Browse State-of-the-Art › Multi-Instance Retrieval
Multi-Instance Retrieval
15 papers with code · 0 benchmarks · 1 dataset archive 2025-07-28
Benchmarks archive 2025-07-28
No benchmark for this task in the archive.
Libraries
Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.
Datasets archive 2025-07-28
1 dataset whose archive record lists this task, ordered by the archive's paper count.
Subtasks archive 2025-07-28
No subtask under this task in the archive's task tree.
Most implemented papers archive 2025-07-28
15 shown of 15 papers with code (19 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.
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8 Dec 2022 3 repositories listed Syntology ran 6 of 20 samples · 14 unverified · 20 pointer-only (licence)We introduce LaViLa, a new approach to learning video-language representations by leveraging Large Language Models (LLMs).
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3 Jun 2022 2 repositories listed Syntology ran 3 of 3 samples · 0 unverified · 2 pointer-only (licence)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.
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16 Mar 2022 2 repositories listedDue 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.
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17 Jun 2025 1 repository listedIn this paper, we introduce EVA02-AT, a suite of EVA02-based video-language foundation models tailored to egocentric video understanding tasks.
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12 Jun 2025 1 repository listedThis report presents ContextRefine-CLIP (CR-CLIP), an efficient model for visual-textual multi-instance retrieval tasks.
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2 Mar 2025 1 repository listed Syntology ran 2 of 5 samples · 3 unverifiedHowever, existing egocentric video representation learning methods mainly focus on aligning video representation with high-level narrations, overlooking the intricate dynamics between hands and objects.
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26 Jun 2024 1 repository listed Syntology ran 11 of 15 samples · 4 unverified · 15 pointer-only (licence)In this report, we present our solutions to the EgoVis Challenges in CVPR 2024, including five tracks in the Ego4D challenge and three tracks in the EPIC-Kitchens challenge.
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18 Jun 2024 1 repository listedIn this report, we present our champion solution for EPIC-KITCHENS-100 Multi-Instance Retrieval Challenge in CVPR 2024.
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28 May 2024 1 repository listed Syntology ran 5 of 5 samples · 0 unverified · 5 pointer-only (licence)Due to the occurrence of diverse EgoHOIs in the real world, we propose an open-vocabulary benchmark named EgoHOIBench to reveal the diminished performance of current egocentric video-language models (EgoVLM) on…
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28 Sep 2023 1 repository listedVideos are big, complex to pre-process, and slow to train on.
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11 Jul 2023 1 repository listedVideo-language pre-training (VLP) has become increasingly important due to its ability to generalize to various vision and language tasks.
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5 Jan 2023 1 repository listed Syntology ran 4 of 5 samples · 1 unverified · 5 pointer-only (licence)Video-language embeddings are a promising avenue for injecting semantics into visual representations, but existing methods capture only short-term associations between seconds-long video clips and their accompanying…
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4 Jul 2022 1 repository listedIn this report, we propose a video-language pretraining (VLP) based solution \cite{kevin2022egovlp} for the EPIC-KITCHENS-100 Multi-Instance Retrieval (MIR) challenge.
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29 Jun 2022 1 repository listedIn this report, we present our approach for EPIC-KITCHENS-100 Multi-Instance Retrieval Challenge 2022.
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27 Apr 2022 1 repository listed Syntology ran 4 of 5 samples · 1 unverifiedWe 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.
Syntology lines on 7 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.
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