Learning to See by Moving (1505.01596v2)

Abstract: The dominant paradigm for feature learning in computer vision relies on training neural networks for the task of object recognition using millions of hand labelled images. Is it possible to learn useful features for a diverse set of visual tasks using any other form of supervision? In biology, living organisms developed the ability of visual perception for the purpose of moving and acting in the world. Drawing inspiration from this observation, in this work we investigate if the awareness of egomotion can be used as a supervisory signal for feature learning. As opposed to the knowledge of class labels, information about egomotion is freely available to mobile agents. We show that given the same number of training images, features learnt using egomotion as supervision compare favourably to features learnt using class-label as supervision on visual tasks of scene recognition, object recognition, visual odometry and keypoint matching.

• The paper demonstrates that egomotion is an effective supervisory signal, enabling robust visual feature learning with a Siamese CNN without relying on extensive labels.
• It achieves competitive results in tasks such as scene recognition, keypoint matching, and visual odometry across both synthetic and real-world datasets.
• The study highlights a promising direction for autonomous systems by reducing dependency on manual annotation through intrinsic motion cues.

Learning to See by Moving: An Overview

The paper "Learning to See by Moving" by Agrawal, Carreira, and Malik from UC Berkeley explores an innovative approach to feature learning in computer vision, focusing on the concept of egomotion-based supervision. Traditional methods rely heavily on training neural networks through extensive datasets of labeled images for tasks such as object recognition, which can be resource-intensive. This research investigates the potential of using egomotion—self-motion awareness—as an alternative form of supervision.

Core Concept and Methodology

The foundational premise of this paper is inspired by biological systems, where visual perception is closely intertwined with movement and interaction with the environment. Egomotion provides intrinsic supervisory signals that are naturally available to mobile agents, including both biological organisms and robotic systems. By leveraging egomotion, the authors propose that visual systems can be trained to learn robust features without the exclusive dependency on labeled data.

The research employs a Siamese-style Convolutional Neural Network (SCNN) architecture to predict camera transformations between pairs of images. This network learns visual features by correlating image pairs with corresponding egomotion data, essentially treating agents as cameras moving through a scene. The tasks of scene recognition, object recognition, visual odometry, and keypoint matching serve as benchmarks for evaluating the utility of features learned through this method.

Experimental Setup and Results

The authors validate their approach through a systematic experimental setup conducted on datasets such as MNIST for proof-of-concept and real-world imagery from the KITTI and SF datasets. The experiments compare egomotion-based feature learning against traditional class-label supervised learning and other unsupervised approaches like Slow Feature Analysis (SFA).

1. MNIST Dataset: For the synthetic setup on MNIST, egomotion-based pretrained features demonstrated superior performance compared to several unsupervised learning baselines, particularly in scenarios with limited labeled examples.
2. KITTI and SF Datasets: These datasets provided scenarios mirroring a camera-equipped agent moving through urban settings. The performance of features learned from egomotion-based pretraining was assessed across various vision tasks, showing competitive or superior results compared to networks trained with labeled datasets of similar size.
3. Scene Recognition: Evaluations on the SUN dataset indicated that egomotion-trained features were comparable to those trained with human-annotated labels, indicating the potential efficacy of this approach in practical applications.
4. Keypoint Matching: The approach excelled in intra-class keypoint matching tasks, achieving results superior to many traditionally supervised methods and aligning closely with expertly engineered features like SIFT.
5. Visual Odometry: When applied to the task of visual odometry, egomotion-based features matched or exceeded the performance of features developed through extensive supervised learning.

Implications and Future Directions

By providing the first effective demonstration of learning visual representations through egomotion, this work suggests a promising direction for reducing dependency on large-scale labeled datasets. The potential for applying this form of self-supervision in active vision scenarios, where agents continuously integrate egomotion with intermittent external cues, represents an intriguing avenue for future research.

The implications are significant for autonomous systems, where obtaining labeled data is often infeasible. This approach could facilitate more adaptive and resilient feature learning paradigms in real-world applications. Additionally, the consideration of more diverse and extensive datasets could further enhance the efficacy and generalization capabilities of egomotion-based learning methods.

In conclusion, the paper's exploration of egomotion as a supervisory signal in feature learning introduces a valuable perspective, challenging the notion that class-label based supervision is essential for robust visual feature extraction. As AI continues to evolve, leveraging intrinsic signals like egomotion could become a cornerstone for developing more autonomous and intelligent systems.