Long-Term Image Boundary Prediction

By Apratim Bhattacharyya, Mateusz Malinowski, Bernt Schiele, Mario Fritz. 2016

15 citations recorded by the source corpus.

Boundary estimation in images and videos has been a very active topic of research, and organizing visual information into boundaries and segments is believed to be a corner stone of visual perception. While prior work has focused on estimating boundaries for observed frames, our work aims at predicting boundaries of future unobserved frames. This requires our model to learn about the fate of boundaries and corresponding motion patterns---including a notion of "intuitive physics." We experiment on natural video sequences along with synthetic sequences with deterministic physics-based and agent-based motions. While not being our primary goal, we also show that fusion of RGB and boundary prediction leads to improved RGB predictions.

Subfields: Computer Vision and Pattern Recognition

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In Computer Vision and Pattern Recognition

Citations in the corpus

Structured agents for physical construction (2019, background): “Modern advances in learning-based approaches have opened new avenues for using vector and convolutional representations for physical reasoning (Wu et al., 2015; 2016; 2017; Mottaghi et al., 2016; Fragkiadaki et al., 2016; Finn et al., 2016; Agrawal et al., 2016; Lerer et al., 2016; Li et al., 2016; ”
r2p2: A ReparameteRized Pushforward Policy for Diverse, Precise Generative Path Forecasting (2018, unknown): “In [7], image boundaries are predicted.”
Analogues of mental simulation and imagination in deep learning (2019, unknown): “motion; [41] predict image masks indicating how a tower of blocks will fall; and [42] predict the boundaries of objects in images.”