Bi-directional Adapter for Multi-modal Tracking

🤗 PEFT: State-of-the-art Parameter-Efficient Fine-Tuning.

Code for loralib, an implementation of "LoRA: Low-Rank Adaptation of Large Language Models"

[CVPR21] LightTrack: Finding Lightweight Neural Network for Object Tracking via One-Shot Architecture Search

[CVPR 2024] Unified Multi-Sensor Tracker With One Parameter Set

add useful and convenient function to [ECCV2022] OSTrack

Pyramid Grafting Network for One-Stage High Resolution Saliency Detection. CVPR 2022

A curated list of prompt-based paper in computer vision and vision-language learning.

This is the repo for our new project Highly Accurate Dichotomous Image Segmentation

Jupyter notebook tutorials for MMSegmentation

OpenMMLab Semantic Segmentation Toolbox and Benchmark.

Pytorch implementation for Semantic Segmentation/Scene Parsing on MIT ADE20K dataset

Semantic Segmentation on PyTorch (include FCN, PSPNet, Deeplabv3, Deeplabv3+, DANet, DenseASPP, BiSeNet, EncNet, DUNet, ICNet, ENet, OCNet, CCNet, PSANet, CGNet, ESPNet, LEDNet, DFANet)

deep learning for image processing including classification and object-detection etc.

A selection of RGB-T object tracking papers and their performance on various benchmarks.

[CVPR 2023] Official repository of paper titled "MaPLe: Multi-modal Prompt Learning".

[CVPR19/TPAMI23] SiamMask: A Framework for Fast Online Object Tracking and Segmentation

SG-One: Similarity Guidance Network for One-Shot Semantic Segmentation

jointly train primary task and auxiliary tasks

互联网首份程序员考公指南，由3位已经进入体制内的前大厂程序员联合献上。

Official implementation for the paper "Prompt Pre-Training with Over Twenty-Thousand Classes for Open-Vocabulary Visual Recognition"

LRRNet (IEEE TPAMI 2023, Highly Cited Paper), Python 3.7, Pytorch >=1.8

The papers and results about RGB-T fusion tracking

Prompt Learning for Vision-Language Models (IJCV'22, CVPR'22)

[ECCV 2022] Joint Feature Learning and Relation Modeling for Tracking: A One-Stream Framework

[CVPR23] Visual Prompt Multi-Modal Tracking

[CVPR'23] Universal Instance Perception as Object Discovery and Retrieval