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Found 6 papers, 3 papers with code
EAGLE: Enhanced Visual Grounding Minimizes Hallucinations in Instructional Multimodal Models
Our approach, named EAGLE, is fully agnostic to the LLM or fusion module and works as a post-pretraining approach that improves the grounding and language alignment of the visual encoder.
PIVOT: Prompting for Video Continual Learning
In this paper, we address the problem of continual learning for video data.
vCLIMB: A Novel Video Class Incremental Learning Benchmark
We perform in-depth evaluations of existing CL methods in vCLIMB, and observe two unique challenges in video data.
MAD: A Scalable Dataset for Language Grounding in Videos from Movie Audio Descriptions
The recent and increasing interest in video-language research has driven the development of large-scale datasets that enable data-intensive machine learning techniques.
Ranked #4 on
Natural Language Moment Retrieval
on MAD
MovieCuts: A New Dataset and Benchmark for Cut Type Recognition
Advances in automatic Cut-type recognition can unleash new experiences in the video editing industry, such as movie analysis for education, video re-editing, virtual cinematography, machine-assisted trailer generation, machine-assisted video editing, among others.
Learning to Cut by Watching Movies
Video content creation keeps growing at an incredible pace; yet, creating engaging stories remains challenging and requires non-trivial video editing expertise.
