Localizing Objects with Self-Supervised Transformers and no Labels (2109.14279v1)

Abstract: Localizing objects in image collections without supervision can help to avoid expensive annotation campaigns. We propose a simple approach to this problem, that leverages the activation features of a vision transformer pre-trained in a self-supervised manner. Our method, LOST, does not require any external object proposal nor any exploration of the image collection; it operates on a single image. Yet, we outperform state-of-the-art object discovery methods by up to 8 CorLoc points on PASCAL VOC 2012. We also show that training a class-agnostic detector on the discovered objects boosts results by another 7 points. Moreover, we show promising results on the unsupervised object discovery task. The code to reproduce our results can be found at https://github.com/valeoai/LOST.

• The paper presents LOST, a method that localizes objects using self-supervised transformer features without requiring any human annotations.
• It achieves linear complexity by analyzing patch similarities within single images, improving the Correct Localization metric by up to 8 points on PASCAL VOC 2012.
• The approach enables unsupervised, class-aware detection via clustering, paving the way for scalable and practical visual recognition applications.

Localizing Objects with Self-Supervised Transformers and No Labels

The paper introduces a novel, simple, and efficient methodology to localize objects within image collections without requiring any form of human annotations. Leveraging the advancements in vision transformers, particularly those pre-trained using self-supervised methods like DINO, this approach is both computationally effective and scalable. The method, referred to as LOST, is designed to operate by utilizing the features derived from vision transformers to discern foreground objects from the background within individual images, subsequently using these localized objects to enhance detection capabilities.

Key Contributions and Methodology

1. Self-supervised Features for Localization: The paper capitalizes on the learned feature representations of transformers trained with self-supervised learning approaches. By evaluating patch similarities within an image and identifying salient regions with minimal similarity to background areas, LOST effectively localizes objects. This strategy avoids the exponential complexity typically associated with inter-image similarity evaluations.
2. Efficient and Scalable Process: The approach maintains linear complexity by focusing on individual images without necessitating inter-image comparisons or external region proposals. This ensures the method is not only efficient but can be easily applied to larger datasets.
3. Performance Evaluation: The results underscore LOST's effectiveness, showcasing improvements over previous state-of-the-art methods in unsupervised object discovery. A crucial metric, the Correct Localization (CorLoc), shows improvements up to 8 points on the PASCAL VOC 2012 dataset. Training a subsequent class-agnostic detector using LOST's generated boxes further elevates the results by approximately 7 more points.
4. Unsupervised Class-Aware Detection: Beyond single-object localization, the research extends to training fully unsupervised object detectors capable of multiple object detection per image. Clustering techniques facilitate a pseudo-labeling strategy that effectively groups similar objects into coherent categories.

Numerical Performance and Observations

The methodology demonstrates substantial promise as reflected in the comprehensive set of experiments. Notably, LOST achieves significant performance metrics that underscore its potential for practical applications in scenarios where annotation is complex or infeasible. Noteworthy is the system's ability to match or surpass performance metrics traditionally achieved with weakly supervised methods, despite using no annotations.

Implications and Future Directions

The implications of this research are manifold, indicating potential enhancements for automated visual recognition pipelines, especially in applications like autonomous vehicles where real-time processing of unannotated video data is critical. Moreover, the reliance on transformer features, which exhibit less bias towards texture compared to CNNs, suggests the opportunity for further nuanced object detection and segmentation tasks.

Future explorations might focus on refining the scalability of LOST to even larger datasets, possibly integrating multi-modal data or adversarial approaches to bolster localization accuracy. The adaptive clustering component presents additional avenues for refining class-aware detection, especially in diverse visual contexts.

Ultimately, LOST exemplifies the convergence of self-supervised learning and advanced neural architectures, paving the way for a broader deployment of AI models that operate independently of human supervision, reshaping both theoretical and applied facets of object detection.