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Found 6 papers, 4 papers with code
Zero-Shot Video Question Answering via Frozen Bidirectional Language Models
Manual annotation of question and answers for videos, however, is tedious and prohibits scalability.
Learning to Answer Visual Questions from Web Videos
We use our method to generate the WebVidVQA3M dataset from the WebVid dataset, i. e., videos with alt-text annotations, and show its benefits for training VideoQA models.
TubeDETR: Spatio-Temporal Video Grounding with Transformers
We consider the problem of localizing a spatio-temporal tube in a video corresponding to a given text query.
Ranked #1 on
Spatio-Temporal Video Grounding
on VidSTG
Language-Based Temporal Localization
Natural Language Visual Grounding
+5
Just Ask: Learning to Answer Questions from Millions of Narrated Videos
In this work, we propose to avoid manual annotation and generate a large-scale training dataset for video question answering making use of automatic cross-modal supervision.
Ranked #1 on
Video Question Answering
on iVQA
(using extra training data)
NAS evaluation is frustratingly hard
As such, and due to the under-use of ablation studies, there is a lack of clarity regarding why certain methods are more effective than others.
MANAS: Multi-Agent Neural Architecture Search
The Neural Architecture Search (NAS) problem is typically formulated as a graph search problem where the goal is to learn the optimal operations over edges in order to maximise a graph-level global objective.
