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Video Description

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

Papers

Showing 125 of 104 papers

TitleStatusHype
HOIGen-1M: A Large-scale Dataset for Human-Object Interaction Video Generation0
DANTE-AD: Dual-Vision Attention Network for Long-Term Audio Description0
Cross-Modal Learning for Music-to-Music-Video Description Generation0
VideoA11y: Method and Dataset for Accessible Video Description0
AVD2: Accident Video Diffusion for Accident Video Description0
Enhancing Video Understanding: Deep Neural Networks for Spatiotemporal Analysis0
Towards Zero-Shot & Explainable Video Description by Reasoning over Graphs of Events in Space and Time0
Tarsier2: Advancing Large Vision-Language Models from Detailed Video Description to Comprehensive Video UnderstandingCode4
Implicit Location-Caption Alignment via Complementary Masking for Weakly-Supervised Dense Video CaptioningCode0
StoryTeller: Improving Long Video Description through Global Audio-Visual Character IdentificationCode2
PV-VTT: A Privacy-Centric Dataset for Mission-Specific Anomaly Detection and Natural Language Interpretation0
FIOVA: A Multi-Annotator Benchmark for Human-Aligned Video Captioning0
VideoCLIP-XL: Advancing Long Description Understanding for Video CLIP Models0
Technical Report: Competition Solution For Modelscope-Sora0
Kubrick: Multimodal Agent Collaborations for Synthetic Video Generation0
SUSTechGAN: Image Generation for Object Detection in Adverse Conditions of Autonomous DrivingCode0
https://arxiv.org/abs/2407.00634Code0
Tarsier: Recipes for Training and Evaluating Large Video Description ModelsCode4
LLAVIDAL: A Large LAnguage VIsion Model for Daily Activities of Living0
A Labelled Dataset for Sentiment Analysis of Videos on YouTube, TikTok, and Other Sources about the 2024 Outbreak of Measles0
Hawk: Learning to Understand Open-World Video AnomaliesCode3
TrafficVLM: A Controllable Visual Language Model for Traffic Video CaptioningCode2
X-VARS: Introducing Explainability in Football Refereeing with Multi-Modal Large Language Model0
JMI at SemEval 2024 Task 3: Two-step approach for multimodal ECAC using in-context learning with GPT and instruction-tuned Llama modelsCode0
Panda-70M: Captioning 70M Videos with Multiple Cross-Modality TeachersCode4
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