End-to-End Joint Semantic Segmentation of Actors and Actions in Video

Jingwei Ji, Shyamal Buch, Alvaro Soto, Juan Carlos Niebles; The European Conference on Computer Vision (ECCV), 2018, pp. 702-717

Abstract


Traditional video understanding tasks include human action recognition and actor/object semantic segmentation. However, the combined task of providing semantic segmentation for different actor classes simultaneously with their action class remains a challenging but necessary task for many applications. In this work, we propose a new end-to-end architecture for tackling this task in videos. Our model effectively leverages multiple input modalities, contextual information, and multitask learning in the video to directly output semantic segmentations in a single unified framework. We train and benchmark our model on the Actor-Action Dataset (A2D) for joint actor-action semantic segmentation, and demonstrate state-of-the-art performance for both segmentation and detection. We also perform experiments verifying our approach improves performance for zero-shot recognition, indicating generalizability of our jointly learned feature space.

Related Material


[pdf]
[bibtex]
@InProceedings{Ji_2018_ECCV,
author = {Ji, Jingwei and Buch, Shyamal and Soto, Alvaro and Niebles, Juan Carlos},
title = {End-to-End Joint Semantic Segmentation of Actors and Actions in Video},
booktitle = {The European Conference on Computer Vision (ECCV)},
month = {September},
year = {2018}
}