Unsupervised learning from video to detect foreground objects in single images (1703.10901v1)

Abstract: Unsupervised learning from visual data is one of the most difficult challenges in computer vision, being a fundamental task for understanding how visual recognition works. From a practical point of view, learning from unsupervised visual input has an immense practical value, as very large quantities of unlabeled videos can be collected at low cost. In this paper, we address the task of unsupervised learning to detect and segment foreground objects in single images. We achieve our goal by training a student pathway, consisting of a deep neural network. It learns to predict from a single input image (a video frame) the output for that particular frame, of a teacher pathway that performs unsupervised object discovery in video. Our approach is different from the published literature that performs unsupervised discovery in videos or in collections of images at test time. We move the unsupervised discovery phase during the training stage, while at test time we apply the standard feed-forward processing along the student pathway. This has a dual benefit: firstly, it allows in principle unlimited possibilities of learning and generalization during training, while remaining very fast at testing. Secondly, the student not only becomes able to detect in single images significantly better than its unsupervised video discovery teacher, but it also achieves state of the art results on two important current benchmarks, YouTube Objects and Object Discovery datasets. Moreover, at test time, our system is at least two orders of magnitude faster than other previous methods.

Unsupervised Learning from Video to Detect Foreground Objects in Single Images

This paper addresses the challenging task of unsupervised learning to detect and segment foreground objects in single images, leveraging video data. The approach is rooted in the development of a dual student-teacher system, where the student pathway is a deep neural network that learns to predict the outputs of a teacher pathway. The teacher conducts unsupervised object discovery in video frames, providing outputs that guide the student network.

Methodology

The teaching strategy pivots on two complementary pathways:

1. Teacher Pathway: The unsupervised teacher utilizes a video-based discovery algorithm, specifically VideoPCA. This method capitalizes on temporal coherence in video data to identify foreground objects. VideoPCA operates efficiently (50-100 fps) with basic features like pixel colors and without supervised pre-trained features.
2. Student Pathway: The student network is a deeper convolutional model that processes single image frames. It is restricted to single image inputs, learning to mimic the segmentation patterns of the teacher via unsupervised labels provided by the video discovery phase.

This dual mechanism allows the student to surpass its teacher, producing improved object masks with enhanced form, fewer holes, and more consistent contours. The system is evaluated on the YouTube Objects and Object Discovery datasets, achieving state-of-the-art results. Importantly, the student's performance at test time allows it to operate at least two orders of magnitude faster than other existing methods.

Experimental Results

The paper demonstrates impressive quantitative outcomes, notably on benchmarks like YouTube Objects and Object Discovery datasets, with significant strides in both performance speed and mask quality:

• Speed: The student’s processing time drastically improves to 0.04 seconds per frame, contrasting the significantly longer times required by traditional methods.
• Performance: Through the Object Discovery in Internet Images dataset and YouTube Objects dataset, the student network outperformed its teacher path and other state-of-the-art methods, marking an advancement in unsupervised single-image object detection.

Implications and Future Work

The implications of this research extend to robust applications in computer vision and robotics, where unsupervised learning methodologies could reduce dependency on labeled data, thereby diminishing associated costs in these domains. The dual-pathway model exhibits a capacity to learn generalized object features effectively, which is a promising direction for autonomous systems requiring visual recognition capabilities without extensive datasets.

Looking forward, the potential scaling of unsupervised models in different contexts and the exploration of more intricate video analysis techniques are intriguing avenues. Further research into optimizing the student network’s architecture and exploring the interaction between temporal video data and single-frame processing could enhance the learning and generalization abilities of such systems. Emphasizing real-world tests and expanding datasets could lead to a broader application across diverse and dynamic environments.

The approach this paper proposes adds an important dimension to the unsupervised learning paradigm, emphasizing how video data can contribute to single-image processing tasks and suggesting pathways for more widespread adoption and refinement in AI systems.