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zhulinxiaohai's Projects

actionclip icon actionclip

This is the official implement of paper "ActionCLIP: A New Paradigm for Action Recognition"

advsemiseg icon advsemiseg

Adversarial Learning for Semi-supervised Semantic Segmentation, BMVC 2018

aimclr icon aimclr

This is an official PyTorch implementation of "Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition" in AAAI2022.

bytetrack icon bytetrack

[ECCV 2022] ByteTrack: Multi-Object Tracking by Associating Every Detection Box

cct icon cct

:page_facing_up: Semi-Supervised Semantic Segmentation with Cross-Consistency Training (CVPR 2020).

corrpm icon corrpm

Pytorch implementation of CVPR2020 paper "Correlating Edge, Pose with Parsing"

gpt4free icon gpt4free

The official gpt4free repository | various collection of powerful language models

instancerefer icon instancerefer

[ICCV 2021] InstanceRefer: Cooperative Holistic Understanding for Visual Grounding on Point Clouds through Instance Multi-level Contextual Referring

moco icon moco

PyTorch implementation of MoCo: https://arxiv.org/abs/1911.05722

npp icon npp

Neural Architecture Search for Joint Human Parsing and Pose Estimation

ps-mt icon ps-mt

[CVPR'22] Perturbed and Strict Mean Teachers for Semi-supervised Semantic Segmentation

segment-and-track-anything icon segment-and-track-anything

An open-source project dedicated to tracking and segmenting any objects in videos, either automatically or interactively. The primary algorithms utilized include the Segment Anything Model (SAM) for key-frame segmentation and Associating Objects with Transformers (AOT) for efficient tracking and propagation purposes.

smie icon smie

This is an official PyTorch implementation of "Zero-shot Skeleton-based Action Recognition via Mutual Information Estimation and Maximization" in ACMMM 2023.

torchsemiseg icon torchsemiseg

[CVPR 2021] Semi-Supervised Semantic Segmentation with Cross Pseudo Supervision

u2pl icon u2pl

[CVPR 2022] Semi-Supervised Semantic Segmentation Using Unreliable Pseudo-Labels

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