Large Vision-Language Models

Kaiyang Zhou is an Assistant Professor at the Department of Computer Science, Hong Kong Baptist University, working on computer vision and machine learning. He has published more than 30 technical papers in top-tier journals and conferences in relevant fields, including CVPR, ICCV, ECCV, NeurlPS, ICLR, ICML, AAAI, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), and International Journal of Computer Vision (IJCV), with over 10,000 citations received in total. He is an Associate Editor of IJCV, the flagship journal in computer vision, and regularly serves as area chair and senior program committee for top-tier computer vision and machine learning conferences, such as NeurIPS, CVPR, ECCV, and AAAI.Ziwei Liu is an Associate Professor at Nanyang Technological University, Singapore. His research interests include computer vision, machine learning, and computer graphics. He has published extensively with top-tier conferences and journals in relevant fields, including CVPR, ICCV, ECCV, NeurlPS, ICLR, ICML, IEEE Transactions on Pattern Analysis and Machine Intelligence, ACM Transactions on Graphics and Nature - Machine Intelligence. He is the recipient of ICCV Young Researcher Award, HKSTP Best Paper Award, CVPR Best Paper Award Candidate, ICBS Frontiers of Science Award and MIT Technology Review Innovators under 35 Asia Pacific. He serves as an area chair of CVPR, ICCV, ECCV, NeurlPS and ICLR, as well as an associate editor of International Journal of Computer Vision. Peng Gao is a research scientist at Shanghai Artificial Intelligence Laboratory, working on large language models and vision-language models. His research interests include vision-language models, large language models and diffusion models for contents creation. He has published more than 40 papers in top-tier journals and conferences, including International Journal of Computer Vision (IJCV), ICML, ICLR, NeurIPS, CVPR, ICCV and ECCV, receiving more than 10,000 citations. He has led several influential open-source projects including LLaMa-Adapter and the Lumina series, receiving more than 7000 and 2000 stars, respectively.

• Part 1: Pre-training and Datasets.- Chapter 1: LAION-5B: A Massive Open Image-Text Dataset.- Chapter 2: Efficient Training of Large-Scale Vision-Language Models.- Chapter 3: Scaling Laws for Contrastive Language-Image Learning.- Chapter 4: Scaling Up Vision-Language Models for Generic Tasks.- Chapter 5: Searching for Next-Gen Multimodal Datasets.- Part 2: Prompting and Generalization.- Chapter 6: Soft Prompt Learning for Vision-Language Models.- Chapter 7: Unified Prompting for Vision and Language.- Chapter 8: Zero-Shot Image Classification with Custom Prompts.- Chapter 9: Enhancing Vision-Language Models with Feature Adapters.- Chapter 10: Automatic Optimization of Prompting Architectures.- Chapter 11: Open-Vocabulary Calibration for VL Models.- Part 3: Applications.- Chapter 12: Open-Vocabulary DETR with Conditional Matching.- Chapter 13: Extracting Dense Labels from CLIP.- Chapter 14: PointCLIP: Understanding Point Clouds with VL.- Chapter 15: Diffusion-Based Relation Inversion from Images.- Chapter 16: Text-to-Video Generation.- Chapter 17: Text-Driven Human Motion Generation.- Chapter 18: Zero-Shot Text-Driven 3D Avatar Generation.- Chapter 19: Zero-Shot Text-Driven HDR Panorama Generation.