Weakly Supervised Object Localization via Transformer with Implicit Spatial Calibration

By Haotian Bai, Ruimao Zhang, Jiong Wang, Xiang Wan

Introduction

[ECCV 2022] Weakly Supervised Object Localization (WSOL), which aims to localize objects by only image-level labels, has attracted much attention because of its low annotation cost in actual application.Recent studies leverage the advantage of self-attention in visual Transformer for long-range dependency to re-active semantic regions, aiming to avoid partial activation in traditional class activation mapping (CAM). However, the long-range modeling in Transformer neglects the inherent spatial coherence of the object. Usually, it diffuses the semantic-aware regions far from the object boundary, making localization results significantly larger or far smaller. To address such an issue, we introduce a simple yet effective Spatial Calibration Module (SCM) for accurate WSOL, incorporating semantic similarities of patch tokens and their spatial relationships into a unified diffusion model. Specifically, we introduce a learnable parameter to dynamically adjust the semantic correlations and spatial context intensities for effective information propagation. In practice, SCM is designed as an external module of Transformer, and can be removed during inference to reduce the computation cost. The object-sensitive localization ability is implicitly embedded into the Transformer encoder through optimization in the training phase. In summary, SCM enables the generated attention maps to capture the sharper object boundaries and filter the object-irrelevant background area. Extensive experimental results demonstrate the effectiveness of the proposed method, which significantly outperforms its counterpart TS-CAM on both CUB-200 and ImageNet-1K benchmarks.

This is the official implementation of "Weakly Supervised Object Localization via Transformer with Implicit Spatial Calibration" in PyTorch. Our code is developed based on TS-CAM and Wsolevaluation. Thanks for your code!

For details, please refer to Link:Arxiv, or the video in Youtube.

Overview

Updates

• [2022-07-17] Initial Commits. Code publically available!
• [2022-07-22] The Arxiv version is available!
• [2022-09-25] ECCV 2022 poster is available!
• [2022-10-02] ECCV video presentation is available!

Results and Models

Datasets	Backbone	Top1-Loc Acc	Top5-Loc Acc	GT-Known	Top1-Cls Acc	Top5-Cls Acc	Checkpoints & logs
CUB	Deit-S	76.4	91.6	96.6	78.5	94.5	Google Drive
ILSVRC	Deit-S	56.1	66.4	68.8	76.7	93.0	Google Drive

Usage

Installation

conda env create -f environment.yml
conda activate SCM

Dataset

To prepare the datasets, you can download CUB and ISVRC from the links below.

CUB: https://www.vision.caltech.edu/datasets/cub_200_2011/
ISVRC: https://www.image-net.org/challenges/LSVRC/2012/

Then the absolute paths should be specified in the following config file paths.

.
├── CUB
│ ├── conformer_scm_small_patch16_224.yaml
│ ├── deit_scm_base_patch16_224.yaml
│ ├── deit_scm_small_patch16_224.yaml
│ ├── deit_tscam_base_patch16_224.yaml
│ ├── deit_tscam_small_patch16_224.yaml
│ ├── deit_tscam_tiny_patch16_224.yaml
│ └── vit_scm_small_patch16_224.yaml
└── ILSVRC
 ├── conformer_tscam_small_patch16_224.yaml
 ├── deit_scm_base_patch16_224.yaml
 ├── deit_scm_small_patch16_224.yaml
 ├── deit_tscam_base_patch16_224.yaml
 ├── deit_tscam_small_patch16_224.yaml
 └── deit_tscam_tiny_patch16_224.yaml

For instance in CUB/deit_scm_small_patch16_224.yaml:

DATA:
 DATASET: CUB
 DATADIR: /root/Datasets/CUB_200_2011

TRAIN:
 BATCH_SIZE: 256

TEST:
 BATCH_SIZE: 256

You could change the DATADIR for the absolute path of the dataset or the BATCH_SIZE depending on your memory.

Traing

Please use the commands below to launch SCM. Replace {config_file} with the config file path you set up and learning rate {lr}.

python tools_cam/test_cam.py --config_file {config_file} --lr {lr}

# CUB
python tools_cam/train_cam.py --config_file ./configs/CUB/deit_scm_small_patch16_224.yaml --lr 5e-5
# ILSVRC
python tools_cam/train_cam.py --config_file ./configs/ILSVRC/deit_scm_small_patch16_224.yaml --lr 1e-6

Since only CUBV2 dataset is available for MaxboxAcc. Please prepare the dataset CUBV2 at the root directory of CUB_200_2011 if you want to test it.

CUB_200_2011/CUBV2

We provide the default False option at tools_cam/train_cam.py

CUBV2=False

You can test MaxboxAcc when setting it to True.

Testing

Specify the path of .pth file for testing and visualization.

# CUB
python tools_cam/test_cam.py --config_file ./configs/CUB/deit_scm_small_patch16_224.yaml --resume ${pth_file_path}
# ILSVRC
python tools_cam/test_cam.py --config_file ./configs/ILSVRC/deit_scm_small_patch16_224.yaml --resume ${pth_file_path}

Set up testing at tools_cam/test_cam.py

DRAW = False
# VALIDATE_SET = False
topk = (1,5)
cam_curve_interval=.01
multi_contour_eval=False
iou_threshold_list=[30, 50, 70]
metrics = ["maxbox_acc", "GT-Known", "loc_acc", "cls_acc"]

1. DRAW means whether to draw the visualization result.
2. cam_curve_interval, mutli_contour_eval, and iou_threshold_list are used to search for optimal threshold values. For details, please refer to Wsolevaluation.
3. metrics denote the measures for evaluation.

If DRAW=TRUE, the resulting folder contains:

.
├── attn_images
├── backup
├── binary
├── boxed_image
├── ckpt
├── log
├── Log_xx.txt
├── roi_images
└── sem_images

where attn_images refers to the attention map $\boldsymbol{F}^0$, sem_images refers to $\boldsymbol{S}^0$, and roi_images refers to the predicted scores $\boldsymbol{F}^0\odot \boldsymbol{S}^0$ used for bounding box prediction.

The roi_images, binary folders, and boxed_image correspond to the our viusal results displayed in the main paper.

Resume Training

Add the ${pth_file_path} to continue the training if needed.

 --resume ${pth_file_path}

Log

The logs are organized as

├── Log_2022-xx-xx-xx-xx.txt
├── backup
├── ckpt
│ └── model_best.pth
├── log
│ └── events.out.tfevents.xxx
└── val.txt

where the training result is saved as Log_2022-xx-xx-16-20.txt with logs of validation in val.txt. You can also check the parameters update at events.out.tfevents by tensorboard.

Core design

The diffusion algorithm is available at

lib/models/graphFusion.py

which is imported at lib/models/deit.py

Citation

Please consider citing our article.

@inproceedings{Bai2022SCM,
 title={Weakly Supervised Object Localization via Transformer with Implicit Spatial Calibration},
 author={Haotian, Bai and Ruimao, Zhang and Jiong, Wang and Xiang, Wan},
 booktitle={Proceedings of the European conference on computer vision (ECCV)},
 year={2022}
 }