Making a Case for 3D Convolutions for Object Segmentation in Videos

arXiv (Cornell University)

Abstract

The task of object segmentation in videos is usually accomplished by processing appearance and motion information separately using standard 2D convolutional networks, followed by a learned fusion of the two sources of information. On the other hand, 3D convolutional networks have been successfully applied for video classification tasks, but have not been leveraged as effectively to problems involving dense per-pixel interpretation of videos compared to their 2D convolutional counterparts and lag behind the aforementioned networks in terms of performance. In this work, we show that 3D CNNs can be effectively applied to dense video prediction tasks such as salient object segmentation. We propose a simple yet effective encoder-decoder network architecture consisting entirely of 3D convolutions that can be trained end-to-end using a standard cross-entropy loss. To this end, we leverage an efficient 3D encoder, and propose a 3D decoder architecture, that comprises novel 3D Global Convolution layers and 3D Refinement modules. Our approach outperforms existing state-of-the-arts by a large margin on the DAVIS'16 Unsupervised, FBMS and ViSal dataset benchmarks in addition to being faster, thus showing that our architecture can efficiently learn expressive spatio-temporal features and produce high quality video segmentation masks. We have made our code and trained models publicly available at https://github.com/sabarim/3DC-Seg.

Authors 6

1. Sabarinath Mahadevan Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University Aachen , Germany

   RWTH Aachen University, Aachen, Germany

2. Ali Athar Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University Aachen , Germany

   RWTH Aachen University, Aachen, Germany

3. Aljoša Ošep

   Technical University of Munich

   Affiliation as printed

   Technical University of Munich Munich , Germany

   Technical University of Munich, Munich, Germany

4. Sebastian Hennen Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University Aachen , Germany

   RWTH Aachen University, Aachen, Germany

5. Laura Leal-Taixé

   Technical University of Munich

   Affiliation as printed

   Technical University of Munich Munich , Germany

   Technical University of Munich, Munich, Germany

6. Bastian Leibe Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University Aachen , Germany

   RWTH Aachen University, Aachen, Germany