Search Results for author:
Found 10 papers, 7 papers with code
Exploiting CLIP-based Multi-modal Approach for Artwork Classification and Retrieval
Given the recent advances in multimodal image pretraining where visual models trained with semantically dense textual supervision tend to have better generalization capabilities than those trained using categorical attributes or through unsupervised techniques, in this work we investigate how recent CLIP model can be applied in several tasks in artwork domain.
Composed Image Retrieval using Contrastive Learning and Task-oriented CLIP-based Features
Given a query composed of a reference image and a relative caption, the Composed Image Retrieval goal is to retrieve images visually similar to the reference one that integrates the modifications expressed by the caption.
Ranked #6 on
Image Retrieval
on CIRR
Learning advisor networks for noisy image classification
In this paper, we introduced the novel concept of advisor network to address the problem of noisy labels in image classification.
Ranked #6 on
Image Classification
on Clothing1M
(using extra training data)
Conditioned and Composed Image Retrieval Combining and Partially Fine-Tuning CLIP-Based Features
The proposed method is based on an initial training stage where a simple combination of visual and textual features is used, to fine-tune the CLIP text encoder.
Ranked #3 on
Image Retrieval
on LaSCo
Composed Image Retrieval (CoIR)
Content-Based Image Retrieval
+2
Effective Conditioned and Composed Image Retrieval Combining CLIP-Based Features
the visual content of the query image.
Ranked #9 on
Image Retrieval
on CIRR
Learning Group Activities from Skeletons without Individual Action Labels
To understand human behavior we must not just recognize individual actions but model possibly complex group activity and interactions.
Ranked #8 on
Group Activity Recognition
on Volleyball
Image Retrieval using Multi-scale CNN Features Pooling
In this paper, we address the problem of image retrieval by learning images representation based on the activations of a Convolutional Neural Network.
Am I Done? Predicting Action Progress in Videos
In this paper we deal with the problem of predicting action progress in videos.
Automatic Image Annotation via Label Transfer in the Semantic Space
Automatic image annotation is among the fundamental problems in computer vision and pattern recognition, and it is becoming increasingly important in order to develop algorithms that are able to search and browse large-scale image collections.
Socializing the Semantic Gap: A Comparative Survey on Image Tag Assignment, Refinement and Retrieval
Where previous reviews on content-based image retrieval emphasize on what can be seen in an image to bridge the semantic gap, this survey considers what people tag about an image.
