activity
20142023
most citedMS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition

162 citations · 272 across the 6 of their papers we have counts for

collaborators

6 papers

eess.AS2023

OTF: Optimal Transport based Fusion of Supervised and Self-Supervised Learning Models for Automatic Speech Recognition

Li Fu, Siqi Li, Qingtao Li +6

Self-Supervised Learning (SSL) Automatic Speech Recognition (ASR) models have shown great promise over Supervised Learning (SL) ones in low-resource settings. However, the advantag…

cs.CV201618 cited

Semantic Compositional Networks for Visual Captioning

Zhe Gan, Chuang Gan, Xiaodong He +5

A Semantic Compositional Network (SCN) is developed for image captioning, in which semantic concepts (i.e., tags) are detected from the image, and the probability of each tag is us…

cs.CL201613 cited

Bi-directional Attention with Agreement for Dependency Parsing

Hao Cheng, Hao Fang, Xiaodong He +2

We develop a novel bi-directional attention model for dependency parsing, which learns to agree on headword predictions from the forward and backward parsing directions. The parsin…

cs.CV2016162 cited

MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition

Yandong Guo, Lei Zhang, Yuxiao Hu +2

In this paper, we design a benchmark task and provide the associated datasets for recognizing face images and link them to corresponding entity keys in a knowledge base. More speci…

cs.IR201458 cited

Semantic Modelling with Long-Short-Term Memory for Information Retrieval

H. Palangi, L. Deng, Y. Shen +5

In this paper we address the following problem in web document and information retrieval (IR): How can we use long-term context information to gain better IR performance? Unlike co…

cs.CL201421 cited

Learning Multi-Relational Semantics Using Neural-Embedding Models

Bishan Yang, Wen-tau Yih, Xiaodong He +2

In this paper we present a unified framework for modeling multi-relational representations, scoring, and learning, and conduct an empirical study of several recent multi-relational…