcong Xu, Jussi Keppo, Ying Shan, XiaoHu Qie, Mike Zheng Shou

Our method enables pausing the video at any frame and rendering all scene details (dynamic humans, objects, and backgrounds) from arbitrary viewpoints.

Human-Object Interaction Detection

Open-World Weakly-Supervised Object Localization

1 code implementation17 Apr 2023 Jinheng Xie, Zhaochuan Luo, Yuexiang Li, Haozhe Liu, Linlin Shen, Mike Zheng Shou

To handle such data, we propose a novel paradigm of contrastive representation co-learning using both labeled and unlabeled data to generate a complete G-CAM (Generalized Class Activation Map) for object localization, without the requirement of bounding box annotation.

Representation Learning Weakly-Supervised Object Localization

ICDAR 2023 Video Text Reading Competition for Dense and Small Text

no code implementations10 Apr 2023 Weijia Wu, Yuzhong Zhao, Zhuang Li, Jiahong Li, Mike Zheng Shou, Umapada Pal, Dimosthenis Karatzas, Xiang Bai

In this competition report, we establish a video text reading benchmark, DSText, which focuses on dense and small text reading challenges in the video with various scenarios.

Text Detection Text Spotting

Attack is Good Augmentation: Towards Skeleton-Contrastive Representation Learning

no code implementations8 Apr 2023 Binqian Xu, Xiangbo Shu, Rui Yan, Guo-Sen Xie, Yixiao Ge, Mike Zheng Shou

In particular, we propose a novel Attack-Augmentation Mixing-Contrastive learning (A$^2$MC) to contrast hard positive features and hard negative features for learning more robust skeleton representations.

Action Recognition Contrastive Learning +4

SparseFormer: Sparse Visual Recognition via Limited Latent Tokens

1 code implementation7 Apr 2023 Ziteng Gao, Zhan Tong, LiMin Wang, Mike Zheng Shou

In this paper, we challenge this dense paradigm and present a new method, coined SparseFormer, to imitate human's sparse visual recognition in an end-to-end manner.

Sparse Representation-based Classification Video Classification

Making Vision Transformers Efficient from A Token Sparsification View

1 code implementation CVPR 2023 Shuning Chang, Pichao Wang, Ming Lin, Fan Wang, David Junhao Zhang, Rong Jin, Mike Zheng Shou

In this work, we propose a novel Semantic Token ViT (STViT), for efficient global and local vision transformers, which can also be revised to serve as backbone for downstream tasks.

Image Classification Instance Segmentation +4

Revisit Parameter-Efficient Transfer Learning: A Two-Stage Paradigm

no code implementations14 Mar 2023 Hengyuan Zhao, Hao Luo, Yuyang Zhao, Pichao Wang, Fan Wang, Mike Zheng Shou

In view of the practicality of PETL, previous works focus on tuning a small set of parameters for each downstream task in an end-to-end manner while rarely considering the task distribution shift issue between the pre-training task and the downstream task.

Transfer Learning Vocal Bursts Valence Prediction

Mover: Mask and Recovery based Facial Part Consistency Aware Method for Deepfake Video Detection

no code implementations3 Mar 2023 Juan Hu, Xin Liao, Difei Gao, Satoshi Tsutsui, Qian Wang, Zheng Qin, Mike Zheng Shou

Specifically, given a real face image, we first pretrain a masked autoencoder to learn facial part consistency by dividing faces into three parts and randomly masking ROIs, which are then recovered based on the unmasked facial parts.

DeepFake Detection Face Swapping

Object-centric Learning with Cyclic Walks between Parts and Whole

no code implementations16 Feb 2023 Ziyu Wang, Mike Zheng Shou, Mengmi Zhang

To capture compositional entities of the scene, we proposed cyclic walks between perceptual features extracted from CNN or transformers and object entities.

STPrivacy: Spatio-Temporal Privacy-Preserving Action Recognition

no code implementations ICCV 2023 Ming Li, Xiangyu Xu, Hehe Fan, Pan Zhou, Jun Liu, Jia-Wei Liu, Jiahe Li, Jussi Keppo, Mike Zheng Shou, Shuicheng Yan

For the first time, we introduce vision Transformers into PPAR by treating a video as a tubelet sequence, and accordingly design two complementary mechanisms, i. e., sparsification and anonymization, to remove privacy from a spatio-temporal perspective.

Action Recognition Facial Expression Recognition (FER) +2

MIST: Multi-modal Iterative Spatial-Temporal Transformer for Long-form Video Question Answering

1 code implementation CVPR 2023 Difei Gao, Luowei Zhou, Lei Ji, Linchao Zhu, Yi Yang, Mike Zheng Shou

To build Video Question Answering (VideoQA) systems capable of assisting humans in daily activities, seeking answers from long-form videos with diverse and complex events is a must.

Question Answering Video Question Answering +2

Position-guided Text Prompt for Vision-Language Pre-training

1 code implementation CVPR 2023 Alex Jinpeng Wang, Pan Zhou, Mike Zheng Shou, Shuicheng Yan

In this work, we propose a novel Position-guided Text Prompt (PTP) paradigm to enhance the visual grounding ability of cross-modal models trained with VLP.

Cross-Modal Retrieval Image Captioning +4

PV3D: A 3D Generative Model for Portrait Video Generation

no code implementations13 Dec 2022 Zhongcong Xu, Jianfeng Zhang, Jun Hao Liew, Wenqing Zhang, Song Bai, Jiashi Feng, Mike Zheng Shou

While some prior works have applied such image GANs to unconditional 2D portrait video generation and static 3D portrait synthesis, there are few works successfully extending GANs for generating 3D-aware portrait videos.

Video Generation

Rethinking the Objectives of Vector-Quantized Tokenizers for Image Synthesis

no code implementations6 Dec 2022 YuChao Gu, Xintao Wang, Yixiao Ge, Ying Shan, XiaoHu Qie, Mike Zheng Shou

Vector-Quantized (VQ-based) generative models usually consist of two basic components, i. e., VQ tokenizers and generative transformers.

Conditional Image Generation

Darwinian Model Upgrades: Model Evolving with Selective Compatibility

no code implementations13 Oct 2022 Binjie Zhang, Shupeng Su, Yixiao Ge, Xuyuan Xu, Yexin Wang, Chun Yuan, Mike Zheng Shou, Ying Shan

The traditional model upgrading paradigm for retrieval requires recomputing all gallery embeddings before deploying the new model (dubbed as "backfilling"), which is quite expensive and time-consuming considering billions of instances in industrial applications.

Face Recognition Retrieval

Single-Stage Open-world Instance Segmentation with Cross-task Consistency Regularization

1 code implementation18 Aug 2022 Xizhe Xue, Dongdong Yu, Lingqiao Liu, Yu Liu, Satoshi Tsutsui, Ying Li, Zehuan Yuan, Ping Song, Mike Zheng Shou

Based on the single-stage instance segmentation framework, we propose a regularization model to predict foreground pixels and use its relation to instance segmentation to construct a cross-task consistency loss.

Autonomous Driving Instance Segmentation +1

Egocentric Video-Language Pretraining @ Ego4D Challenge 2022

1 code implementation4 Jul 2022 Kevin Qinghong Lin, Alex Jinpeng Wang, Mattia Soldan, Michael Wray, Rui Yan, Eric Zhongcong Xu, Difei Gao, RongCheng Tu, Wenzhe Zhao, Weijie Kong, Chengfei Cai, Hongfa Wang, Dima Damen, Bernard Ghanem, Wei Liu, Mike Zheng Shou

In this report, we propose a video-language pretraining (VLP) based solution \cite{kevin2022egovlp} for four Ego4D challenge tasks, including Natural Language Query (NLQ), Moment Query (MQ), Object State Change Classification (OSCC), and PNR Localization (PNR).

Language Modelling Object State Change Classification

Towards Fast Adaptation of Pretrained Contrastive Models for Multi-channel Video-Language Retrieval

1 code implementation CVPR 2023 Xudong Lin, Simran Tiwari, Shiyuan Huang, Manling Li, Mike Zheng Shou, Heng Ji, Shih-Fu Chang

We surprisingly find that discrete text tokens coupled with a pretrained contrastive text model yields the best performance, which can even outperform state-of-the-art on the iVQA and How2QA datasets without additional training on millions of video-text data.

Retrieval Sentence Embeddings +1

Egocentric Video-Language Pretraining

1 code implementation3 Jun 2022 Kevin Qinghong Lin, Alex Jinpeng Wang, Mattia Soldan, Michael Wray, Rui Yan, Eric Zhongcong Xu, Difei Gao, RongCheng Tu, Wenzhe Zhao, Weijie Kong, Chengfei Cai, Hongfa Wang, Dima Damen, Bernard Ghanem, Wei Liu, Mike Zheng Shou

Video-Language Pretraining (VLP), which aims to learn transferable representation to advance a wide range of video-text downstream tasks, has recently received increasing attention.

Action Recognition Contrastive Learning +9

Label-Efficient Online Continual Object Detection in Streaming Video

1 code implementation ICCV 2023 Jay Zhangjie Wu, David Junhao Zhang, Wynne Hsu, Mengmi Zhang, Mike Zheng Shou

Remarkably, with only 25% annotated video frames, our method still outperforms the base CL learners, which are trained with 100% annotations on all video frames.

Continual Learning Hippocampus +2

Novel View Synthesis for High-fidelity Headshot Scenes

1 code implementation31 May 2022 Satoshi Tsutsui, Weijia Mao, Sijing Lin, Yunyi Zhu, Murong Ma, Mike Zheng Shou

Based on these observations, we propose a method to use both NeRF and 3DMM to synthesize a high-fidelity novel view of a scene with a face.

Novel View Synthesis Vocal Bursts Intensity Prediction

Unified Transformer Tracker for Object Tracking

1 code implementation CVPR 2022 Fan Ma, Mike Zheng Shou, Linchao Zhu, Haoqi Fan, Yilei Xu, Yi Yang, Zhicheng Yan

Although UniTrack \cite{wang2021different} demonstrates that a shared appearance model with multiple heads can be used to tackle individual tracking tasks, it fails to exploit the large-scale tracking datasets for training and performs poorly on single object tracking.

Multiple Object Tracking

Revitalize Region Feature for Democratizing Video-Language Pre-training of Retrieval

2 code implementations15 Mar 2022 Guanyu Cai, Yixiao Ge, Binjie Zhang, Alex Jinpeng Wang, Rui Yan, Xudong Lin, Ying Shan, Lianghua He, XiaoHu Qie, Jianping Wu, Mike Zheng Shou

Recent dominant methods for video-language pre-training (VLP) learn transferable representations from the raw pixels in an end-to-end manner to achieve advanced performance on downstream video-language retrieval.

Question Answering Retrieval +4

All in One: Exploring Unified Video-Language Pre-training

1 code implementation CVPR 2023 Alex Jinpeng Wang, Yixiao Ge, Rui Yan, Yuying Ge, Xudong Lin, Guanyu Cai, Jianping Wu, Ying Shan, XiaoHu Qie, Mike Zheng Shou

In this work, we for the first time introduce an end-to-end video-language model, namely \textit{all-in-one Transformer}, that embeds raw video and textual signals into joint representations using a unified backbone architecture.

Language Modelling Multiple-choice +10

AssistQ: Affordance-centric Question-driven Task Completion for Egocentric Assistant

4 code implementations8 Mar 2022 Benita Wong, Joya Chen, You Wu, Stan Weixian Lei, Dongxing Mao, Difei Gao, Mike Zheng Shou

In this paper, we define a new task called Affordance-centric Question-driven Task Completion, where the AI assistant should learn from instructional videos to provide step-by-step help in the user's view.

Visual Question Answering (VQA)

Contrastive Learning of Semantic and Visual Representations for Text Tracking

1 code implementation30 Dec 2021 Zhuang Li, Weijia Wu, Mike Zheng Shou, Jiahong Li, Size Li, Zhongyuan Wang, Hong Zhou

Semantic representation is of great benefit to the video text tracking(VTT) task that requires simultaneously classifying, detecting, and tracking texts in the video.

Contrastive Learning

Video-Text Pre-training with Learned Regions

1 code implementation2 Dec 2021 Rui Yan, Mike Zheng Shou, Yixiao Ge, Alex Jinpeng Wang, Xudong Lin, Guanyu Cai, Jinhui Tang

Video-Text pre-training aims at learning transferable representations from large-scale video-text pairs via aligning the semantics between visual and textual information.

Representation Learning Retrieval +2

Object-aware Video-language Pre-training for Retrieval

1 code implementation CVPR 2022 Alex Jinpeng Wang, Yixiao Ge, Guanyu Cai, Rui Yan, Xudong Lin, Ying Shan, XiaoHu Qie, Mike Zheng Shou

In this work, we present Object-aware Transformers, an object-centric approach that extends video-language transformer to incorporate object representations.

Retrieval Text Matching

AVA-AVD: Audio-Visual Speaker Diarization in the Wild

4 code implementations29 Nov 2021 Eric Zhongcong Xu, Zeyang Song, Satoshi Tsutsui, Chao Feng, Mang Ye, Mike Zheng Shou

Audio-visual speaker diarization aims at detecting "who spoke when" using both auditory and visual signals.

Relation Network speaker-diarization +1

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

On Pursuit of Designing Multi-modal Transformer for Video Grounding

no code implementations EMNLP 2021 Meng Cao, Long Chen, Mike Zheng Shou, Can Zhang, Yuexian Zou

Almost all existing video grounding methods fall into two frameworks: 1) Top-down model: It predefines a set of segment candidates and then conducts segment classification and regression.

Video Grounding

Generic Event Boundary Detection: A Benchmark for Event Segmentation

2 code implementations ICCV 2021 Mike Zheng Shou, Stan Weixian Lei, Weiyao Wang, Deepti Ghadiyaram, Matt Feiszli

This paper presents a novel task together with a new benchmark for detecting generic, taxonomy-free event boundaries that segment a whole video into chunks.

Action Detection Boundary Detection +3

Channel Augmented Joint Learning for Visible-Infrared Recognition

1 code implementation ICCV 2021 Mang Ye, Weijian Ruan, Bo Du, Mike Zheng Shou

This paper introduces a powerful channel augmented joint learning strategy for the visible-infrared recognition problem.

Data Augmentation Metric Learning

Actor-Context-Actor Relation Network for Spatio-Temporal Action Localization

3 code implementations CVPR 2021 Junting Pan, Siyu Chen, Mike Zheng Shou, Yu Liu, Jing Shao, Hongsheng Li

We propose to explicitly model the Actor-Context-Actor Relation, which is the relation between two actors based on their interactions with the context.

Action Detection Action Recognition +4

Cannot find the paper you are looking for? You can Submit a new open access paper.