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Found 4 papers, 4 papers with code
Large Language Models are Temporal and Causal Reasoners for Video Question Answering
We observe that the LLMs provide effective priors in exploiting $\textit{linguistic shortcuts}$ for temporal and causal reasoning in Video Question Answering (VideoQA).
Ranked #1 on
Video Question Answering
on TVQA
Open-vocabulary Video Question Answering: A New Benchmark for Evaluating the Generalizability of Video Question Answering Models
We hence propose a new benchmark, Open-vocabulary Video Question Answering (OVQA), to measure the generalizability of VideoQA models by considering rare and unseen answers.
Ranked #8 on
Visual Question Answering (VQA)
on MSRVTT-QA
MELTR: Meta Loss Transformer for Learning to Fine-tune Video Foundation Models
Therefore, we propose MEta Loss TRansformer (MELTR), a plug-in module that automatically and non-linearly combines various loss functions to aid learning the target task via auxiliary learning.
Ranked #2 on
Video Captioning
on YouCook2
Video-Text Representation Learning via Differentiable Weak Temporal Alignment
In this paper, we propose a novel multi-modal self-supervised framework Video-Text Temporally Weak Alignment-based Contrastive Learning (VT-TWINS) to capture significant information from noisy and weakly correlated data using a variant of Dynamic Time Warping (DTW).
