Generalizing from Limited Resources in the Open World : Second International Workshop, GLOW 2024, Held in Conjunction with IJCAI 2024, Jeju, South Korea, August 3, 2024, Proceedings / edited by Jinyang Guo, Yuqing Ma, Yifu Ding, Ruihao Gong, Xingyu Zheng, Changyi He, Yantao Lu, Xianglong Liu.

Format:

Book

Contributor:

Guo, Jinyang, editor.

Series:

Communications in Computer and Information Science, 1865-0937 ; 2160

Language:

English

Subjects (All):

Artificial intelligence.

Computer science.

Computers.

Application software.

Artificial Intelligence.

Theory of Computation.

Computing Milieux.

Computer and Information Systems Applications.

Local Subjects:

Artificial Intelligence.

Theory of Computation.

Computing Milieux.

Computer and Information Systems Applications.

Physical Description:

1 online resource (215 pages)

Edition:

1st ed. 2024.

Place of Publication:

Singapore : Springer Nature Singapore : Imprint: Springer, 2024.

Summary:

This book presents the Proceedings from the Second International Workshop GLOW 2024 held in conjunction with the International Joint Conference on Artificial Intelligence, IJCAI 2024, in Jeju Island, South Korea, in August 2024. The 11 full papers and 4 short papers included in this book were carefully reviewed and selected from 22 submissions. They were organized in topical sections as follows: efficient methods for low-resource hardware; efficient fintuning with limited data; advancements in multimodal systems; recognition and reasoning in the open world.

Contents:

Efficient Methods for Low-resource Hardware

Towards Point Cloud Compression for Machine Perception: A Simple and Strong Baseline by Learning the Octree Depth Level Predictor

Toward Efficient Deep Spiking Neuron Networks: A Survey On Compression

Towards Efficient Fault Detection of UHV DC Circuit Breakers

Robust Autonomous Unmanned Aerial Vehicle System for Efficient Tracking of Moving Objects

Efficient Fintuning with Limited Data

Entity Augmentation for Efficient Classification of Vertically Partitioned Data with Limited Overlap.-CafeLLM: Context-Aware Fine-Grained Semantic Clustering using Large Language Models

Adapter-Based Contextualized Meta Embeddings

Advancements in Multimodal Systems

MADP: Multi-modal Sequence Learning for Alzheimer’s Disease Prediction with Missing Data

Multi-modal Spatiotemporal Forecasting via Cross-scale Operator Learning and Spatial Representation Aggregation

Improved VLN-BERT with Reinforcing Endpoint Alignment for Vision-and-Language Navigation

Bridging the Language Gap: Domain-Specific Dataset Construction for Medical LLMs

Integrating Text-to-Image and Vision Language Models for Synergistic Dataset Generation: The Creation of Synergy-General-Multimodal Pairs

Recognition and Reasoning in the Open World

Semantic-Degrade Learning Framework for Open World Object Detection

Multi-modal Prompts with Feature Decoupling for Open-Vocabulary Object Detection

YOLO-FCNET: Enhancing SAR Ship Detection with Fourier Convolution in YOLOv8.

Notes:

Includes bibliographical references and index.

Other Format:

Print version: Guo, Jinyang Generalizing from Limited Resources in the Open World

ISBN:

9789819761258