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A comprehensive list of papers using large language/multi-modal models for Robotics/RL, including papers, codes, and related websites
A Collection of Papers and Codes for CVPR2024/ECCV2024 AIGC
[ECCV2024] Grounded Multimodal Large Language Model with Localized Visual Tokenization
📖 A curated list of resources dedicated to hallucination of multimodal large language models (MLLM).
[CVPR'23] Universal Instance Perception as Object Discovery and Retrieval
CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image
The official GitHub page for the survey paper "A Survey of Large Language Models".
✨✨Latest Advances on Multimodal Large Language Models
Use Confident Learning to clean out noise labels in object detection dataset, based on mmdetection
OpenMMLab Model Compression Toolbox and Benchmark.
Code to accompany "A Method for Animating Children's Drawings of the Human Figure"
AutoGPT is the vision of accessible AI for everyone, to use and to build on. Our mission is to provide the tools, so that you can focus on what matters.
Code for ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks
GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond
Official PyTorch Implementation for "Rotate to Attend: Convolutional Triplet Attention Module." [WACV 2021]
The code for the paper 'BA-Net: Bridge Attention for Deep Convolutional Neural Networks'
SDA-xNet: Selective Depth Attention Networks for Adaptive Multi-scale Feature Representation, IEEE Transactions on Artificial Intelligence, 2024
Multi-head Recurrent Layer Attention for Vision Network
An Invitation to 3D Vision: A Tutorial for Everyone
A PyTorch Library for Multi-Task Learning
Summary of related papers on visual attention. Related code will be released based on Jittor gradually.
《代码随想录》LeetCode 刷题攻略：200道经典题目刷题顺序，共60w字的详细图解，视频难点剖析，50余张思维导图，支持C++，Java，Python，Go，JavaScript等多语言版本，从此算法学习不再迷茫！🔥🔥 来看看，你会发现相见恨晚！🚀
Pytorch Implementations of large number classical backbone CNNs, data enhancement, torch loss, attention, visualization and some common algorithms.
OpenMMLab's Next Generation Video Understanding Toolbox and Benchmark
OpenMMLab Video Perception Toolbox. It supports Video Object Detection (VID), Multiple Object Tracking (MOT), Single Object Tracking (SOT), Video Instance Segmentation (VIS) with a unified framework.
OpenMMLab Detection Toolbox and Benchmark
Object detection, 3D detection, and pose estimation using center point detection: