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
CVPR 2021 - Official code repository for the paper: On Self-Contact and Human Pose.

SMPLify-XMC This repo is part of our project: On Self-Contact and Human Pose. [Project Page] [Paper] [MPI Project Page] License Software Copyright Lic

Lea Müller 83 Dec 14, 2022
Source code for "Pack Together: Entity and Relation Extraction with Levitated Marker"

PL-Marker Source code for Pack Together: Entity and Relation Extraction with Levitated Marker. Quick links Overview Setup Install Dependencies Data Pr

THUNLP 173 Dec 30, 2022
In this project, we'll be making our own screen recorder in Python using some libraries.

Screen Recorder in Python Project Description: In this project, we'll be making our own screen recorder in Python using some libraries. Requirements:

Hassan Shahzad 4 Jan 24, 2022
Official code for "On the Frequency Bias of Generative Models", NeurIPS 2021

Frequency Bias of Generative Models Generator Testbed Discriminator Testbed This repository contains official code for the paper On the Frequency Bias

35 Nov 01, 2022
《Geo Word Clouds》paper implementation

《Geo Word Clouds》paper implementation

Russellwzr 2 Jan 28, 2022
Compute FID scores with PyTorch.

FID score for PyTorch This is a port of the official implementation of Fréchet Inception Distance to PyTorch. See https://github.com/bioinf-jku/TTUR f

2.1k Jan 06, 2023
Awesome-AI-books - Some awesome AI related books and pdfs for learning and downloading

Awesome AI books Some awesome AI related books and pdfs for downloading and learning. Preface This repo only used for learning, do not use in business

luckyzhou 1k Jan 01, 2023
Benchmarking the robustness of Spatial-Temporal Models

Benchmarking the robustness of Spatial-Temporal Models This repositery contains the code for the paper Benchmarking the Robustness of Spatial-Temporal

Yi Chenyu Ian 15 Dec 16, 2022
Zeyuan Chen, Yangchao Wang, Yang Yang and Dong Liu.

Principled S2R Dehazing This repository contains the official implementation for PSD Framework introduced in the following paper: PSD: Principled Synt

zychen 78 Dec 30, 2022
Unsupervised CNN for Single View Depth Estimation: Geometry to the Rescue

Realtime Unsupervised Depth Estimation from an Image This is the caffe implementation of our paper "Unsupervised CNN for single view depth estimation:

Ravi Garg 227 Nov 28, 2022
An e-commerce company wants to segment its customers and determine marketing strategies according to these segments.

customer_segmentation_with_rfm Business Problem : An e-commerce company wants to

Buse Yıldırım 3 Jan 06, 2022
Tutorial: Introduction to Graph Machine Learning, with Jupyter notebooks

GraphMLTutorialNLDL22 Tutorial NLDL22: Introduction to Graph Machine Learning, with Jupyter notebooks This tutorial takes place during the conference

UiT Machine Learning Group 3 Jan 10, 2022
Official implementation of "Robust channel-wise illumination estimation"

This repository provides the official implementation of "Robust channel-wise illumination estimation." accepted in BMVC (2021).

Firas Laakom 4 Nov 08, 2022
Single-Stage Instance Shadow Detection with Bidirectional Relation Learning (CVPR 2021 Oral)

Single-Stage Instance Shadow Detection with Bidirectional Relation Learning (CVPR 2021 Oral) Tianyu Wang*, Xiaowei Hu*, Chi-Wing Fu, and Pheng-Ann Hen

Steve Wong 51 Oct 20, 2022
Behind the Curtain: Learning Occluded Shapes for 3D Object Detection

Behind the Curtain: Learning Occluded Shapes for 3D Object Detection Acknowledgement We implement our model, BtcDet, based on [OpenPcdet 0.3.0]. Insta

Qiangeng Xu 163 Dec 19, 2022
Python-kafka-reset-consumergroup-offset-example - Python Kafka reset consumergroup offset example

Python Kafka reset consumergroup offset example This is a simple example of how

Willi Carlsen 1 Feb 16, 2022
Materials for my scikit-learn tutorial

Scikit-learn Tutorial Jake VanderPlas email: [email protected] twitter: @jakevdp gith

Jake Vanderplas 1.6k Dec 30, 2022
Generative Models as a Data Source for Multiview Representation Learning

GenRep Project Page | Paper Generative Models as a Data Source for Multiview Representation Learning Ali Jahanian, Xavier Puig, Yonglong Tian, Phillip

Ali 81 Dec 03, 2022
COPA-SSE contains crowdsourced explanations for the Balanced COPA dataset

COPA-SSE Repository for COPA-SSE: Semi-Structured Explanations for Commonsense Reasoning. COPA-SSE contains crowdsourced explanations for the Balanced

Ana Brassard 5 Jul 31, 2022