Search Results for author: Siming Chen

Found 7 papers, 2 papers with code

OneLabeler: A Flexible System for Building Data Labeling Tools

1 code implementation27 Mar 2022 Yu Zhang, Yun Wang, Haidong Zhang, Bin Zhu, Siming Chen, Dongmei Zhang

In this paper, we propose a conceptual framework for data labeling and OneLabeler based on the conceptual framework to support easy building of labeling tools for diverse usage scenarios.

Novelty Detection in Sequential Data by Informed Clustering and Modeling

1 code implementation5 Mar 2021 Linara Adilova, Siming Chen, Michael Kamp

We propose to approach this challenge through decomposition: by clustering the data we break down the problem, obtaining simpler modeling task in each cluster which can be modeled more accurately.

Clustering Novelty Detection

Exploring Multi-dimensional Data via Subset Embedding

no code implementations24 Apr 2021 Peng Xie, Wenyuan Tao, Jie Li, Wentao Huang, Siming Chen

The core of the approach is a subset embedding network (SEN) that represents a group of subsets as uniformly-formatted embeddings.

Rethinking Super-Resolution as Text-Guided Details Generation

no code implementations14 Jul 2022 Chenxi Ma, Bo Yan, Qing Lin, Weimin Tan, Siming Chen

To enhance the semantic accuracy and the visual quality of the reconstructed image, we explore the multi-modal fusion learning in SISR by proposing a Text-Guided Super-Resolution (TGSR) framework, which can effectively utilize the information from the text and image modalities.

Image Super-Resolution

A Comprehensive Capability Analysis of GPT-3 and GPT-3.5 Series Models

no code implementations18 Mar 2023 Junjie Ye, Xuanting Chen, Nuo Xu, Can Zu, Zekai Shao, Shichun Liu, Yuhan Cui, Zeyang Zhou, Chao Gong, Yang shen, Jie zhou, Siming Chen, Tao Gui, Qi Zhang, Xuanjing Huang

GPT series models, such as GPT-3, CodeX, InstructGPT, ChatGPT, and so on, have gained considerable attention due to their exceptional natural language processing capabilities.

Natural Language Understanding

SoMeLVLM: A Large Vision Language Model for Social Media Processing

no code implementations20 Feb 2024 Xinnong Zhang, Haoyu Kuang, Xinyi Mou, Hanjia Lyu, Kun Wu, Siming Chen, Jiebo Luo, Xuanjing Huang, Zhongyu Wei

The powerful Large Vision Language Models make it possible to handle a variety of tasks simultaneously, but even with carefully designed prompting methods, the general domain models often fall short in aligning with the unique speaking style and context of social media tasks.

Language Modelling

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