Search Results for author: Zheng Tang

Found 21 papers, 9 papers with code

Taxonomy Builder: a Data-driven and User-centric Tool for Streamlining Taxonomy Construction

no code implementations NAACL (HCINLP) 2022 Mihai Surdeanu, John Hungerford, Yee Seng Chan, Jessica MacBride, Benjamin Gyori, Andrew Zupon, Zheng Tang, Haoling Qiu, Bonan Min, Yan Zverev, Caitlin Hilverman, Max Thomas, Walter Andrews, Keith Alcock, Zeyu Zhang, Michael Reynolds, Steven Bethard, Rebecca Sharp, Egoitz Laparra

An existing domain taxonomy for normalizing content is often assumed when discussing approaches to information extraction, yet often in real-world scenarios there is none. When one does exist, as the information needs shift, it must be continually extended.

Text Summarization

Interpretability Rules: Jointly Bootstrapping a Neural Relation Extractorwith an Explanation Decoder

no code implementations NAACL (TrustNLP) 2021 Zheng Tang, Mihai Surdeanu

We introduce a method that transforms a rule-based relation extraction (RE) classifier into a neural one such that both interpretability and performance are achieved.

Decoder Relation +1

Paraphrase and Aggregate with Large Language Models for Minimizing Intent Classification Errors

no code implementations24 Jun 2024 Vikas Yadav, Zheng Tang, Vijay Srinivasan

Large language models (LLM) have achieved remarkable success in natural language generation but lesser focus has been given to their applicability in decision making tasks such as classification.

Classification Decision Making +4

The 8th AI City Challenge

no code implementations15 Apr 2024 Shuo Wang, David C. Anastasiu, Zheng Tang, Ming-Ching Chang, Yue Yao, Liang Zheng, Mohammed Shaiqur Rahman, Meenakshi S. Arya, Anuj Sharma, Pranamesh Chakraborty, Sanjita Prajapati, Quan Kong, Norimasa Kobori, Munkhjargal Gochoo, Munkh-Erdene Otgonbold, Fady Alnajjar, Ganzorig Batnasan, Ping-Yang Chen, Jun-Wei Hsieh, Xunlei Wu, Sameer Satish Pusegaonkar, Yizhou Wang, Sujit Biswas, Rama Chellappa

The eighth AI City Challenge highlighted the convergence of computer vision and artificial intelligence in areas like retail, warehouse settings, and Intelligent Traffic Systems (ITS), presenting significant research opportunities.

Dense Video Captioning

Training with Product Digital Twins for AutoRetail Checkout

1 code implementation18 Aug 2023 Yue Yao, Xinyu Tian, Zheng Tang, Sujit Biswas, Huan Lei, Tom Gedeon, Liang Zheng

Because the digital twins individually mimic user bias, the resulting DT training set better reflects the characteristics of the target scenario and allows us to train more effective product detection and tracking models.

Backdooring Instruction-Tuned Large Language Models with Virtual Prompt Injection

1 code implementation31 Jul 2023 Jun Yan, Vikas Yadav, Shiyang Li, Lichang Chen, Zheng Tang, Hai Wang, Vijay Srinivasan, Xiang Ren, Hongxia Jin

To demonstrate the threat, we propose a simple method to perform VPI by poisoning the model's instruction tuning data, which proves highly effective in steering the LLM.

Backdoor Attack

Instruction-following Evaluation through Verbalizer Manipulation

no code implementations20 Jul 2023 Shiyang Li, Jun Yan, Hai Wang, Zheng Tang, Xiang Ren, Vijay Srinivasan, Hongxia Jin

We conduct a comprehensive evaluation of four major model families across nine datasets, employing twelve sets of verbalizers for each of them.

Instruction Following

AlpaGasus: Training A Better Alpaca with Fewer Data

3 code implementations17 Jul 2023 Lichang Chen, Shiyang Li, Jun Yan, Hai Wang, Kalpa Gunaratna, Vikas Yadav, Zheng Tang, Vijay Srinivasan, Tianyi Zhou, Heng Huang, Hongxia Jin

Large language models (LLMs) strengthen instruction-following capability through instruction-finetuning (IFT) on supervised instruction/response data.

Instruction Following

The Staged Knowledge Distillation in Video Classification: Harmonizing Student Progress by a Complementary Weakly Supervised Framework

no code implementations11 Jul 2023 Chao Wang, Zheng Tang

Our proposed substage-based distillation approach has the potential to inform future research on label-efficient learning for video data.

Knowledge Distillation Pseudo Label +2

The 7th AI City Challenge

no code implementations15 Apr 2023 Milind Naphade, Shuo Wang, David C. Anastasiu, Zheng Tang, Ming-Ching Chang, Yue Yao, Liang Zheng, Mohammed Shaiqur Rahman, Meenakshi S. Arya, Anuj Sharma, Qi Feng, Vitaly Ablavsky, Stan Sclaroff, Pranamesh Chakraborty, Sanjita Prajapati, Alice Li, Shangru Li, Krishna Kunadharaju, Shenxin Jiang, Rama Chellappa

The AI City Challenge's seventh edition emphasizes two domains at the intersection of computer vision and artificial intelligence - retail business and Intelligent Traffic Systems (ITS) - that have considerable untapped potential.

Retrieval

It Takes Two Flints to Make a Fire: Multitask Learning of Neural Relation and Explanation Classifiers

1 code implementation25 Apr 2022 Zheng Tang, Mihai Surdeanu

Our approach uses a multi-task learning architecture, which jointly trains a classifier for relation extraction, and a sequence model that labels words in the context of the relation that explain the decisions of the relation classifier.

Multi-Task Learning Relation +1

Exploring Interpretability in Event Extraction: Multitask Learning of a Neural Event Classifier and an Explanation Decoder

no code implementations ACL 2020 Zheng Tang, Gus Hahn-Powell, Mihai Surdeanu

Our approach uses an encoder-decoder architecture, which jointly trains a classifier for event extraction, and a rule decoder that generates syntactico-semantic rules that explain the decisions of the event classifier.

Decoder Event Extraction

MOANA: An Online Learned Adaptive Appearance Model for Robust Multiple Object Tracking in 3D

no code implementations9 Jan 2019 Zheng Tang, Jenq-Neng Hwang

Multiple object tracking has been a challenging field, mainly due to noisy detection sets and identity switch caused by occlusion and similar appearance among nearby targets.

Multiple Object Tracking

Multiple-Kernel Based Vehicle Tracking Using 3D Deformable Model and Camera Self-Calibration

no code implementations22 Aug 2017 Zheng Tang, Gaoang Wang, Tao Liu, Young-Gun Lee, Adwin Jahn, Xu Liu, Xiaodong He, Jenq-Neng Hwang

In this challenge, we propose a model-based vehicle localization method, which builds a kernel at each patch of the 3D deformable vehicle model and associates them with constraints in 3D space.

Ensemble Learning object-detection +1

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