Methods › Computer Vision › Video-Text Retrieval Models › CAMoE

CAMoE

2 papers tagged archive 2025-07-28

Introduced by Xing Cheng et al. in Improving Video-Text Retrieval by Multi-Stream Corpus Alignment and Dual Softmax Loss

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

CAMoE is a multi-stream Corpus Alignment network with single gate Mixture-of-Experts (MoE) for video-text retrieval. The CAMoE employs Mixture-of-Experts (MoE) to extract multi-perspective video representations, including action, entity, scene, etc., then align them with the corresponding part of the text. A Dual Softmax Loss (DSL) is used to avoid the one-way optimum-match which occurs in previous contrastive methods. Introducing the intrinsic prior of each pair in a batch, DSL serves as a reviser to correct the similarity matrix and achieves the dual optimal match.

PaperSource

Papers archive 2025-07-28

2 shown of 2, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

7 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Mixture-of-Experts2
Click-Through Rate Prediction1
Multi-Task Learning1
Retrieval1
Text Retrieval1
Video Retrieval1
Video-Text Retrieval1

Usage over time archive 2025-07-28

Papers per year tagged with CAMoE: 2021 to 2025, peak 1 1 0 2021: 1 paper 2021 2022: 0 papers 2022 2023: 0 papers 2023 2024: 0 papers 2024 2025: 1 paper 2025
Papers per year the archive tags with this method, by the paper's archive date (2 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Video-Text Retrieval Models

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