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Found 8 papers, 3 papers with code
A Deep Recurrent Framework for Cleaning Motion Capture Data
We present a deep, bidirectional, recurrent framework for cleaning noisy and incomplete motion capture data.
GeoStyle: Discovering Fashion Trends and Events
Understanding fashion styles and trends is of great potential interest to retailers and consumers alike.
Discovering Underground Maps from Fashion
The fashion sense -- meaning the clothing styles people wear -- in a geographical region can reveal information about that region.
PiCIE: Unsupervised Semantic Segmentation using Invariance and Equivariance in Clustering
With our novel learning objective, our framework can learn high-level semantic concepts.
Ranked #3 on
Unsupervised Semantic Segmentation
on COCO-Stuff-171
Field-Guide-Inspired Zero-Shot Learning
Annotating the full set of attributes for a novel category proves to be a tedious and expensive task in deployment.
Change-Aware Sampling and Contrastive Learning for Satellite Images
While a vast amount of spatio-temporal satellite image data is readily available, most of it remains unlabelled.
Remote Sensing Vision-Language Foundation Models without Annotations via Ground Remote Alignment
We introduce a method to train vision-language models for remote-sensing images without using any textual annotations.
Evolving Interpretable Visual Classifiers with Large Language Models
To address these limitations, we present a novel method that discovers interpretable yet discriminative sets of attributes for visual recognition.
