Transitive Invariance for Self-supervised Visual Representation Learning (1708.02901v3)

Abstract: Learning visual representations with self-supervised learning has become popular in computer vision. The idea is to design auxiliary tasks where labels are free to obtain. Most of these tasks end up providing data to learn specific kinds of invariance useful for recognition. In this paper, we propose to exploit different self-supervised approaches to learn representations invariant to (i) inter-instance variations (two objects in the same class should have similar features) and (ii) intra-instance variations (viewpoint, pose, deformations, illumination, etc). Instead of combining two approaches with multi-task learning, we argue to organize and reason the data with multiple variations. Specifically, we propose to generate a graph with millions of objects mined from hundreds of thousands of videos. The objects are connected by two types of edges which correspond to two types of invariance: "different instances but a similar viewpoint and category" and "different viewpoints of the same instance". By applying simple transitivity on the graph with these edges, we can obtain pairs of images exhibiting richer visual invariance. We use this data to train a Triplet-Siamese network with VGG16 as the base architecture and apply the learned representations to different recognition tasks. For object detection, we achieve 63.2% mAP on PASCAL VOC 2007 using Fast R-CNN (compare to 67.3% with ImageNet pre-training). For the challenging COCO dataset, our method is surprisingly close (23.5%) to the ImageNet-supervised counterpart (24.4%) using the Faster R-CNN framework. We also show that our network can perform significantly better than the ImageNet network in the surface normal estimation task.

• The paper presents a graph-based self-supervised method that uses transitive inference to capture both inter-instance and intra-instance invariances.
• It employs a Triplet-Siamese network with VGG16, achieving a mAP of 63.2% on PASCAL VOC 2007, nearly matching supervised ImageNet results.
• The approach also improves tasks like surface normal estimation, demonstrating its versatility and potential for reducing reliance on annotated data.

Transitive Invariance for Self-supervised Visual Representation Learning

The paper "Transitive Invariance for Self-supervised Visual Representation Learning" presents a novel strategy for leveraging self-supervised learning techniques to enhance visual representation learning. In the field of computer vision, self-supervised learning has gained traction due to its ability to harness freely available labels through auxiliary tasks. The essence of this paper is to achieve visual representations that exhibit invariance to both inter-instance and intra-instance variations without resorting to multi-task learning. This allows for a comprehensive organization and reasoning of data variations.

The paper introduces a graph-based approach that associates millions of objects extracted from extensive video databases via transitive inference. The cornerstone of this system is the construction of a graph where nodes signify objects, linked by two types of edges that embody distinct forms of invariance: inter-instance edges, which connect different instances sharing similar viewpoints within the same category, and intra-instance edges, linking multiple views of the same object instance.

By exploiting transitive relations within this graph, the authors derive pairs of images that encapsulate intricate visual invariances, ultimately feeding these pairs into a Triplet-Siamese network. They utilize VGG16 as the foundational architecture to process the graph-derived data, generating visual representations that transfer effectively to various recognition tasks.

Numerical results from object detection tasks demonstrate the competitive nature of the proposed method relative to well-established baselines. Specifically, the authors achieved a mean Average Precision (mAP) of 63.2% on the PASCAL VOC 2007 set with Fast R-CNN when their model was pre-trained on self-supervised data. This result nears the performance obtained with models pre-trained on supervised ImageNet data (67.3% mAP), showcasing the robustness of the self-supervised approach. Furthermore, when evaluated on the COCO dataset, the performance of the self-supervised model (23.5% AP) approached that of its ImageNet-supervised counterpart (24.4% AP), indicating that their method is effective even in challenging scenarios.

Additionally, the authors apply their representations to surface normal estimation tasks, observing superior performance compared to models pre-trained on ImageNet. This suggests that their approach not only excels in object recognition tasks but also offers advantages for certain low-level vision tasks due to its emphasis on capturing a broad range of invariances.

Theoretical implications include an improved understanding of how data organization and transitive reasoning can foster richer invariance in learned representations, potentially influencing future research directions in self-supervised learning. Practically, this work opens avenues for reduced reliance on costly annotated datasets in tasks where capturing both intra-instance and inter-instance variability is critical.

Future research could focus on extending the graph-based transitive approach to other domains within artificial intelligence, investigating how similar principles could benefit areas like natural language processing or autonomous systems, where invariance understanding is crucial. Moreover, exploring the integration of multi-modal data within this framework could enhance the richness of learned representations even further.

In conclusion, this paper presents a compelling approach to self-supervised learning that bridges the gap between existing methodologies and the need for richer, more invariant visual representations. It exemplifies how computational organization and relational inference in data can play pivotal roles in advancing self-supervised learning paradigms.