About
The goal of automatic Video Description is to tell a story about events happening in a video. While early Video Description methods produced captions for short clips that were manually segmented to contain a single event of interest, more recently dense video captioning has been proposed to both segment distinct events in time and describe them in a series of coherent sentences. This problem is a generalization of dense image region captioning and has many practical applications, such as generating textual summaries for the visually impaired, or detecting and describing important events in surveillance footage.
Source: Joint Event Detection and Description in Continuous Video Streams
Benchmarks
Greatest papers with code
VizSeq: A Visual Analysis Toolkit for Text Generation Tasks
Automatic evaluation of text generation tasks (e. g. machine translation, text summarization, image captioning and video description) usually relies heavily on task-specific metrics, such as BLEU and ROUGE.
IMAGE CAPTIONING MACHINE TRANSLATION TEXT GENERATION TEXT SUMMARIZATION VIDEO DESCRIPTION
Describing Videos by Exploiting Temporal Structure
In this context, we propose an approach that successfully takes into account both the local and global temporal structure of videos to produce descriptions.
Grounded Video Description
CVPR 2019
• facebookresearch/grounded-video-description
• 
Our dataset, ActivityNet-Entities, augments the challenging ActivityNet Captions dataset with 158k bounding box annotations, each grounding a noun phrase.
TGIF: A New Dataset and Benchmark on Animated GIF Description
The motivation for this work is to develop a testbed for image sequence description systems, where the task is to generate natural language descriptions for animated GIFs or video clips.
IMAGE CAPTIONING MACHINE TRANSLATION TEXT GENERATION VIDEO DESCRIPTION
Video Description using Bidirectional Recurrent Neural Networks
Although traditionally used in the machine translation field, the encoder-decoder framework has been recently applied for the generation of video and image descriptions.
Predicting Visual Features from Text for Image and Video Caption Retrieval
• danieljf24/w2vv
• 
This paper strives to find amidst a set of sentences the one best describing the content of a given image or video.
End-to-End Audio Visual Scene-Aware Dialog using Multimodal Attention-Based Video Features
• hudaAlamri/DSTC7-Audio-Visual-Scene-Aware-Dialog-AVSD-Challenge
•
We introduce a new dataset of dialogs about videos of human behaviors.
QUESTION ANSWERING VIDEO DESCRIPTION VISUAL QUESTION ANSWERING
Audio Visual Scene-Aware Dialog (AVSD) Challenge at DSTC7
• hudaAlamri/DSTC7-Audio-Visual-Scene-Aware-Dialog-AVSD-Challenge
•
Scene-aware dialog systems will be able to have conversations with users about the objects and events around them.
VATEX: A Large-Scale, High-Quality Multilingual Dataset for Video-and-Language Research
ICCV 2019
• eric-xw/Video-guided-Machine-Translation
•
We also introduce two tasks for video-and-language research based on VATEX: (1) Multilingual Video Captioning, aimed at describing a video in various languages with a compact unified captioning model, and (2) Video-guided Machine Translation, to translate a source language description into the target language using the video information as additional spatiotemporal context.
Adversarial Inference for Multi-Sentence Video Description
CVPR 2019
• jamespark3922/adv-inf
•
Among the main issues are the fluency and coherence of the generated descriptions, and their relevance to the video.