https://github.com/L-YeZhu/Video-Description-via-Dialog-Agents-ECCV2020
공식 코드Describing Unseen Videos via Multi-Modal Cooperative Dialog Agents
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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
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Describing Unseen Videos via Multi-Modal Cooperative Dialog Agents
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StoryTeller: Improving Long Video Description through Global Audio-Visual Character Identification
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VATEX: A Large-Scale, High-Quality Multilingual Dataset for Video-and-Language Research
VATEX: A Large-Scale, High-Quality Multilingual Dataset for Video-and-Language Research
VATEX: A Large-Scale, High-Quality Multilingual Dataset for Video-and-Language Research
Improving LSTM-based Video Description with Linguistic Knowledge Mined from Text
Improving LSTM-based Video Description with Linguistic Knowledge Mined from Text
Improving LSTM-based Video Description with Linguistic Knowledge Mined from Text
End-to-End Audio Visual Scene-Aware Dialog using Multimodal Attention-Based Video Features
VATEX: A Large-Scale, High-Quality Multilingual Dataset for Video-and-Language Research
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.