most citedOut of Many, One: Designing and Scaffolding Proteins at the Scale of the Structural Universe with Genie 2

12 citations · 23 across the 7 of their papers we have counts for

collaborators

8 papers

cs.CV2025

HaploOmni: Unified Single Transformer for Multimodal Video Understanding and Generation

Yicheng Xiao, Lin Song, Rui Yang +6

With the advancement of language models, unified multimodal understanding and generation have made significant strides, with model architectures evolving from separated components…

cs.GR2025

Generating 360° Video is What You Need For a 3D Scene

Zhaoyang Zhang, Yannick Hold-Geoffroy, Miloš Hašan +4

Generating 3D scenes is still a challenging task due to the lack of readily available scene data. Most existing methods only produce partial scenes and provide limited navigational…

cs.CV2024

A dual contrastive framework

Yuan Sun, Zhao Zhang, Jorge Ortiz

In current multimodal tasks, models typically freeze the encoder and decoder while adapting intermediate layers to task-specific goals, such as region captioning. Region-level visu…

cs.CV2024★ 5 cited

Artistic Neural Style Transfer Algorithms with Activation Smoothing

Xiangtian Li, Han Cao, Zhaoyang Zhang +3

The works of Gatys et al. demonstrated the capability of Convolutional Neural Networks (CNNs) in creating artistic style images. This process of transferring content images in diff…

cs.CL2024★ 4 cited

Mitigating Knowledge Conflicts in Language Model-Driven Question Answering

Han Cao, Zhaoyang Zhang, Xiangtian Li +3

In the context of knowledge-driven seq-to-seq generation tasks, such as document-based question answering and document summarization systems, two fundamental knowledge sources play…

cs.LG2024★ 1 cited

Research on Key Technologies for Cross-Cloud Federated Training of Large Language Models

Haowei Yang, Mingxiu Sui, Shaobo Liu +3

With the rapid development of natural language processing technology, large language models have demonstrated exceptional performance in various application scenarios. However, tra…