Papers by Zhiyi Zhou

3 papers
ORANGE: Text-video Retrieval via Watch-time-aware Heterogeneous Graph Contrastive Learning (2023.emnlp-industry)

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Challenge: Existing methods for text-video retrieval focus on informative representations and delicate matching mechanisms, but real-world scenarios often involve brief, ambiguous queries and low-quality videos.
Approach: They propose a novel method to learn informative embeddings for queries and videos . they use a watch-time-aware contrastive learning paradigm to capture dependencies .
Outcome: The proposed method is effective in a real-world video-search service.
Contextual Representation Learning beyond Masked Language Modeling (2022.acl-long)

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Challenge: masked language models adopt sampled embeddings as anchors to estimate and inject contextual semantics to representations.
Approach: They propose a representation learning approach that uses embeddings as anchors to model contextual representations.
Outcome: The proposed model achieves 5x speedup and 1.2 points average improvement over MLM.
Query-aware Multi-modal based Ranking Relevance in Video Search (2023.emnlp-industry)

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Challenge: Existing relevance ranking methods focus on text modality, incapable of fully exploiting cross-modal cues present in video.
Approach: They propose a QUery-Aware pre-training model with multi-modality that integrates video tag information as alignment targets and enhances ranking optimization method based on ordinal regression.
Outcome: The proposed model significantly improves video search performance.

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