38 citations · 48 across the 4 of their papers we have counts for
5 papers
LoopITR: Combining Dual and Cross Encoder Architectures for Image-Text Retrieval
Jie Lei, Xinlei Chen, Ning Zhang +4
Dual encoders and cross encoders have been widely used for image-text retrieval. Between the two, the dual encoder encodes the image and text independently followed by a dot produc…
Unsupervised Vision-and-Language Pre-training via Retrieval-based Multi-Granular Alignment
Mingyang Zhou, Licheng Yu, Amanpreet Singh +3
Vision-and-Language (V+L) pre-training models have achieved tremendous success in recent years on various multi-modal benchmarks. However, the majority of existing models require p…
CommerceMM: Large-Scale Commerce MultiModal Representation Learning with Omni Retrieval
Licheng Yu, Jun Chen, Animesh Sinha +4
We introduce CommerceMM - a multimodal model capable of providing a diverse and granular understanding of commerce topics associated to the given piece of content (image, text, ima…
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…
Connecting What to Say With Where to Look by Modeling Human Attention Traces
Zihang Meng, Licheng Yu, Ning Zhang +4
We introduce a unified framework to jointly model images, text, and human attention traces. Our work is built on top of the recent Localized Narratives annotation framework [30], w…