Search Results for author:
Found 9 papers, 4 papers with code
Weakly-Supervised 3D Visual Grounding based on Visual Linguistic Alignment
During the inference stage, the learned text-3D correspondence will help us ground the text queries to the 3D target objects even without 2D images.
A Closer Look at Debiased Temporal Sentence Grounding in Videos: Dataset, Metric, and Approach
New benchmarking results indicate that our proposed evaluation protocols can better monitor the research progress.
Controllable Video Captioning with an Exemplar Sentence
In this paper, we investigate a novel and challenging task, namely controllable video captioning with an exemplar sentence.
A Survey on Temporal Sentence Grounding in Videos
In this survey, we give a comprehensive overview for TSGV, which i) summarizes the taxonomy of existing methods, ii) provides a detailed description of the evaluation protocols(i. e., datasets and metrics) to be used in TSGV, and iii) in-depth discusses potential problems of current benchmarking designs and research directions for further investigations.
A Closer Look at Temporal Sentence Grounding in Videos: Dataset and Metric
All the results demonstrate that the re-organized dataset splits and new metric can better monitor the progress in TSGV.
Semantic Conditioned Dynamic Modulation for Temporal Sentence Grounding in Videos
Temporal sentence grounding in videos aims to detect and localize one target video segment, which semantically corresponds to a given sentence.
Sentence Specified Dynamic Video Thumbnail Generation
With the tremendous growth of videos over the Internet, video thumbnails, providing video content previews, are becoming increasingly crucial to influencing users' online searching experiences.
To Find Where You Talk: Temporal Sentence Localization in Video with Attention Based Location Regression
Then, a multi-modal co-attention mechanism is introduced to generate not only video attention which reflects the global video structure, but also sentence attention which highlights the crucial details for temporal localization.
