Search Results for author: Haochen Shi

Found 27 papers, 8 papers with code

EcomEdit: An Automated E-commerce Knowledge Editing Framework for Enhanced Product and Purchase Intention Understanding

no code implementations18 Oct 2024 Ching Ming Samuel Lau, Weiqi Wang, Haochen Shi, Baixuan Xu, Jiaxin Bai, Yangqiu Song

However, there are naturally occurring scenarios that make KE necessary in this domain, such as the timely updating of product features and trending purchase intentions by customers, which necessitate further exploration.

knowledge editing

TradExpert: Revolutionizing Trading with Mixture of Expert LLMs

no code implementations16 Oct 2024 Qianggang Ding, Haochen Shi, Bang Liu

The integration of Artificial Intelligence (AI) in the financial domain has opened new avenues for quantitative trading, particularly through the use of Large Language Models (LLMs).

FürElise: Capturing and Physically Synthesizing Hand Motions of Piano Performance

no code implementations8 Oct 2024 Ruocheng Wang, Pei Xu, Haochen Shi, Elizabeth Schumann, C. Karen Liu

We then further augmented the data by using musical similarity to retrieve similar motions from the captured dataset to boost the precision of the RL policy.

Imitation Learning Pose Estimation

RADAR: Robust Two-stage Modality-incomplete Industrial Anomaly Detection

no code implementations2 Oct 2024 Bingchen Miao, Wenqiao Zhang, Juncheng Li, Siliang Tang, Zhaocheng Li, Haochen Shi, Jun Xiao, Yueting Zhuang

To address this practical challenge, we introduce a first-of-its-kind study that comprehensively investigates Modality-Incomplete Industrial Anomaly Detection (MIIAD), to consider the imperfect learning environment in which the multimodal information may be incomplete.

Anomaly Detection Philosophy

Enhancing Agent Learning through World Dynamics Modeling

1 code implementation25 Jul 2024 Zhiyuan Sun, Haochen Shi, Marc-Alexandre Côté, Glen Berseth, Xingdi Yuan, Bang Liu

Large language models (LLMs) have been increasingly applied to tasks in language understanding and interactive decision-making, with their impressive performance largely attributed to the extensive domain knowledge embedded within them.

Decision Making

RoboPack: Learning Tactile-Informed Dynamics Models for Dense Packing

no code implementations1 Jul 2024 Bo Ai, Stephen Tian, Haochen Shi, YiXuan Wang, Cheston Tan, Yunzhu Li, Jiajun Wu

Tactile feedback is critical for understanding the dynamics of both rigid and deformable objects in many manipulation tasks, such as non-prehensile manipulation and dense packing.

Graph Neural Network Model Predictive Control

DexCap: Scalable and Portable Mocap Data Collection System for Dexterous Manipulation

no code implementations12 Mar 2024 Chen Wang, Haochen Shi, Weizhuo Wang, Ruohan Zhang, Li Fei-Fei, C. Karen Liu

Imitation learning from human hand motion data presents a promising avenue for imbuing robots with human-like dexterity in real-world manipulation tasks.

Imitation Learning

OPEx: A Component-Wise Analysis of LLM-Centric Agents in Embodied Instruction Following

no code implementations5 Mar 2024 Haochen Shi, Zhiyuan Sun, Xingdi Yuan, Marc-Alexandre Côté, Bang Liu

Embodied Instruction Following (EIF) is a crucial task in embodied learning, requiring agents to interact with their environment through egocentric observations to fulfill natural language instructions.

Instruction Following

MIKO: Multimodal Intention Knowledge Distillation from Large Language Models for Social-Media Commonsense Discovery

no code implementations28 Feb 2024 Feihong Lu, Weiqi Wang, Yangyifei Luo, Ziqin Zhu, Qingyun Sun, Baixuan Xu, Haochen Shi, Shiqi Gao, Qian Li, Yangqiu Song, JianXin Li

However, understanding the intention behind social media posts remains challenging due to the implicitness of intentions in social media posts, the need for cross-modality understanding of both text and images, and the presence of noisy information such as hashtags, misspelled words, and complicated abbreviations.

Knowledge Distillation Language Modeling +4

CANDLE: Iterative Conceptualization and Instantiation Distillation from Large Language Models for Commonsense Reasoning

2 code implementations14 Jan 2024 Weiqi Wang, Tianqing Fang, Chunyang Li, Haochen Shi, Wenxuan Ding, Baixuan Xu, Zhaowei Wang, Jiaxin Bai, Xin Liu, Jiayang Cheng, Chunkit Chan, Yangqiu Song

The sequential process of conceptualization and instantiation is essential to generalizable commonsense reasoning as it allows the application of existing knowledge to unfamiliar scenarios.

Diversity

Deciphering Digital Detectives: Understanding LLM Behaviors and Capabilities in Multi-Agent Mystery Games

no code implementations1 Dec 2023 Dekun Wu, Haochen Shi, Zhiyuan Sun, Bang Liu

In this study, we explore the application of Large Language Models (LLMs) in \textit{Jubensha}, a Chinese detective role-playing game and a novel area in Artificial Intelligence (AI) driven gaming.

AI Agent In-Context Learning +3

AbsPyramid: Benchmarking the Abstraction Ability of Language Models with a Unified Entailment Graph

1 code implementation15 Nov 2023 Zhaowei Wang, Haochen Shi, Weiqi Wang, Tianqing Fang, Hongming Zhang, Sehyun Choi, Xin Liu, Yangqiu Song

Cognitive research indicates that abstraction ability is essential in human intelligence, which remains under-explored in language models.

Benchmarking

QADYNAMICS: Training Dynamics-Driven Synthetic QA Diagnostic for Zero-Shot Commonsense Question Answering

1 code implementation17 Oct 2023 Haochen Shi, Weiqi Wang, Tianqing Fang, Baixuan Xu, Wenxuan Ding, Xin Liu, Yangqiu Song

Zero-shot commonsense Question-Answering (QA) requires models to reason about general situations beyond specific benchmarks.

Question Answering

Dilated Context Integrated Network with Cross-Modal Consensus for Temporal Emotion Localization in Videos

1 code implementation3 Aug 2022 Juncheng Li, Junlin Xie, Linchao Zhu, Long Qian, Siliang Tang, Wenqiao Zhang, Haochen Shi, Shengyu Zhang, Longhui Wei, Qi Tian, Yueting Zhuang

In this paper, we introduce a new task, named Temporal Emotion Localization in videos~(TEL), which aims to detect human emotions and localize their corresponding temporal boundaries in untrimmed videos with aligned subtitles.

Emotion Classification Temporal Action Localization +1

BOSS: Bottom-up Cross-modal Semantic Composition with Hybrid Counterfactual Training for Robust Content-based Image Retrieval

no code implementations9 Jul 2022 Wenqiao Zhang, Jiannan Guo, Mengze Li, Haochen Shi, Shengyu Zhang, Juncheng Li, Siliang Tang, Yueting Zhuang

In this scenario, the input image serves as an intuitive context and background for the search, while the corresponding language expressly requests new traits on how specific characteristics of the query image should be modified in order to get the intended target image.

Content-Based Image Retrieval counterfactual +2

RoboCraft: Learning to See, Simulate, and Shape Elasto-Plastic Objects with Graph Networks

no code implementations5 May 2022 Haochen Shi, Huazhe Xu, Zhiao Huang, Yunzhu Li, Jiajun Wu

Our learned model-based planning framework is comparable to and sometimes better than human subjects on the tested tasks.

Model Predictive Control

Consensus Graph Representation Learning for Better Grounded Image Captioning

no code implementations2 Dec 2021 Wenqiao Zhang, Haochen Shi, Siliang Tang, Jun Xiao, Qiang Yu, Yueting Zhuang

The contemporary visual captioning models frequently hallucinate objects that are not actually in a scene, due to the visual misclassification or over-reliance on priors that resulting in the semantic inconsistency between the visual information and the target lexical words.

Graph Representation Learning Hallucination +2

Adaptive Hierarchical Graph Reasoning with Semantic Coherence for Video-and-Language Inference

no code implementations ICCV 2021 Juncheng Li, Siliang Tang, Linchao Zhu, Haochen Shi, Xuanwen Huang, Fei Wu, Yi Yang, Yueting Zhuang

Secondly, we introduce semantic coherence learning to explicitly encourage the semantic coherence of the adaptive hierarchical graph network from three hierarchies.

Empower Distantly Supervised Relation Extraction with Collaborative Adversarial Training

1 code implementation21 Jun 2021 Tao Chen, Haochen Shi, Liyuan Liu, Siliang Tang, Jian Shao, Zhigang Chen, Yueting Zhuang

In this paper, we propose collaborative adversarial training to improve the data utilization, which coordinates virtual adversarial training (VAT) and adversarial training (AT) at different levels.

Relation Relation Extraction

Semi-Supervised Active Learning for Semi-Supervised Models: Exploit Adversarial Examples With Graph-Based Virtual Labels

no code implementations ICCV 2021 Jiannan Guo, Haochen Shi, Yangyang Kang, Kun Kuang, Siliang Tang, Zhuoren Jiang, Changlong Sun, Fei Wu, Yueting Zhuang

Although current mainstream methods begin to combine SSL and AL (SSL-AL) to excavate the diverse expressions of unlabeled samples, these methods' fully supervised task models are still trained only with labeled data.

Active Learning

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