211 research outputs found
Describing Videos by Exploiting Temporal Structure
Recent progress in using recurrent neural networks (RNNs) for image
description has motivated the exploration of their application for video
description. However, while images are static, working with videos requires
modeling their dynamic temporal structure and then properly integrating that
information into a natural language description. In this context, we propose an
approach that successfully takes into account both the local and global
temporal structure of videos to produce descriptions. First, our approach
incorporates a spatial temporal 3-D convolutional neural network (3-D CNN)
representation of the short temporal dynamics. The 3-D CNN representation is
trained on video action recognition tasks, so as to produce a representation
that is tuned to human motion and behavior. Second we propose a temporal
attention mechanism that allows to go beyond local temporal modeling and learns
to automatically select the most relevant temporal segments given the
text-generating RNN. Our approach exceeds the current state-of-art for both
BLEU and METEOR metrics on the Youtube2Text dataset. We also present results on
a new, larger and more challenging dataset of paired video and natural language
descriptions.Comment: Accepted to ICCV15. This version comes with code release and
supplementary materia
Temporal activity detection in untrimmed videos with recurrent neural networks
This work proposes a simple pipeline to classify and temporally localize activities in untrimmed videos. Our system uses features from a 3D Convolutional Neural Network (C3D) as input to train a a recurrent neural network (RNN) that learns to classify video clips of 16 frames. After clip prediction, we post-process the output of the RNN to assign a single activity label to each video, and determine the temporal boundaries of the activity within the video. We show how our system can achieve competitive results in both tasks with a simple architecture. We evaluate our method in the ActivityNet Challenge 2016, achieving a 0.5874 mAP and a 0.2237 mAP in the classification and detection tasks, respectively. Our code and models are publicly available at: https://imatge-upc.github.io/activitynet-2016-cvprw/Peer ReviewedPostprint (published version
Hierarchically-Attentive RNN for Album Summarization and Storytelling
We address the problem of end-to-end visual storytelling. Given a photo
album, our model first selects the most representative (summary) photos, and
then composes a natural language story for the album. For this task, we make
use of the Visual Storytelling dataset and a model composed of three
hierarchically-attentive Recurrent Neural Nets (RNNs) to: encode the album
photos, select representative (summary) photos, and compose the story.
Automatic and human evaluations show our model achieves better performance on
selection, generation, and retrieval than baselines.Comment: To appear at EMNLP-2017 (7 pages
A Neural, Interactive-predictive System for Multimodal Sequence to Sequence Tasks
We present a demonstration of a neural interactive-predictive system for
tackling multimodal sequence to sequence tasks. The system generates text
predictions to different sequence to sequence tasks: machine translation, image
and video captioning. These predictions are revised by a human agent, who
introduces corrections in the form of characters. The system reacts to each
correction, providing alternative hypotheses, compelling with the feedback
provided by the user. The final objective is to reduce the human effort
required during this correction process.
This system is implemented following a client-server architecture. For
accessing the system, we developed a website, which communicates with the
neural model, hosted in a local server. From this website, the different tasks
can be tackled following the interactive-predictive framework. We open-source
all the code developed for building this system. The demonstration in hosted in
http://casmacat.prhlt.upv.es/interactive-seq2seq.Comment: ACL 2019 - System demonstration
- …