Search Results for author:
Found 6 papers, 2 papers with code
Beyond Coarse-Grained Matching in Video-Text Retrieval
We perform comprehensive experiments using four state-of-the-art models across two standard benchmarks (MSR-VTT and VATEX) and two specially curated datasets enriched with detailed descriptions (VLN-UVO and VLN-OOPS), resulting in a number of novel insights: 1) our analyses show that the current evaluation benchmarks fall short in detecting a model's ability to perceive subtle single-word differences, 2) our fine-grained evaluation highlights the difficulty models face in distinguishing such subtle variations.
D&M: Enriching E-commerce Videos with Sound Effects by Key Moment Detection and SFX Matching
Previous studies about adding SFX to videos perform video to SFX matching at a holistic level, lacking the ability of adding SFX to a specific moment.
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.
Learn to Understand Negation in Video Retrieval
We propose a learning based method for training a negation-aware video retrieval model.
Lightweight Attentional Feature Fusion: A New Baseline for Text-to-Video Retrieval
In this paper we revisit feature fusion, an old-fashioned topic, in the new context of text-to-video retrieval.
Ranked #1 on
Ad-hoc video search
on TRECVID-AVS20 (V3C1)
(using extra training data)
Towards Annotation-Free Evaluation of Cross-Lingual Image Captioning
For the first scenario with the references available, we propose two metrics, i. e., WMDRel and CLinRel.
