190 citations · 498 across the 18 of their papers we have counts for
5 papers · 1 filter
VIMPAC: Video Pre-Training via Masked Token Prediction and Contrastive Learning
Hao Tan, Jie Lei, Thomas Wolf +1
Video understanding relies on perceiving the global content and modeling its internal connections (e.g., causality, movement, and spatio-temporal correspondence). To learn these in…
VALUE: A Multi-Task Benchmark for Video-and-Language Understanding Evaluation
Linjie Li, Jie Lei, Zhe Gan +12
Most existing video-and-language (VidL) research focuses on a single dataset, or multiple datasets of a single task. In reality, a truly useful VidL system is expected to be easily…
Adversarial VQA: A New Benchmark for Evaluating the Robustness of VQA Models
Linjie Li, Jie Lei, Zhe Gan +1
Benefiting from large-scale pre-training, we have witnessed significant performance boost on the popular Visual Question Answering (VQA) task. Despite rapid progress, it remains un…
Less is More: ClipBERT for Video-and-Language Learning via Sparse Sampling
Jie Lei, Linjie Li, Luowei Zhou +4
The canonical approach to video-and-language learning (e.g., video question answering) dictates a neural model to learn from offline-extracted dense video features from vision mode…
Unifying Vision-and-Language Tasks via Text Generation
Jaemin Cho, Jie Lei, Hao Tan +1
Existing methods for vision-and-language learning typically require designing task-specific architectures and objectives for each task. For example, a multi-label answer classifier…