Kai Chen

Orcid: 0000-0002-6820-2325

Affiliations:
  • SenseTime Research, Hong Kong
  • Shanghai AI Laboratory, Guangzhou, China
  • Chinese University of Hong Kong, SenseTime Joint Lab, Hong Kong (PhD 2019)


According to our database1, Kai Chen authored at least 50 papers between 2017 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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PhD thesis 
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Links

Online presence:

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Bibliography

2024
Make-It-Vivid: Dressing Your Animatable Biped Cartoon Characters from Text.
CoRR, 2024

InternLM-Math: Open Math Large Language Models Toward Verifiable Reasoning.
CoRR, 2024

InternLM-XComposer2: Mastering Free-form Text-Image Composition and Comprehension in Vision-Language Large Model.
CoRR, 2024

2023
BotChat: Evaluating LLMs' Capabilities of Having Multi-Turn Dialogues.
CoRR, 2023

InternLM-XComposer: A Vision-Language Large Model for Advanced Text-image Comprehension and Composition.
CoRR, 2023

MMBench: Is Your Multi-modal Model an All-around Player?
CoRR, 2023

Segment Any Point Cloud Sequences by Distilling Vision Foundation Models.
CoRR, 2023

MultiModal-GPT: A Vision and Language Model for Dialogue with Humans.
CoRR, 2023

Transformer-Based Visual Segmentation: A Survey.
CoRR, 2023

RoboBEV: Towards Robust Bird's Eye View Perception under Corruptions.
CoRR, 2023

Robo3D: Towards Robust and Reliable 3D Perception against Corruptions.
CoRR, 2023

PixMIM: Rethinking Pixel Reconstruction in Masked Image Modeling.
CoRR, 2023

Dense Distinct Query for End-to-End Object Detection.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

Semantics-Aware Dynamic Localization and Refinement for Referring Image Segmentation.
Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022
CARAFE++: Unified Content-Aware ReAssembly of FEatures.
IEEE Trans. Pattern Anal. Mach. Intell., 2022

RTMDet: An Empirical Study of Designing Real-Time Object Detectors.
CoRR, 2022

DG-STGCN: Dynamic Spatial-Temporal Modeling for Skeleton-based Action Recognition.
CoRR, 2022

What Are Expected Queries in End-to-End Object Detection?
CoRR, 2022

Dense Siamese Network.
CoRR, 2022

MMRotate: A Rotated Object Detection Benchmark using PyTorch.
Proceedings of the MM '22: The 30th ACM International Conference on Multimedia, Lisboa, Portugal, October 10, 2022

PYSKL: Towards Good Practices for Skeleton Action Recognition.
Proceedings of the MM '22: The 30th ACM International Conference on Multimedia, Lisboa, Portugal, October 10, 2022

Dense Siamese Network for Dense Unsupervised Learning.
Proceedings of the Computer Vision - ECCV 2022, 2022

Mitigating Representation Bias in Action Recognition: Algorithms and Benchmarks.
Proceedings of the Computer Vision - ECCV 2022 Workshops, 2022

Group R-CNN for Weakly Semi-supervised Object Detection with Points.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

OCSampler: Compressing Videos to One Clip with Single-step Sampling.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

Video K-Net: A Simple, Strong, and Unified Baseline for Video Segmentation.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

TransRank: Self-supervised Video Representation Learning via Ranking-based Transformation Recognition.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

Revisiting Skeleton-based Action Recognition.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

LAVT: Language-Aware Vision Transformer for Referring Image Segmentation.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

2021
Towards Balanced Learning for Instance Recognition.
Int. J. Comput. Vis., 2021

STransGAN: An Empirical Study on Transformer in GANs.
CoRR, 2021

WSSOD: A New Pipeline for Weakly- and Semi-Supervised Object Detection.
CoRR, 2021

Revisiting Skeleton-based Action Recognition.
CoRR, 2021

K-Net: Towards Unified Image Segmentation.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Few-Shot Object Detection via Association and DIscrimination.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

MMOCR: A Comprehensive Toolbox for Text Detection, Recognition and Understanding.
Proceedings of the MM '21: ACM Multimedia Conference, Virtual Event, China, October 20, 2021

Positional Encoding As Spatial Inductive Bias in GANs.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2021

Seesaw Loss for Long-Tailed Instance Segmentation.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2021

Temporal ROI Align for Video Object Recognition.
Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020
Feature Pyramid Grids.
CoRR, 2020

Side-Aware Boundary Localization for More Precise Object Detection.
Proceedings of the Computer Vision - ECCV 2020, 2020

Prime Sample Attention in Object Detection.
Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020

2019
MMDetection: Open MMLab Detection Toolbox and Benchmark.
CoRR, 2019

CARAFE: Content-Aware ReAssembly of FEatures.
Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision, 2019

Region Proposal by Guided Anchoring.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019

Libra R-CNN: Towards Balanced Learning for Object Detection.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019

Hybrid Task Cascade for Instance Segmentation.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019

2018
Optimizing Video Object Detection via a Scale-Time Lattice.
Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition, 2018

2017
Video Object Segmentation with Re-identification.
CoRR, 2017

Discover and Learn New Objects from Documentaries.
Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, 2017


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