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Found 9 papers, 3 papers with code
Progressive Learning for Image Retrieval with Hybrid-Modality Queries
Image retrieval with hybrid-modality queries, also known as composing text and image for image retrieval (CTI-IR), is a retrieval task where the search intention is expressed in a more complex query format, involving both vision and text modalities.
WenLan: Bridging Vision and Language by Large-Scale Multi-Modal Pre-Training
We further construct a large Chinese multi-source image-text dataset called RUC-CAS-WenLan for pre-training our BriVL model.
Ranked #1 on
Image Retrieval
on RUC-CAS-WenLan
The End-of-End-to-End: A Video Understanding Pentathlon Challenge (2020)
This report summarizes the results of the first edition of the challenge together with the findings of the participants.
Team RUC_AIM3 Technical Report at Activitynet 2020 Task 2: Exploring Sequential Events Detection for Dense Video Captioning
Detecting meaningful events in an untrimmed video is essential for dense video captioning.
Ranked #3 on
Dense Video Captioning
on ActivityNet Captions
Fine-grained Video-Text Retrieval with Hierarchical Graph Reasoning
To improve fine-grained video-text retrieval, we propose a Hierarchical Graph Reasoning (HGR) model, which decomposes video-text matching into global-to-local levels.
Integrating Temporal and Spatial Attentions for VATEX Video Captioning Challenge 2019
This notebook paper presents our model in the VATEX video captioning challenge.
Unpaired Cross-lingual Image Caption Generation with Self-Supervised Rewards
We employ self-supervision from mono-lingual corpus in the target language to provide fluency reward, and propose a multi-level visual semantic matching model to provide both sentence-level and concept-level visual relevancy rewards.
Activitynet 2019 Task 3: Exploring Contexts for Dense Captioning Events in Videos
The overall system achieves the state-of-the-art performance on the dense-captioning events in video task with 9. 91 METEOR score on the challenge testing set.
RUC+CMU: System Report for Dense Captioning Events in Videos
This notebook paper presents our system in the ActivityNet Dense Captioning in Video task (task 3).
