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Found 6 papers, 4 papers with code
OpenStreetView-5M: The Many Roads to Global Visual Geolocation
Determining the location of an image anywhere on Earth is a complex visual task, which makes it particularly relevant for evaluating computer vision algorithms.
FeatUp: A Model-Agnostic Framework for Features at Any Resolution
Deep features are a cornerstone of computer vision research, capturing image semantics and enabling the community to solve downstream tasks even in the zero- or few-shot regime.
Ranked #1 on
Feature Upsampling
on ImageNet
A Vision Check-up for Language Models
Although LLM-generated images do not look like natural images, results on image generation and the ability of models to correct these generated images indicate that precise modeling of strings can teach language models about numerous aspects of the visual world.
DreamSim: Learning New Dimensions of Human Visual Similarity using Synthetic Data
Furthermore, our metric outperforms both prior learned metrics and recent large vision models on these tasks.
Axiomatic Explanations for Visual Search, Retrieval, and Similarity Learning
Visual search, recommendation, and contrastive similarity learning power technologies that impact billions of users worldwide.
MosAIc: Finding Artistic Connections across Culture with Conditional Image Retrieval
We introduce MosAIc, an interactive web app that allows users to find pairs of semantically related artworks that span different cultures, media, and millennia.
Cultural Vocal Bursts Intensity Prediction
Image Retrieval
+2
