most citedPoseRAC: Pose Saliency Transformer for Repetitive Action Counting

8 citations · 16 across the 6 of their papers we have counts for

collaborators

6 papers

cs.AI2024

Look Further Ahead: Testing the Limits of GPT-4 in Path Planning

Mohamed Aghzal, Erion Plaku, Ziyu Yao

Large Language Models (LLMs) have shown impressive capabilities across a wide variety of tasks. However, they still face challenges with long-horizon planning. To study this, we pr…

cs.AI20237 cited

Gentopia: A Collaborative Platform for Tool-Augmented LLMs

Binfeng Xu, Xukun Liu, Hua Shen +7

Augmented Language Models (ALMs) empower large language models with the ability to use tools, transforming them into intelligent agents for real-world interactions. However, most e…

cs.CL2023

Learning to Simulate Natural Language Feedback for Interactive Semantic Parsing

Hao Yan, Saurabh Srivastava, Yintao Tai +3

Interactive semantic parsing based on natural language (NL) feedback, where users provide feedback to correct the parser mistakes, has emerged as a more practical scenario than the…

cs.CL2023

Improving Generalization in Language Model-Based Text-to-SQL Semantic Parsing: Two Simple Semantic Boundary-Based Techniques

Daking Rai, Bailin Wang, Yilun Zhou +1

Compositional and domain generalization present significant challenges in semantic parsing, even for state-of-the-art semantic parsers based on pre-trained language models (LMs). I…

cs.CV20238 cited

PoseRAC: Pose Saliency Transformer for Repetitive Action Counting

Ziyu Yao, Xuxin Cheng, Yuexian Zou

This paper presents a significant contribution to the field of repetitive action counting through the introduction of a new approach called Pose Saliency Representation. The propos…

cs.CL20231 cited

Explaining Large Language Model-Based Neural Semantic Parsers (Student Abstract)

Daking Rai, Yilun Zhou, Bailin Wang +1

While large language models (LLMs) have demonstrated strong capability in structured prediction tasks such as semantic parsing, few amounts of research have explored the underlying…