[NeurIPS 2020] Code for the paper "Balanced Meta-Softmax for Long-Tailed Visual Recognition"

Last update: Dec 21, 2022

Related tags

Overview

Balanced Meta-Softmax

Code for the paper

Balanced Meta-Softmax for Long-Tailed Visual Recognition
Jiawei Ren, Cunjun Yu, Shunan Sheng, Xiao Ma, Haiyu Zhao, Shuai Yi, Hongsheng Li
NeurIPS 2020

We present our code for image classification and instance segmentation in two sub-repositories. Please go into the links below for more information.

Classification	Instance Segmentation

Cite BALMS

@inproceedings{
    Ren2020balms,
    title={Balanced Meta-Softmax for Long-Tailed Visual Recognition},
    author={Jiawei Ren and Cunjun Yu and Shunan Sheng and Xiao Ma and Haiyu Zhao and Shuai Yi and Hongsheng Li},
    booktitle={Proceedings of Neural Information Processing Systems(NeurIPS)},
    month = {Dec},
    year={2020}
}

Owner

Jiawei Ren

GitHub Repository https://sites.google.com/view/balms-neurips2020/home

This repository holds code and data for our PETS'22 article 'From "Onion Not Found" to Guard Discovery'.

From "Onion Not Found" to Guard Discovery (PETS'22) This repository holds the code and data for our PETS'22 paper titled 'From "Onion Not Found" to Gu

3 May 04, 2022

This code implements constituency parse tree aggregation

README This code implements constituency parse tree aggregation. Folder details code: This folder contains the code that implements constituency parse

0 Oct 11, 2021

The software associated with a paper accepted at EMNLP 2021 titled "Open Knowledge Graphs Canonicalization using Variational Autoencoders".

Open-KG-canonicalization The software associated with a paper accepted at EMNLP 2021 titled "Open Knowledge Graphs Canonicalization using Variational

13 Nov 11, 2022

💡 Learnergy is a Python library for energy-based machine learning models.

Learnergy: Energy-based Machine Learners Welcome to Learnergy. Did you ever reach a bottleneck in your computational experiments? Are you tired of imp

57 Nov 17, 2022

A Pytorch implementation of SMU: SMOOTH ACTIVATION FUNCTION FOR DEEP NETWORKS USING SMOOTHING MAXIMUM TECHNIQUE

SMU_pytorch A Pytorch Implementation of SMU: SMOOTH ACTIVATION FUNCTION FOR DEEP NETWORKS USING SMOOTHING MAXIMUM TECHNIQUE arXiv https://arxiv.org/ab

36 Dec 24, 2022

Breaking the Dilemma of Medical Image-to-image Translation

Breaking the Dilemma of Medical Image-to-image Translation Supervised Pix2Pix and unsupervised Cycle-consistency are two modes that dominate the field

86 Dec 21, 2022

Driller: augmenting AFL with symbolic execution!

Driller Driller is an implementation of the driller paper. This implementation was built on top of AFL with angr being used as a symbolic tracer. Dril

791 Jan 06, 2023

Official PyTorch code for Hierarchical Conditional Flow: A Unified Framework for Image Super-Resolution and Image Rescaling (HCFlow, ICCV2021)

Hierarchical Conditional Flow: A Unified Framework for Image Super-Resolution and Image Rescaling (HCFlow, ICCV2021) This repository is the official P

159 Dec 30, 2022

Real-CUGAN - Real Cascade U-Nets for Anime Image Super Resolution

Real Cascade U-Nets for Anime Image Super Resolution 中文 | English 🔥 Real-CUGAN

111 Dec 28, 2022

A Collection of LiDAR-Camera-Calibration Papers, Toolboxes and Notes

443 Jan 06, 2023

A flexible and extensible framework for gait recognition.

A flexible and extensible framework for gait recognition. You can focus on designing your own models and comparing with state-of-the-arts easily with the help of OpenGait.

335 Dec 22, 2022

Codes accompanying the paper "Believe What You See: Implicit Constraint Approach for Offline Multi-Agent Reinforcement Learning" (NeurIPS 2021 Spotlight

Implicit Constraint Q-Learning This is a pytorch implementation of ICQ on Datasets for Deep Data-Driven Reinforcement Learning (D4RL) and ICQ-MA on SM

42 Dec 23, 2022

Llvlir - Low Level Variable Length Intermediate Representation

Low Level Variable Length Intermediate Representation Low Level Variable Length

2 Jan 24, 2022

Experiments and code to generate the GINC small-scale in-context learning dataset from "An Explanation for In-context Learning as Implicit Bayesian Inference"

GINC small-scale in-context learning dataset GINC (Generative In-Context learning Dataset) is a small-scale synthetic dataset for studying in-context

29 Dec 19, 2022