MViTs Excel at Class-agnostic Object Detection

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Multi-modal Vision Transformers Excel at Class-agnostic Object Detection

Muhammad Maaz, Hanoona Rasheed, Salman Khan, Fahad Shahbaz Khan, Rao Muhammad Anwer and Ming-Hsuan Yang

Paper: https://arxiv.org/abs/2111.11430

? News

  • (Feb 01, 2022)
    • Training codes for MDef-DETR and MDef-DETR minus Language models are released -> training/README.md
    • Instructions to use class-agnostic object detection behavior of MDef-DETR on different applications are released -> applications/README.md
    • All the pretrained models (MDef-DETR, Def-DETR, MDETR, DETReg, Faster-RCNN, RetinaNet, ORE, and others), along with the instructions to reproduce the results are released -> this link
  • (Nov 25, 2021) Evaluation code along with pre-trained models & pre-computed predictions is released. evaluation/README.md

main figure

Abstract: What constitutes an object? This has been a long-standing question in computer vision. Towards this goal, numerous learning-free and learning-based approaches have been developed to score objectness. However, they generally do not scale well across new domains and for unseen objects. In this paper, we advocate that existing methods lack a top-down supervision signal governed by human-understandable semantics. To bridge this gap, we explore recent Multi-modal Vision Transformers (MViT) that have been trained with aligned image-text pairs. Our extensive experiments across various domains and novel objects show the state-of-the-art performance of MViTs to localize generic objects in images. Based on these findings, we develop an efficient and flexible MViT architecture using multi-scale feature processing and deformable self-attention that can adaptively generate proposals given a specific language query. We show the significance of MViT proposals in a diverse range of applications including open-world object detection, salient and camouflage object detection, supervised and self-supervised detection tasks. Further, MViTs offer enhanced interactability with intelligible text queries.


Architecture overview of MViTs used in this work

Architecture overview


Installation

The code is tested with PyTorch 1.8.0 and CUDA 11.1. After cloning the repository, follow the below steps for installation,

  1. Install PyTorch and torchvision
pip install torch==1.8.0+cu111 torchvision==0.9.0+cu111 -f https://download.pytorch.org/whl/torch_stable.html
  1. Install other dependencies
pip install -r requirements.txt
  1. Compile Deformable Attention modules

cd models/ops
sh make.sh

Results


Class-agnostic OD performance of MViTs in comparison with uni-modal detector (RetinaNet) on several datasets. MViTs show consistently good results on all datasets.

Results


Enhanced Interactability: Effect of using different intuitive text queries on the MDef-DETR class-agnostic OD performance. Combining detections from multiple queries captures varying aspects of objectness.

Results


Language Skeleton/Structure: Experimental analysis to explore the contribution of language by removing all textual inputs, but maintaining the structure introduced by captions. All experiments are performed on Def-DETR. In setting 1, annotations corresponding to same images are combined. Setting 2 has an additional NMS applied to remove duplicate boxes. In setting 3, four to eight boxes are randomly grouped in each iteration. The same model is trained longer in setting 4. In setting 5, the dataloader structure corresponding to captions is kept intact. Results from setting 5 demonstrate the importance of structure introduced by language.

Results


Generalization to Rare/Novel Classes: MDef-DETR class-agnostic OD performance on rarely and frequently occurring categories in the pretraining captions. The numbers on top of the bars indicate occurrences of the corresponding category in the training dataset. The MViT achieves good recall values even for the classes with no or very few occurrences.

Results


Open-world Object Detection: Effect of using class-agnostic OD proposals from MDef-DETR for pseudo labelling of unknowns in Open World Detector (ORE).

Results


Pretraining for Class-aware Object Detection: Effect of using MDef-DETR proposals for pre-training of DETReg instead of Selective Search proposals.

Results


Evaluation

Please refer to evaluation/class_agnostic_od/README.md.


Training

Please refer to training/README.md.

Applications

Please refer to applications/README.md.

Citation

If you use our work, please consider citing:

    @article{Maaz2021Multimodal,
        title={Multi-modal Transformers Excel at Class-agnostic Object Detection},
        author={Muhammad Maaz and Hanoona Rasheed and Salman Khan and Fahad Shahbaz Khan and Rao Muhammad Anwer and Ming-Hsuan Yang},
        journal={ArXiv 2111.11430},
        year={2021}
    }

Contact

Should you have any question, please create an issue on this repository or contact at [email protected], [email protected]

GitHub

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