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CONDA

Code release for the ECCV 2024 paper "CONDA: Condensed Deep Association Learning for Co-Salient Object Detection" by Long Li, Junwei Han*, Dingwen Zhang, Zhongyu Li, Salman Khan, Rao Anwer, Hisham Cholakkal, Nian Liu*, Fahad Shahbaz Khan

Abstract

Inter-image association modeling is crucial for co-salient object detection. Despite satisfactory performance, previous methods still have limitations on sufficient inter-image association modeling. Because most of them focus on image feature optimization under the guidance of heuristically calculated raw inter-image associations. They directly rely on raw associations which are not reliable in complex scenarios, and their image feature optimization approach is not explicit for inter-image association modeling. To alleviate these limitations, this paper proposes a deep association learning strategy that deploys deep networks on raw associations to explicitly transform them into deep association features. Specifically, we first create hyperassociations to collect dense pixel-pair-wise raw associations and then deploys deep aggregation networks on them. We design a progressive association generation module for this purpose with additional enhancement of the hyperassociation calculation. More importantly, we propose a correspondence-induced association condensation module that introduces a pretext task, i.e. semantic correspondence estimation, to condense the hyperassociations for computational burden reduction and noise elimination. We also design an object-aware cycle consistency loss for high-quality correspondence estimations. Experimental results in three benchmark datasets demonstrate the remarkable effectiveness of our proposed method with various training settings.

Result

The prediction results of our dataset can be download from prediction (g5hd).

Environment Configuration

• Linux with Python ≥ 3.6
• PyTorch ≥ 1.7 and torchvision that matches the PyTorch installation. Install them together at pytorch.org to make sure of this. Note, please check PyTorch version matches that is required by Detectron2.
• Detectron2: follow Detectron2 installation instructions.
• OpenCV is optional but needed by demo and visualization
• pip install -r requirements.txt

Data Preparation

Download the dataset from Baidu Driver (cxx2) and unzip them to './dataset'. Then the structure of the './dataset' folder will show as following:

-- dataset
   |-- train_data
   |   |-- | CoCo9k
   |   |-- | DUTS_class
   |   |-- | DUTS_class_syn
   |   |-- |-- | img_png_seamless_cloning_add_naive
   |   |-- |-- | img_png_seamless_cloning_add_naive_reverse_2
   |-- test_data
   |   |-- | CoCA
   |   |-- | CoSal2015
   |   |-- | CoSOD3k

Training model

1. Download the pretrained VGG model from Baidu Driver(sqd5) and put it into ./checkpoints folder.
2. Run python train.py.
3. The trained models with satisfactory performance will be saved in ./checkpoints/CONDA/.

Testing model

1. Download our trained model from DUTS+CoCo9k (qxye) or DUTS+CoCoSeg (1qiy) and put them into ./checkpoint/CVPR2023_Final_Code folder.
2. Run python test.py.
3. The prediction images will be saved in ./prediction.
4. Run python ./evaluation/eval_from_imgs.py to evaluate the predicted results on three datasets and the evaluation scores will be written in ./evaluation/result.