activity
20182023
most citedPixel-BERT: Aligning Image Pixels with Text by Deep Multi-Modal Transformers

283 citations · 456 across the 11 of their papers we have counts for

collaborators

11 papers

cs.CV2023★ 1 cited

SINC: Self-Supervised In-Context Learning for Vision-Language Tasks

Yi-Syuan Chen, Yun-Zhu Song, Cheng Yu Yeo +3

Large Pre-trained Transformers exhibit an intriguing capacity for in-context learning. Without gradient updates, these models can rapidly construct new predictors from demonstratio…

cs.CV2022★ 2 cited

Exploring Anchor-based Detection for Ego4D Natural Language Query

Sipeng Zheng, Qi Zhang, Bei Liu +2

In this paper we provide the technique report of Ego4D natural language query challenge in CVPR 2022. Natural language query task is challenging due to the requirement of comprehen…

cs.CV2021★ 49 cited

Reference-based Defect Detection Network

Zhaoyang Zeng, Bei Liu, Jianlong Fu +1

The defect detection task can be regarded as a realistic scenario of object detection in the computer vision field and it is widely used in the industrial field. Directly applying…

cs.CV2021★ 9 cited

Probing Inter-modality: Visual Parsing with Self-Attention for Vision-Language Pre-training

Hongwei Xue, Yupan Huang, Bei Liu +4

Vision-Language Pre-training (VLP) aims to learn multi-modal representations from image-text pairs and serves for downstream vision-language tasks in a fine-tuning fashion. The dom…

cs.CV2021★ 25 cited

Seeing Out of tHe bOx: End-to-End Pre-training for Vision-Language Representation Learning

Zhicheng Huang, Zhaoyang Zeng, Yupan Huang +3

We study joint learning of Convolutional Neural Network (CNN) and Transformer for vision-language pre-training (VLPT) which aims to learn cross-modal alignments from millions of im…

cs.CV2020★ 283 cited

Pixel-BERT: Aligning Image Pixels with Text by Deep Multi-Modal Transformers

Zhicheng Huang, Zhaoyang Zeng, Bei Liu +2

We propose Pixel-BERT to align image pixels with text by deep multi-modal transformers that jointly learn visual and language embedding in a unified end-to-end framework. We aim to…