4 citations · 4 across the 2 of their papers we have counts for
7 papers
EfficientVLM: Fast and Accurate Vision-Language Models via Knowledge Distillation and Modal-adaptive Pruning
Tiannan Wang, Wangchunshu Zhou, Yan Zeng +1
Pre-trained vision-language models (VLMs) have achieved impressive results in a range of vision-language tasks. However, popular VLMs usually consist of hundreds of millions of par…
VLUE: A Multi-Task Benchmark for Evaluating Vision-Language Models
Wangchunshu Zhou, Yan Zeng, Shizhe Diao +1
Recent advances in vision-language pre-training (VLP) have demonstrated impressive performance in a range of vision-language (VL) tasks. However, there exist several challenges for…
A probabilistic deep learning approach to automate the interpretation of multi-phase diffraction spectra
Nathan J. Szymanski, Christopher J. Bartel, Yan Zeng +2
Autonomous synthesis and characterization of inorganic materials requires the automatic and accurate analysis of X-ray diffraction spectra. For this task, we designed a probabilist…
Open-Domain Dialogue Generation Based on Pre-trained Language Models
Yan Zeng, Jian-Yun Nie
Pre-trained language models have been successfully used in response generation for open-domain dialogue. Four main frameworks have been proposed: (1) Transformer-ED using Transform…
Multi-Domain Dialogue State Tracking based on State Graph
Yan Zeng, Jian-Yun Nie
We investigate the problem of multi-domain Dialogue State Tracking (DST) with open vocabulary, which aims to extract the state from the dialogue. Existing approaches usually concat…
Jointly Optimizing State Operation Prediction and Value Generation for Dialogue State Tracking
Yan Zeng, Jian-Yun Nie
We investigate the problem of multi-domain Dialogue State Tracking (DST) with open vocabulary. Existing approaches exploit BERT encoder and copy-based RNN decoder, where the encode…