732 citations
- Microsoft (United States)US47 papers
- Massachusetts Institute of TechnologyUS32 papers
- University of WashingtonUS31 papers
- Carnegie Mellon UniversityUS29 papers
- University of Science and Technology of ChinaCN28 papers
- Peking UniversityCN26 papers
- Stanford UniversityUS24 papers
- University of CambridgeGB24 papers
- Tsinghua UniversityCN22 papers
- University of California, BerkeleyUS21 papers
- University College LondonGB16 papers
- Georgia Institute of TechnologyUS14 papers
100 papers · 1 filter
Enhancing Deformable Local Features by Jointly Learning to Detect and Describe Keypoints
Guilherme Potje, Felipe Cadar, Andre Araujo +2
Local feature extraction is a standard approach in computer vision for tackling important tasks such as image matching and retrieval. The core assumption of most methods is that im…
Video Summarization Overview
Mayu Otani, Yale Song, Yang Wang
With the broad growth of video capturing devices and applications on the web, it is more demanding to provide desired video content for users efficiently. Video summarization facil…
Visio-Linguistic Brain Encoding
Subba Reddy Oota, Jashn Arora, Vijay Rowtula +2
Enabling effective brain-computer interfaces requires understanding how the human brain encodes stimuli across modalities such as visual, language (or text), etc. Brain encoding ai…
NVS-MonoDepth: Improving Monocular Depth Prediction with Novel View Synthesis
Zuria Bauer, Zuoyue Li, Sergio Orts-Escolano +3
Building upon the recent progress in novel view synthesis, we propose its application to improve monocular depth estimation. In particular, we propose a novel training method split…
A Picture is Worth a Thousand Words: A Unified System for Diverse Captions and Rich Images Generation
Yupan Huang, Bei Liu, Jianlong Fu +1
A creative image-and-text generative AI system mimics humans' extraordinary abilities to provide users with diverse and comprehensive caption suggestions, as well as rich image cre…
Context-LGM: Leveraging Object-Context Relation for Context-Aware Object Recognition
Mingzhou Liu, Xinwei Sun, Fandong Zhang +2
Context, as referred to situational factors related to the object of interest, can help infer the object's states or properties in visual recognition. As such contextual features a…