51 citations · 287 across the 18 of their papers we have counts for
49 papers
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…
CUPID: Adaptive Curation of Pre-training Data for Video-and-Language Representation Learning
Luowei Zhou, Jingjing Liu, Yu Cheng +2
This work concerns video-language pre-training and representation learning. In this now ubiquitous training scheme, a model first performs pre-training on paired videos and text (e…
LightningDOT: Pre-training Visual-Semantic Embeddings for Real-Time Image-Text Retrieval
Siqi Sun, Yen-Chun Chen, Linjie Li +3
Multimodal pre-training has propelled great advancement in vision-and-language research. These large-scale pre-trained models, although successful, fatefully suffer from slow infer…
UC2: Universal Cross-lingual Cross-modal Vision-and-Language Pre-training
Mingyang Zhou, Luowei Zhou, Shuohang Wang +4
Vision-and-language pre-training has achieved impressive success in learning multimodal representations between vision and language. To generalize this success to non-English langu…
Adversarial Feature Augmentation and Normalization for Visual Recognition
Tianlong Chen, Yu Cheng, Zhe Gan +4
Recent advances in computer vision take advantage of adversarial data augmentation to ameliorate the generalization ability of classification models. Here, we present an effective…