Keep CALM and Improve Visual Feature Attribution

Related tags

Deep Learningcalm
Overview

Keep CALM and Improve Visual Feature Attribution

Jae Myung Kim1*, Junsuk Choe1*, Zeynep Akata2, Seong Joon Oh1†
* Equal contribution Corresponding author

1 NAVER AI LAB 2 University of Tübingen

CAM vs CALM

Abstract

The class activation mapping, or CAM, has been the cornerstone of feature attribution methods for multiple vision tasks. Its simplicity and effectiveness have led to wide applications in the explanation of visual predictions and weakly-supervised localization tasks. However, CAM has its own shortcomings. The computation of attribution maps relies on ad-hoc calibration steps that are not part of the training computational graph, making it difficult for us to understand the real meaning of the attribution values. In this paper, we improve CAM by explicitly incorporating a latent variable encoding the location of the cue for recognition in the formulation, thereby subsuming the attribution map into the training computational graph. The resulting model, class activation latent mapping, or CALM, is trained with the expectation-maximization algorithm. Our experiments show that CALM identifies discriminative attributes for image classifiers more accurately than CAM and other visual attribution baselines. CALM also shows performance improvements over prior arts on the weakly-supervised object localization benchmarks.

Dataset downloading

For ImageNet and CUB datasets, please follow the common procedure for downloading the datasets.
For ImageNetV2, CUBV2, and OpenImages30k, please follow the procedure introduced in wsol-evaluation page.

How to use models

You can train CALM models by

$ python main.py --experiment_name=experiment_name/ \
                 --architecture=resnet50 \
                 --attribution_method=CALM_EM \
                 --dataset=CUB \
                 --use_bn=True --large_feature_map=True

You can evaluate the models on two different metrics,

$ python eval_pixel_perturb.py --experiment_name=experiment_name/ \
                               --architecture=resnet50 \ 
                               --attribution_method=CALM_EM \
                               --dataset=CUB \
                               --use_bn=True --large_feature_map=True \
                               --use_load_checkpoint=True \
                               --load_checkpoint=checkpoint_name/ \
                               --score_map_process=jointll --norm_type=clipping &
                               
$ python eval_cue_location.py --experiment_name=experiment_name/ \ 
                              --architecture=resnet50 \
                              --attribution_method=CALM_EM \
                              --dataset=CUB \
                              --use_bn=True --large_feature_map=True \
                              --use_load_checkpoint=True \
                              --load_checkpoint=checkpoint_name/ \
                              --score_map_process=jointll --norm_type=clipping --threshold_type=log &

Pretrained weights

For those who wish to use pretrained CALM weights,

Model name Dataset cls. accuracy weights
CALM_EM CUB 71.8 link
CALM_EM OpenImages 70.1 link
CALM_EM ImageNet 70.4 link
CALM_ML CUB 59.6 link
CALM_ML OpenImages 70.9 link
CALM_ML ImageNet 70.6 link

Explainability scores

Cue localization and Remove-and-classify results. More details about the metrics are in the paper.

Cue localization
(the higher, the better)
Remove-and-classify
(the lower, the better)

License

Copyright (c) 2021-present NAVER Corp.

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.  IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
THE SOFTWARE.
Owner
NAVER AI
Official account of NAVER AI, Korea No.1 Industrial AI Research Group
NAVER AI
Tensorflow 2.x implementation of Panoramic BlitzNet for object detection and semantic segmentation on indoor panoramic images.

Deep neural network for object detection and semantic segmentation on indoor panoramic images. The implementation is based on the papers:

Alejandro de Nova Guerrero 9 Nov 24, 2022
Sound Event Detection with FilterAugment

Sound Event Detection with FilterAugment Official implementation of Heavily Augmented Sound Event Detection utilizing Weak Predictions (DCASE2021 Chal

43 Aug 28, 2022
This is a tensorflow-based rotation detection benchmark, also called AlphaRotate.

AlphaRotate: A Rotation Detection Benchmark using TensorFlow Abstract AlphaRotate is maintained by Xue Yang with Shanghai Jiao Tong University supervi

yangxue 972 Jan 05, 2023
An Unbiased Learning To Rank Algorithms (ULTRA) toolbox

Unbiased Learning to Rank Algorithms (ULTRA) This is an Unbiased Learning To Rank Algorithms (ULTRA) toolbox, which provides a codebase for experiment

back 3 Nov 18, 2022
Code for "Contextual Non-Local Alignment over Full-Scale Representation for Text-Based Person Search"

Contextual Non-Local Alignment over Full-Scale Representation for Text-Based Person Search This is an implementation for our paper Contextual Non-Loca

Tencent YouTu Research 50 Dec 03, 2022
[CVPR22] Official codebase of Semantic Segmentation by Early Region Proxy.

RegionProxy Figure 2. Performance vs. GFLOPs on ADE20K val split. Semantic Segmentation by Early Region Proxy Yifan Zhang, Bo Pang, Cewu Lu CVPR 2022

Yifan 54 Nov 29, 2022
[peer review] An Arbitrary Scale Super-Resolution Approach for 3D MR Images using Implicit Neural Representation

ArSSR This repository is the pytorch implementation of our manuscript "An Arbitrary Scale Super-Resolution Approach for 3-Dimensional Magnetic Resonan

Qing Wu 19 Dec 12, 2022
[WACV 2022] Contextual Gradient Scaling for Few-Shot Learning

CxGrad - Official PyTorch Implementation Contextual Gradient Scaling for Few-Shot Learning Sanghyuk Lee, Seunghyun Lee, and Byung Cheol Song In WACV 2

Sanghyuk Lee 4 Dec 05, 2022
AquaTimer - Programmable Timer for Aquariums based on ATtiny414/814/1614

AquaTimer - Programmable Timer for Aquariums based on ATtiny414/814/1614 AquaTimer is a programmable timer for 12V devices such as lighting, solenoid

Stefan Wagner 4 Jun 13, 2022
Source code for "Roto-translated Local Coordinate Framesfor Interacting Dynamical Systems"

Roto-translated Local Coordinate Frames for Interacting Dynamical Systems Source code for Roto-translated Local Coordinate Frames for Interacting Dyna

Miltiadis Kofinas 19 Nov 27, 2022
CRISCE: Automatically Generating Critical Driving Scenarios From Car Accident Sketches

CRISCE: Automatically Generating Critical Driving Scenarios From Car Accident Sketches This document describes how to install and use CRISCE (CRItical

Chair of Software Engineering II, Uni Passau 2 Feb 09, 2022
[CVPR 2021] Anycost GANs for Interactive Image Synthesis and Editing

Anycost GAN video | paper | website Anycost GANs for Interactive Image Synthesis and Editing Ji Lin, Richard Zhang, Frieder Ganz, Song Han, Jun-Yan Zh

MIT HAN Lab 726 Dec 28, 2022
Modular Gaussian Processes

Modular Gaussian Processes for Transfer Learning 🧩 Introduction This repository contains the implementation of our paper Modular Gaussian Processes f

Pablo Moreno-Muñoz 10 Mar 15, 2022
Finding Donors for CharityML

Finding-Donors-for-CharityML - Investigated factors that affect the likelihood of charity donations being made based on real census data.

Moamen Abdelkawy 1 Dec 30, 2021
Equivariant Imaging: Learning Beyond the Range Space

Equivariant Imaging: Learning Beyond the Range Space Equivariant Imaging: Learning Beyond the Range Space Dongdong Chen, Julián Tachella, Mike E. Davi

Dongdong Chen 46 Jan 01, 2023
Recursive Bayesian Networks

Recursive Bayesian Networks This repository contains the code to reproduce the results from the NeurIPS 2021 paper Lieck R, Rohrmeier M (2021) Recursi

Robert Lieck 11 Oct 18, 2022
Use MATLAB to simulate the signal and extract features. Use PyTorch to build and train deep network to do spectrum sensing.

Deep-Learning-based-Spectrum-Sensing Use MATLAB to simulate the signal and extract features. Use PyTorch to build and train deep network to do spectru

10 Dec 14, 2022
PyTorch framework, for reproducing experiments from the paper Implicit Regularization in Hierarchical Tensor Factorization and Deep Convolutional Neural Networks

Implicit Regularization in Hierarchical Tensor Factorization and Deep Convolutional Neural Networks. Code, based on the PyTorch framework, for reprodu

Asaf 3 Dec 27, 2022
Pytorch implementation of SELF-ATTENTIVE VAD, ICASSP 2021

SELF-ATTENTIVE VAD: CONTEXT-AWARE DETECTION OF VOICE FROM NOISE (ICASSP 2021) Pytorch implementation of SELF-ATTENTIVE VAD | Paper | Dataset Yong Rae

97 Dec 23, 2022
Implementation / replication of DALL-E, OpenAI's Text to Image Transformer, in Pytorch

DALL-E in Pytorch Implementation / replication of DALL-E, OpenAI's Text to Image Transformer, in Pytorch. It will also contain CLIP for ranking the ge

Phil Wang 5k Jan 04, 2023