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Found 12 papers, 6 papers with code
MatchDiffusion: Training-free Generation of Match-cuts
Match-cuts are powerful cinematic tools that create seamless transitions between scenes, delivering strong visual and metaphorical connections.
Generative Timelines for Instructed Visual Assembly
The objective of this work is to manipulate visual timelines (e. g. a video) through natural language instructions, making complex timeline editing tasks accessible to non-expert or potentially even disabled users.
Compressed-Language Models for Understanding Compressed File Formats: a JPEG Exploration
These results suggest that CLMs can understand the semantics of compressed data when directly operating on the byte streams of files produced by CFFs.
Combating Missing Modalities in Egocentric Videos at Test Time
Understanding videos that contain multiple modalities is crucial, especially in egocentric videos, where combining various sensory inputs significantly improves tasks like action recognition and moment localization.
Towards Automated Movie Trailer Generation
Movie trailers are an essential tool for promoting films and attracting audiences.
Exploring Missing Modality in Multimodal Egocentric Datasets
Multimodal video understanding is crucial for analyzing egocentric videos, where integrating multiple sensory signals significantly enhances action recognition and moment localization.
Evaluation of Test-Time Adaptation Under Computational Time Constraints
To address this issue, we propose a more realistic evaluation protocol for TTA methods, where data is received in an online fashion from a constant-speed data stream, thereby accounting for the method's adaptation speed.
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.
BAOD: Budget-Aware Object Detection
We adopt a hybrid supervised learning framework to train the object detector from both these types of annotation.
RefineLoc: Iterative Refinement for Weakly-Supervised Action Localization
RefineLoc shows competitive results with the state-of-the-art in weakly-supervised temporal localization.
Temporal Localization
Weakly Supervised Action Localization
+2
