Call for Papers

We invite submissions on any aspect of generalizing from limited resources in the open world. Considering the recent significant success of large model, in this year, we will include more generalization approaches in openworld for generative AI models. We welcome research contributions related to the following (but not limited to) topics:
  • New methods for in-context learning
  • Applications of large AI models in vertical domains
  • New methods for AI alignment
  • New methods and benchmarks of open set/world learning problem
  • New methods for few-/zero-shot learning
  • New methods for domain-adaptation methods
  • New methods for training generative models under limited data
  • Benchmark for evaluating model generalization
  • Understanding the generalization vulnerabilities of deep learning systems
  • Network sparsity, quantization, distillation, etc.
  • Neural architecture search (NAS)
  • Efficient network architecture design
  • Efficient methods for generative models like diffusion, large language models
  • Hardware implementation and on-device deployment
  • On-device learning
  • Brain-inspired artificial intelligence like spiking neural networks (SNN)
  • Optimization on parallel and distributed training
Submission Format: Submissions need to be anonymized and follow Springer's guidelines and author instructions. The workshop considers two types of submissions: (1) Full Paper: Papers are limited to 12-15 pages; (2) Short Paper: Papers are limited to 6-11 pages.
Peer review: Paper submissions must conform with the “double-blind” review policy. All papers will be peer-reviewed by experts in the field, they will receive at least two reviews. Based on the area chair recommendations, the accepted papers will be allocated either a contributed talk or a poster presentation.
Important: The accepted papers will be published on the proceeding of Communications in Computer and Information Science, indexed in EI-Compendex, etc.
Submission Site: https://openreview.net/group?id=ijcai.org/IJCAI/2024/Workshop/GLOW
Submission Deadline: 26th May, 2024 AoE

Workshop Schedule

Accepted Papers

SpringerLink: https://link.springer.com/book/10.1007/978-981-97-6125-8
  • Towards Point Cloud Compression for Machine Perception: A Simple and Strong Baseline by Learning the Octree Depth Level Predictor
    Lei Liu, Zhihao Hu, Zhenghao Chen
  • Toward Efficient Deep Spiking Neuron Networks: A Survey on Compression
    Hui Xie, Ge Yang, Wenjuan Gao
  • Towards Efficient Fault Detection of Ultra-High Voltage Direct Current Circuit Breakers
    Jiayi Wang, Dong Peng, Shaoqing Chen, Dianbo Zhou, Zhenze Long, Botao Cheng
  • Robust Autonomous Unmanned Aerial Vehicle System for Efficient Tracking of Moving Objects
    Zhiwei Dong, Yang Yong, Zining Wang, Song-Lu Chen, Xu-Cheng Yin
  • Entity Augmentation for Efficient Classification of Vertically Partitioned Data with Limited Overlap
    Avi Amalanshu, Viswesh Nagaswamy, G. V. S. S. Prudhvi, Yash Sirvi
  • CafeLLM: Context-Aware Fine-Grained Semantic Clustering Using Large Language Models
    Ryan Yuki Huang, Colin Robert Small
  • MADP: Multi-modal Sequence Learning for Alzheimer's Disease Prediction with Missing Data
    Yudie Wang, Zirui Wang, Huiyun Gong, Sanwang Wang, Mingzhe Li, Jian Dong
  • Multi-modal Spatiotemporal Forecasting via Cross-Scale Operator Learning and Spatial Representation Aggregation
    Yajun Gao, Tianrui Ma, Chujie Xu, Miao Wang
  • Improved VLN-BERT with Reinforcing Endpoint Alignment for Vision-and-Language Navigation
    Chuan Jin, Boyuan Yang, Ruonan Liu
  • Bridging the Language Gap: Domain-Specific Dataset Construction for Medical LLMs
    Chae Yeon Kim, Song Yeon Kim, Seung Hwan Cho, Young-Min Kim
  • Integrating Text-to-Image and Vision Language Models for Synergistic Dataset Generation: The Creation of Synergy-General-Multimodal Pairs
    Mao Xun Huang, Hen-Hsen Huang
  • Semantic-Degrade Learning Framework for Open World Object Detection
    Siqi He, Cancan Yu, Hainan Li
  • Multi-modal Prompts with Feature Decoupling for Open-Vocabulary Object Detection
    Duorui Wang, Xiaowei Zhao
  • YOLO-FCNET: Enhancing SAR Ship Detection with Fourier Convolution in YOLOv8
    Zihao Zhang, Ying Li