Official implementation of Rich Semantics Improve Few-Shot Learning (BMVC, 2021)

Related tags

Deep LearningRS_FSL
Overview

Rich Semantics Improve Few-Shot Learning

Paper Link

Abstract :

Human learning benefits from multi-modal inputs that often appear as rich semantics (e.g., description of an object's attributes while learning about it). This enables us to learn generalizable concepts from very limited visual examples. However, current few-shot learning (FSL) methods use numerical class labels to denote object classes which do not provide rich semantic meanings about the learned concepts. In this work, we show that by using 'class-level' language descriptions, that can be acquired with minimal annotation cost, we can improve the FSL performance. Given a support set and queries, our main idea is to create a bottleneck visual feature (hybrid prototype) which is then used to generate language descriptions of the classes as an auxiliary task during training. We develop a Transformer based forward and backward encoding mechanism to relate visual and semantic tokens that can encode intricate relationships between the two modalities. Forcing the prototypes to retain semantic information about class description acts as a regularizer on the visual features, improving their generalization to novel classes at inference. Furthermore, this strategy imposes a human prior on the learned representations, ensuring that the model is faithfully relating visual and semantic concepts, thereby improving model interpretability. Our experiments on four datasets and ablation studies show the benefit of effectively modeling rich semantics for FSL.

Citation

@article{rsfsl,
  title={Rich Semantics Improve Few-shot Learning},
  author={Afham, Mohamed and Khan, Salman and Khan, Muhammad Haris and Naseer, Muzammal and Khan, Fahad Shahbaz},
  journal={32nd British Machine Vision Conference},
  year={2021}
}

alt text

Class Level descriptions of miniImageNet dataset is available in miniImageNet_descriptions. Some qualitative examples are shown below.

alt text

Code will be released soon !!

Owner
Mohamed Afham
Electronics and Telecommunication Engineering Undergraduate | Passionate in Deep Learning Research
Mohamed Afham
Repo for parser tensorflow(.pb) and tflite(.tflite)

tfmodel_parser .pb file is the format of tensorflow model .tflite file is the format of tflite model, which usually used in mobile devices before star

1 Dec 23, 2021
Neural Factorization of Shape and Reflectance Under An Unknown Illumination

NeRFactor [Paper] [Video] [Project] This is the authors' code release for: NeRFactor: Neural Factorization of Shape and Reflectance Under an Unknown I

Google 283 Jan 04, 2023
NU-Wave: A Diffusion Probabilistic Model for Neural Audio Upsampling @ INTERSPEECH 2021 Accepted

NU-Wave — Official PyTorch Implementation NU-Wave: A Diffusion Probabilistic Model for Neural Audio Upsampling Junhyeok Lee, Seungu Han @ MINDsLab Inc

MINDs Lab 242 Dec 23, 2022
v objective diffusion inference code for PyTorch.

v-diffusion-pytorch v objective diffusion inference code for PyTorch, by Katherine Crowson (@RiversHaveWings) and Chainbreakers AI (@jd_pressman). The

Katherine Crowson 635 Dec 30, 2022
Implicit Deep Adaptive Design (iDAD)

Implicit Deep Adaptive Design (iDAD) This code supports the NeurIPS paper 'Implicit Deep Adaptive Design: Policy-Based Experimental Design without Lik

Desi 12 Aug 14, 2022
Specificity-preserving RGB-D Saliency Detection

Specificity-preserving RGB-D Saliency Detection Authors: Tao Zhou, Huazhu Fu, Geng Chen, Yi Zhou, Deng-Ping Fan, and Ling Shao. 1. Preface This reposi

Tao Zhou 35 Jan 08, 2023
Atif Hassan 103 Dec 14, 2022
Myia prototyping

Myia Myia is a new differentiable programming language. It aims to support large scale high performance computations (e.g. linear algebra) and their g

Mila 456 Nov 07, 2022
Fbone (Flask bone) is a Flask (Python microframework) starter/template/bootstrap/boilerplate application.

Fbone (Flask bone) is a Flask (Python microframework) starter/template/bootstrap/boilerplate application.

Wilson 1.7k Dec 30, 2022
PyTorch common framework to accelerate network implementation, training and validation

pytorch-framework PyTorch common framework to accelerate network implementation, training and validation. This framework is inspired by works from MML

Dongliang Cao 3 Dec 19, 2022
LBBA-boosted WSOD

LBBA-boosted WSOD Summary Our code is based on ruotianluo/pytorch-faster-rcnn and WSCDN Sincerely thanks for your resources. Newer version of our code

Martin Dong 20 Sep 19, 2022
Trajectory Variational Autoencder baseline for Multi-Agent Behavior challenge 2022

MABe_2022_TVAE: a Trajectory Variational Autoencoder baseline for the 2022 Multi-Agent Behavior challenge This repository contains jupyter notebooks t

Andrew Ulmer 15 Nov 08, 2022
Domain Adaptation with Invariant RepresentationLearning: What Transformations to Learn?

Domain Adaptation with Invariant RepresentationLearning: What Transformations to Learn? Repository Structure: DSAN |└───amazon |    └── dataset (Amazo

DMIRLAB 17 Jan 04, 2023
Visualizing Yolov5's layers using GradCam

YOLO-V5 GRADCAM I constantly desired to know to which part of an object the object-detection models pay more attention. So I searched for it, but I di

Pooya Mohammadi Kazaj 200 Jan 01, 2023
Codes for SIGIR'22 Paper 'On-Device Next-Item Recommendation with Self-Supervised Knowledge Distillation'

OD-Rec Codes for SIGIR'22 Paper 'On-Device Next-Item Recommendation with Self-Supervised Knowledge Distillation' Paper, saved teacher models and Andro

Xin Xia 11 Nov 22, 2022
PPO Lagrangian in JAX

PPO Lagrangian in JAX This repository implements PPO in JAX. Implementation is tested on the safety-gym benchmark. Usage Install dependencies using th

Karush Suri 2 Sep 14, 2022
Contrastive Learning of Image Representations with Cross-Video Cycle-Consistency

Contrastive Learning of Image Representations with Cross-Video Cycle-Consistency This is a official implementation of the CycleContrast introduced in

13 Nov 14, 2022
Underwater industrial application yolov5m6

This project wins the intelligent algorithm contest finalist award and stands out from over 2000teams in China Underwater Robot Professional Contest, entering the final of China Underwater Robot Prof

8 Nov 09, 2022
Simple implementation of Mobile-Former on Pytorch

Simple-implementation-of-Mobile-Former At present, only the model but no trained. There may be some bug in the code, and some details may be different

Acheung 103 Dec 31, 2022