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Computer Vision

Enabling computers and devices to understand what they see.

Microsoft at CVPR 2024: Innovations in computer vision and AI research

DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models

Exploring how context, culture, and character matter in avatar research

FeatUp: A Model-Agnostic Framework for Features at Any Resolution

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Published Date

Publication

IntrinsicNeRF: Learning Intrinsic Neural Radiance Fields for Editable Novel View Synthesis

Weicai Ye, Shuo Chen, Chong Bao, Hujun Bao, Marc Pollefeys, Zhaopeng Cui, Guofeng Zhang

ICCV 2023 | September 2023

Publication

Kosmos-2.5: A Multimodal Literate Model

Tengchao Lv, Yupan Huang, Jingye Chen, Lei Cui, Shuming Ma, Yaoyao Chang, Shaohan Huang, Wenhui Wang, Li Dong, Weiyao Luo, Shaoxiang Wu, Guoxin Wang, Cha Zhang, Furu Wei

September 2023

Project

Publication

Efficient Discovery and Effective Evaluation of Visual Perceptual Similarity: A Benchmark and Beyond

Oren Barkan, Tal Reiss, Jonathan Weill, Ori Katz, Roy Hirsch, Itzik Malkiel, Noam Koenigstein

August 2023

Publication

SimDA: Simple Diffusion Adapter for Efficient Video Generation

Zhen Xing, Qi Dai, Han Hu, Zuxuan Wu, Yu-Gang Jiang

CVPR 2024 | August 2023

Publication

DragNUWA: Fine-grained Control in Video Generation by Integrating Text, Image, and Trajectory

Shengming Yin, Chenfei Wu, Jian Liang, Jie Shi, Houqiang Li, Gong Ming, Nan Duan

August 2023

Project

DragNUWA

DragNUWA is a video generation model that utilizes text, images, and trajectory as three essential control factors to facilitate highly controllable video generation. DragNUWA is a video generation model that utilizes text, images, and trajectory…

Publication