Search Results for author:
Found 5 papers, 3 papers with code
SHE-Net: Syntax-Hierarchy-Enhanced Text-Video Retrieval
In particular, text-video retrieval, which aims to find the top matching videos given text descriptions from a vast video corpus, is an essential function, the primary challenge of which is to bridge the modality gap.
M2-RAAP: A Multi-Modal Recipe for Advancing Adaptation-based Pre-training towards Effective and Efficient Zero-shot Video-text Retrieval
We then summarize this empirical study into the M2-RAAP recipe, where our technical contributions lie in 1) the data filtering and text re-writing pipeline resulting in 1M high-quality bilingual video-text pairs, 2) the replacement of video inputs with key-frames to accelerate pre-training, and 3) the Auxiliary-Caption-Guided (ACG) strategy to enhance video features.
Knowledge-enhanced Multi-perspective Video Representation Learning for Scene Recognition
With the explosive growth of video data in real-world applications, a comprehensive representation of videos becomes increasingly important.
Dual-Modal Attention-Enhanced Text-Video Retrieval with Triplet Partial Margin Contrastive Learning
We thereby present a new Triplet Partial Margin Contrastive Learning (TPM-CL) module to construct partial order triplet samples by automatically generating fine-grained hard negatives for matched text-video pairs.
Ranked #4 on
Video Retrieval
on MSR-VTT-1kA
Personalized Hashtag Recommendation for Micro-videos
The hashtags, that a user provides to a post (e. g., a micro-video), are the ones which in her mind can well describe the post content where she is interested in.
