Title: Midway Network: Learning Representations for Recognition and Motion from Latent Dynamics
Primary area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
Keywords: self-supervision learning, representation learning, latent dynamics, natural videos, object recognition, motion understanding, computer vision

Abstract: Object recognition and motion understanding are key components of perception that complement each other.
    While self-supervised learning methods have shown promise in their ability to learn from unlabeled data, they have primarily focused on obtaining rich representations for either recognition or motion rather than both in tandem.
    On the other hand, latent dynamics modeling has been used in decision making to learn latent representations of observations and their transformations over time for control and planning tasks.
    In this work, we present Midway Network, a new self-supervised learning architecture that is the first to learn strong visual representations for both object recognition and motion understanding solely from natural videos, by extending latent dynamics modeling to this domain.
    Midway Network leverages a _midway_ top-down path to infer motion latents between video frames, as well as a dense forward prediction objective and hierarchical structure to tackle the complex, multi-object scenes of natural videos.
    We demonstrate that after pretraining on two large-scale natural video datasets, Midway Network achieves strong performance on both semantic segmentation and optical flow tasks relative to prior self-supervised learning methods.
    We also show that Midway Network's learned dynamics can capture high-level correspondence via a novel analysis method based on forward feature perturbation.
    Code is provided at https://anonymous.4open.science/r/midway-network.

TL;DR: Midway Network is a new SSL architecture that extends latent dynamics modeling to natural videos to learn rich representations for object recognition and motion understanding.