Actor and Action Video Segmentation from a Sentence

Abstract

This paper strives for pixel-level segmentation of actors and their actions in video content. Different from existing works, which all learn to segment from a fixed vocabulary of actor and action pairs, we infer the segmentation from a natural language input sentence. This allows to distinguish between fine-grained actors in the same super-category, identify actor and action instances, and segment pairs that are outside of the actor and action vocabulary. We propose a fully-convolutional model for pixel-level actor and action segmentation using an encoder-decoder architecture optimized for video. To show the potential of actor and action video segmentation from a sentence, we extend two popular actor and action datasets with more than 7,500 natural language descriptions. Experiments demonstrate the quality of the sentence-guided segmentations, the generalization ability of our model, and its advantage for traditional actor and action segmentation compared to the state-of-the-art.

Publication
in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, USA, 2018.
Date
Links

Datasets

A2D Sentences

We have extended Actor and Action (A2D) Dataset with additional description of every object is doing in the videos. We provide three files containing our annotation:

  1. a2d_annotation.txt contains annotation in the format “video_id,instance_id,query” where:

    • “video_id” - the original id of the video from the A2D dataset

    • “instance_id” - the id of the object in the video that we have added to the original annotation

    • “query” - the description of what object is doing throughout the whole video (see the paper for more details)

  2. a2d_annotation_with_instances.zip - the original annotation from the A2D dataset in HDF5 with the field “instance” added. This field corresponds to “instance_id” field in the a2d_annotation.txt file.

  3. a2d_missed_videos.txt contains all the videos that were not annotated with descriptions and therefore were excluded from experiments in the paper.

J-HMDB Sentences

We have extended J-HMDB Dataset with additional description of every human is doing in the videos:

  1. jhmdb_annotation.txt contains annotation in the format “video_id,query”:

    • “video_id” - the original id of the video from the J-HMDB dataset

    • “query” - the description of what human is doing throughout the whole video (see the paper for more details)

Code

The code will be available after CVPR 2018 conference.