Multi-Instance Retrieval
8 papers with code • 1 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in Multi-Instance Retrieval
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Libraries
Use these libraries to find Multi-Instance Retrieval models and implementations
EPIC-KITCHENS-100
Most implemented papers
Learning video retrieval models with relevance-aware online mining
aranciokov/ranp
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Due to the amount of videos and related captions uploaded every hour, deep learning-based solutions for cross-modal video retrieval are attracting more and more attention.
Egocentric Video-Language Pretraining
showlab/egovlp
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Video-Language Pretraining (VLP), which aims to learn transferable representation to advance a wide range of video-text downstream tasks, has recently received increasing attention.
Learning Video Representations from Large Language Models
facebookresearch/lavila
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CVPR 2023
We introduce LaViLa, a new approach to learning video-language representations by leveraging Large Language Models (LLMs).
Relevance-based Margin for Contrastively-trained Video Retrieval Models
aranciokov/relevancemargin-icmr22
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We show that even if we carefully tuned the fixed margin, our technique (which does not have the margin as a hyper-parameter) would still achieve better performance.
Exploiting Semantic Role Contextualized Video Features for Multi-Instance Text-Video Retrieval EPIC-KITCHENS-100 Multi-Instance Retrieval Challenge 2022
buraksatar/RoME_video_retrieval
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In this report, we present our approach for EPIC-KITCHENS-100 Multi-Instance Retrieval Challenge 2022.
Egocentric Video-Language Pretraining @ EPIC-KITCHENS-100 Multi-Instance Retrieval Challenge 2022
showlab/egovlp
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In this report, we propose a video-language pretraining (VLP) based solution \cite{kevin2022egovlp} for the EPIC-KITCHENS-100 Multi-Instance Retrieval (MIR) challenge.
EgoVLPv2: Egocentric Video-Language Pre-training with Fusion in the Backbone
facebookresearch/EgoVLPv2
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ICCV 2023
Video-language pre-training (VLP) has become increasingly important due to its ability to generalize to various vision and language tasks.
Training a Large Video Model on a Single Machine in a Day
Videos are big, complex to pre-process, and slow to train on.
