activity
20202024
most citedBLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation

871 citations · 2.9k across the 76 of their papers we have counts for

collaborators
Showing 2021Show all

23 papers · 1 filter

cs.CL2021★ 3 cited

QAFactEval: Improved QA-Based Factual Consistency Evaluation for Summarization

Alexander R. Fabbri, Chien-Sheng Wu, Wenhao Liu +1

Factual consistency is an essential quality of text summarization models in practical settings. Existing work in evaluating this dimension can be broadly categorized into two lines…

cs.CV2021★ 3 cited

Value Retrieval with Arbitrary Queries for Form-like Documents

Mingfei Gao, Le Xue, Chetan Ramaiah +3

We propose value retrieval with arbitrary queries for form-like documents to reduce human effort of processing forms. Unlike previous methods that only address a fixed set of field…

cs.CL2021★ 3 cited

Combining Data-driven Supervision with Human-in-the-loop Feedback for Entity Resolution

Wenpeng Yin, Shelby Heinecke, Jia Li +7

The distribution gap between training datasets and data encountered in production is well acknowledged. Training datasets are often constructed over a fixed period of time and by c…

cs.CV2021★ 5 cited

Open Vocabulary Object Detection with Pseudo Bounding-Box Labels

Mingfei Gao, Chen Xing, Juan Carlos Niebles +4

Despite great progress in object detection, most existing methods work only on a limited set of object categories, due to the tremendous human effort needed for bounding-box annota…

cs.IR2021★ 1 cited

Dense Hierarchical Retrieval for Open-Domain Question Answering

Ye Liu, Kazuma Hashimoto, Yingbo Zhou +3

Dense neural text retrieval has achieved promising results on open-domain Question Answering (QA), where latent representations of questions and passages are exploited for maximum…

cs.CV2021

Momentum Contrastive Autoencoder: Using Contrastive Learning for Latent Space Distribution Matching in WAE

Devansh Arpit, Aadyot Bhatnagar, Huan Wang +1

Wasserstein autoencoder (WAE) shows that matching two distributions is equivalent to minimizing a simple autoencoder (AE) loss under the constraint that the latent space of this AE…