5 citations · 6 across the 9 of their papers we have counts for
6 papers · 1 filter
BLIP3o-NEXT: Next Frontier of Native Image Generation
Jiuhai Chen, Le Xue, Zhiyang Xu +12
We present BLIP3o-NEXT, a fully open-source foundation model in the BLIP3 series that advances the next frontier of native image generation. BLIP3o-NEXT unifies text-to-image gener…
WALT: Web Agents that Learn Tools
Viraj Prabhu, Yutong Dai, Matthew Fernandez +8
Web agents promise to automate complex browser tasks, but current methods remain brittle -- relying on step-by-step UI interactions and heavy LLM reasoning that break under dynamic…
BLIP3-o: A Family of Fully Open Unified Multimodal Models-Architecture, Training and Dataset
Jiuhai Chen, Zhiyang Xu, Xichen Pan +10
Unifying image understanding and generation has gained growing attention in recent research on multimodal models. Although design choices for image understanding have been extensiv…
ProVision: Programmatically Scaling Vision-centric Instruction Data for Multimodal Language Models
Jieyu Zhang, Le Xue, Linxin Song +11
With the rise of multimodal applications, instruction data has become critical for training multimodal language models capable of understanding complex image-based queries. Existin…
xGen-MM-Vid (BLIP-3-Video): You Only Need 32 Tokens to Represent a Video Even in VLMs
Michael S. Ryoo, Honglu Zhou, Shrikant Kendre +9
We present xGen-MM-Vid (BLIP-3-Video): a multimodal language model for videos, particularly designed to efficiently capture temporal information over multiple frames. BLIP-3-Video…
xGen-VideoSyn-1: High-fidelity Text-to-Video Synthesis with Compressed Representations
Can Qin, Congying Xia, Krithika Ramakrishnan +16
We present xGen-VideoSyn-1, a text-to-video (T2V) generation model capable of producing realistic scenes from textual descriptions. Building on recent advancements, such as OpenAI'…