Can Gao

Can Gao has 2 papers in our library, in 2020–2021, most of them on Language models & pretraining, Sentiment & emotion, Vision & language; often with Haifeng Wang, Hao Liu, Hua Wu.

2 papers in our library · 2020–2021

The profile is the library's match of the name; two people with one name are not always told apart.

Affiliations

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine.

Of its works in our library: the number of works on each and its rating among them.

The most cited first (OpenAlex and our library).

SKEP: Sentiment Knowledge Enhanced Pre-training for Sentiment Analysis (2020 · 333 citations)
Pre-trained language models such as BERT and RoBERTa learn general representations through word-level objectives.
UNIMO: Towards Unified-Modal Understanding and Generation via Cross-Modal Contrastive Learning (2021 · 299 citations)
Existing pre-trained models usually serve only one side. Text-only and vision-only models such as BERT or ResNet cannot handle vision-language inputs. Multi-modal models such as ViLBERT and UNITER learn only from scarce image-text pairs and perform poorly when moved to pure language tasks. The authors (Baidu) propose…

6 co-authors with a page, the most papers together first.

Where their works here were published, the most works first, with the editions of those that have a page.

Papers of our library cite their works 129 times (of the 141,840 papers whose bibliographies we read); 11 citing papers with a page here →.

Updated 24 October 2026