9 citations · 21 across the 6 of their papers we have counts for
5 papers · 1 filter
FaD-VLP: Fashion Vision-and-Language Pre-training towards Unified Retrieval and Captioning
Suvir Mirchandani, Licheng Yu, Mengjiao Wang +4
Multimodal tasks in the fashion domain have significant potential for e-commerce, but involve challenging vision-and-language learning problems - e.g., retrieving a fashion item gi…
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…
An Adversarial Neuro-Tensorial Approach For Learning Disentangled Representations
Mengjiao Wang, Zhixin Shu, Shiyang Cheng +3
Several factors contribute to the appearance of an object in a visual scene, including pose, illumination, and deformation, among others. Each factor accounts for a source of varia…