activity
20172023
most citedAn Adversarial Neuro-Tensorial Approach For Learning Disentangled Representations

21 citations · 42 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CL2023

Coarse-to-Fine Contrastive Learning in Image-Text-Graph Space for Improved Vision-Language Compositionality

Harman Singh, Pengchuan Zhang, Qifan Wang +4

Contrastively trained vision-language models have achieved remarkable progress in vision and language representation learning, leading to state-of-the-art models for various downst…

cs.IR2023★ 9 cited

Que2Engage: Embedding-based Retrieval for Relevant and Engaging Products at Facebook Marketplace

Yunzhong He, Yuxin Tian, Mengjiao Wang +7

Embedding-based Retrieval (EBR) in e-commerce search is a powerful search retrieval technique to address semantic matches between search queries and products. However, commercial s…

cs.CV2022★ 2 cited

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…

cs.CV2022★ 6 cited

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…

cs.CV2022★ 1 cited

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…

cs.CV2022★ 3 cited

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…

cs.CV2017★ 21 cited

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…