Official Pytorch Implementation of Relational Self-Attention: What's Missing in Attention for Video Understanding

Related tags

Deep LearningRSA
Overview

Relational Self-Attention: What's Missing in Attention for Video Understanding

This repository is the official implementation of "Relational Self-Attention: What's Missing in Attention for Video Understanding" by Manjin Kim*, Heeseung Kwon*, Chunyu Wang, Suha Kwak, and Minsu Cho (*equal contribution).

RSA

Requirements

  • Python: 3.7.9
  • Pytorch: 1.6.0
  • TorchVision: 0.2.1
  • Cuda: 10.1
  • Conda environment environment.yml

To install requirements:

    conda env create -f environment.yml
    conda activate rsa

Dataset Preparation

  1. Download Something-Something v1 & v2 (SSv1 & SSv2) datasets and extract RGB frames. Download URLs: SSv1, SSv2
  2. Make txt files that define training & validation splits. Each line in txt files is formatted as [video_path] [#frames] [class_label]. Please refer to any txt files in ./data directory.

Training

To train RSANet-R50 on SSv1 or SSv2 datasets in the paper, run this command:

    # For SSv1
    ./scripts/train_Something_v1.sh 
    
    
     
    # example: ./scripts/train_Something_v1.sh RSA_R50_SSV1_16frames 16
    
    # For SSv2
    ./scripts/train_Something_v2.sh 
      
      
       
    # example: ./scripts/train_Something_v2.sh RSA_R50_SSV2_16frames 16

      
     
    
   

Evaluation

To evaluate RSANet-R50 on SSv2 dataset in the paper, run:

    # For SSv1
    ./scripts/test_Something_v1.sh 
    
     
     
      
    # example: ./scripts/test_Something_v1.sh RSA_R50_SSV1_16frames resnet_rgb_model_best.pth.tar 16
    
    # For SSv2
    ./scripts/test_Something_v2.sh 
       
        
        
          # example: ./scripts/test_Something_v2.sh RSA_R50_SSV2_16frames resnet_rgb_model_best.pth.tar 16 
        
       
      
     
    
   

Results

Our model achieves the following performance on Something-Something-V1 and Something-Something-V2:

model dataset frames top-1 / top-5 logs checkpoints
RSANet-R50 SSV1 16 54.0 % / 81.1 % [log] [checkpoint]
RSANet-R50 SSV2 16 66.0 % / 89.9 % [log] [checkpoint]

Qualitative Results

kernel_visualization

Owner
mandos
PH.D. student
mandos
Low-dose Digital Mammography with Deep Learning

Impact of loss functions on the performance of a deep neural network designed to restore low-dose digital mammography ====== This repository contains

WANG-AXIS 6 Dec 13, 2022
Making self-supervised learning work on molecules by using their 3D geometry to pre-train GNNs. Implemented in DGL and Pytorch Geometric.

3D Infomax improves GNNs for Molecular Property Prediction Video | Paper We pre-train GNNs to understand the geometry of molecules given only their 2D

Hannes Stärk 95 Dec 30, 2022
Cosine Annealing With Warmup

CosineAnnealingWithWarmup Formulation The learning rate is annealed using a cosine schedule over the course of learning of n_total total steps with an

zhuyun 4 Apr 18, 2022
A Parameter-free Deep Embedded Clustering Method for Single-cell RNA-seq Data

A Parameter-free Deep Embedded Clustering Method for Single-cell RNA-seq Data Overview Clustering analysis is widely utilized in single-cell RNA-seque

AI-Biomed @NSCC-gz 3 May 08, 2022
Learning Dynamic Network Using a Reuse Gate Function in Semi-supervised Video Object Segmentation.

Training Script for Reuse-VOS This code implementation of CVPR 2021 paper : Learning Dynamic Network Using a Reuse Gate Function in Semi-supervised Vi

HYOJINPARK 22 Jan 01, 2023
The codes and related files to reproduce the results for Image Similarity Challenge Track 1.

ISC-Track1-Submission The codes and related files to reproduce the results for Image Similarity Challenge Track 1. Required dependencies To begin with

Wenhao Wang 115 Jan 02, 2023
Neural Module Network for VQA in Pytorch

Neural Module Network (NMN) for VQA in Pytorch Note: This is NOT an official repository for Neural Module Networks. NMN is a network that is assembled

Harsh Trivedi 111 Nov 24, 2022
GraphLily: A Graph Linear Algebra Overlay on HBM-Equipped FPGAs

GraphLily: A Graph Linear Algebra Overlay on HBM-Equipped FPGAs GraphLily is the first FPGA overlay for graph processing. GraphLily supports a rich se

Cornell Zhang Research Group 39 Dec 13, 2022
PatrickStar enables Larger, Faster, Greener Pretrained Models for NLP. Democratize AI for everyone.

PatrickStar: Parallel Training of Large Language Models via a Chunk-based Memory Management Meeting PatrickStar Pre-Trained Models (PTM) are becoming

Tencent 633 Dec 28, 2022
Official implementation of "Articulation Aware Canonical Surface Mapping"

Articulation-Aware Canonical Surface Mapping Nilesh Kulkarni, Abhinav Gupta, David F. Fouhey, Shubham Tulsiani Paper Project Page Requirements Python

Nilesh Kulkarni 56 Dec 16, 2022
This program automatically runs Python code copied in clipboard

CopyRun This program runs Python code which is copied in clipboard WARNING!! USE AT YOUR OWN RISK! NO GUARANTIES IF ANYTHING GETS BROKEN. DO NOT COPY

vertinski 4 Sep 10, 2021
Rule Based Classification Project For Python

Rule-Based-Classification-Project (ENG) Business Problem: A game company wants to create new level-based customer definitions (personas) by using some

Deniz Can OĞUZ 4 Oct 29, 2022
Easily Process a Batch of Cox Models

ezcox: Easily Process a Batch of Cox Models The goal of ezcox is to operate a batch of univariate or multivariate Cox models and return tidy result. ⏬

Shixiang Wang 15 May 23, 2022
A Unified Generative Framework for Various NER Subtasks.

This is the code for ACL-ICJNLP2021 paper A Unified Generative Framework for Various NER Subtasks. Install the package in the requirements.txt, then u

177 Jan 05, 2023
RuleBERT: Teaching Soft Rules to Pre-Trained Language Models

RuleBERT: Teaching Soft Rules to Pre-Trained Language Models (Paper) (Slides) (Video) RuleBERT is a pre-trained language model that has been fine-tune

16 Aug 24, 2022
The code for MM2021 paper "Multi-Level Counterfactual Contrast for Visual Commonsense Reasoning"

The Code for MM2021 paper "Multi-Level Counterfactual Contrast for Visual Commonsense Reasoning" Setting up and using the repo Get the dataset. Follow

4 Apr 20, 2022
Official code for "EagerMOT: 3D Multi-Object Tracking via Sensor Fusion" [ICRA 2021]

EagerMOT: 3D Multi-Object Tracking via Sensor Fusion Read our ICRA 2021 paper here. Check out the 3 minute video for the quick intro or the full prese

Aleksandr Kim 276 Dec 30, 2022
PIKA: a lightweight speech processing toolkit based on Pytorch and (Py)Kaldi

PIKA: a lightweight speech processing toolkit based on Pytorch and (Py)Kaldi PIKA is a lightweight speech processing toolkit based on Pytorch and (Py)

336 Nov 25, 2022
The devkit of the nuPlan dataset.

The devkit of the nuPlan dataset.

Motional 264 Jan 03, 2023
Deep generative models of 3D grids for structure-based drug discovery

What is liGAN? liGAN is a research codebase for training and evaluating deep generative models for de novo drug design based on 3D atomic density grid

Matt Ragoza 152 Jan 03, 2023