End-to-End Object Detection with Transformers

Paper Overview

This paper introduces DETR (DEtection TRansformer), a groundbreaking end-to-end object detection framework that leverages the power of Transformers to directly predict a set of object bounding boxes. Unlike traditional object detection methods that rely on hand-designed components like anchor boxes and non-maximum suppression (NMS), DETR simplifies the detection pipeline by using a set-based prediction approach. This allows DETR to achieve competitive performance with state-of-the-art detectors while offering a more elegant and streamlined architecture.

Key Contributions

1. DETR Architecture:

   • CNN Backbone: DETR utilizes a convolutional neural network (CNN) backbone to extract image features.
   • Transformer Encoder-Decoder: The extracted features are then fed into a Transformer encoder-decoder architecture.
     • Encoder: The encoder processes the image features globally, capturing long-range dependencies.
     • Decoder: The decoder attends to the encoded image features and uses learned object queries to directly predict a set of object bounding boxes and class labels.
   • Bipartite Matching: A bipartite matching algorithm is used to assign predicted boxes to ground-truth objects, enabling set-based supervision during training.
   • Feed-Forward Networks: Small feed-forward networks (FFNs) are used to predict the bounding box coordinates and class labels for each object.

2. End-to-End Training:

   • DETR is trained end-to-end with a set prediction loss that combines a classification loss and a bounding box regression loss.
   • This eliminates the need for hand-designed components like anchor boxes and NMS, simplifying the training process and reducing hyperparameter tuning.

3. Performance and Analysis:

   • The paper evaluates DETR on the COCO object detection benchmark, demonstrating competitive performance with state-of-the-art detectors.
   • Strengths: DETR excels at detecting large objects and exhibits good generalization capabilities.
   • Limitations: DETR struggles with detecting small objects and requires longer training times compared to some traditional detectors.

Conclusion

This paper introduces DETR, a novel end-to-end object detection framework that leverages Transformers to simplify the detection pipeline and achieve competitive performance. By eliminating hand-designed components and using a set-based prediction approach, DETR offers a more elegant and streamlined architecture. While DETR has limitations in detecting small objects, its strong performance on large objects and its end-to-end trainability make it a significant contribution to the field of object detection. This work opens up new avenues for research in object detection, demonstrating the potential of Transformers to revolutionize computer vision tasks.