Summary of Watermarking Training Data Of Music Generation Models, by Pascal Epple et al.
Watermarking Training Data of Music Generation Modelsby Pascal Epple, Igor Shilov, Bozhidar Stevanoski, Yves-Alexandre de…
Summary of From Multimodal Llms to Generalist Embodied Agents: Methods and Lessons, by Andrew Szot et al.
From Multimodal LLMs to Generalist Embodied Agents: Methods and Lessonsby Andrew Szot, Bogdan Mazoure, Omar…
Summary of Language-guided Image Tokenization For Generation, by Kaiwen Zha et al.
Language-Guided Image Tokenization for Generationby Kaiwen Zha, Lijun Yu, Alireza Fathi, David A. Ross, Cordelia…
Summary of Enhancing Foundation Models For Time Series Forecasting Via Wavelet-based Tokenization, by Luca Masserano et al.
Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenizationby Luca Masserano, Abdul Fatir Ansari,…
Summary of Linvt: Empower Your Image-level Large Language Model to Understand Videos, by Lishuai Gao et al.
LinVT: Empower Your Image-level Large Language Model to Understand Videosby Lishuai Gao, Yujie Zhong, Yingsen…
Summary of Efficient Long Video Tokenization Via Coordinate-based Patch Reconstruction, by Huiwon Jang et al.
Efficient Long Video Tokenization via Coordinate-based Patch Reconstructionby Huiwon Jang, Sihyun Yu, Jinwoo Shin, Pieter…
Summary of Adaptive Length Image Tokenization Via Recurrent Allocation, by Shivam Duggal et al.
Adaptive Length Image Tokenization via Recurrent Allocationby Shivam Duggal, Phillip Isola, Antonio Torralba, William T.…
Summary of Adapting Language Models Via Token Translation, by Zhili Feng et al.
Adapting Language Models via Token Translationby Zhili Feng, Tanya Marwah, Nicolo Fusi, David Alvarez-Melis, Lester…
Summary of Multitok: Variable-length Tokenization For Efficient Llms Adapted From Lzw Compression, by Noel Elias et al.
MultiTok: Variable-Length Tokenization for Efficient LLMs Adapted from LZW Compressionby Noel Elias, Homa Esfahanizadeh, Kaan…
Summary of Xgen-mm-vid (blip-3-video): You Only Need 32 Tokens to Represent a Video Even in Vlms, by Michael S. Ryoo et al.
xGen-MM-Vid (BLIP-3-Video): You Only Need 32 Tokens to Represent a Video Even in VLMsby Michael…