Search Results for author:
Found 6 papers, 2 papers with code
Towards Efficient and Effective Text-to-Video Retrieval with Coarse-to-Fine Visual Representation Learning
To address this issue, we adopt multi-granularity visual feature learning, ensuring the model's comprehensiveness in capturing visual content features spanning from abstract to detailed levels during the training phase.
TeachCLIP: Multi-Grained Teaching for Efficient Text-to-Video Retrieval
For efficient T2VR, we propose TeachCLIP with multi-grained teaching to let a CLIP4Clip based student network learn from more advanced yet computationally heavy models such as X-CLIP, TS2-Net and X-Pool .
Renmin University of China at TRECVID 2022: Improving Video Search by Feature Fusion and Negation Understanding
The 2022 edition of the TRECVID benchmark has again been a fruitful participation for the RUCMM team.
MVP: Robust Multi-View Practice for Driving Action Localization
Due to the small data scale and unclear action boundary, the dataset presents a unique challenge to precisely localize all the different actions and classify their categories.
Co-Teaching for Unsupervised Domain Adaptation and Expansion
Such sorts of samples are typically in minority in their host domain, so they tend to be overlooked by the domain-specific model, but can be better handled by a model from the other domain.
Unsupervised Domain Expansion for Visual Categorization
In this paper we extend UDA by proposing a new task called unsupervised domain expansion (UDE), which aims to adapt a deep model for the target domain with its unlabeled data, meanwhile maintaining the model's performance on the source domain.
Ranked #1 on
Unsupervised Domain Expansion
on UDE-DomainNet
