activity
20192022
most citedCLIP-Art: Contrastive Pre-training for Fine-Grained Art Classification

102 citations · 107 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV2022102 cited

CLIP-Art: Contrastive Pre-training for Fine-Grained Art Classification

Marcos V. Conde, Kerem Turgutlu

Existing computer vision research in artwork struggles with artwork's fine-grained attributes recognition and lack of curated annotated datasets due to their costly creation. To th…

eess.IV20221 cited

Conformer and Blind Noisy Students for Improved Image Quality Assessment

Marcos V. Conde, Maxime Burchi, Radu Timofte

Generative models for image restoration, enhancement, and generation have significantly improved the quality of the generated images. Surprisingly, these models produce more pleasa…

cs.SD20211 cited

Weakly-Supervised Classification and Detection of Bird Sounds in the Wild. A BirdCLEF 2021 Solution

Marcos V. Conde, Kumar Shubham, Prateek Agnihotri +2

It is easier to hear birds than see them, however, they still play an essential role in nature and they are excellent indicators of deteriorating environmental quality and pollutio…

cs.CV20212 cited

Exploring Vision Transformers for Fine-grained Classification

Marcos V. Conde, Kerem Turgutlu

Existing computer vision research in categorization struggles with fine-grained attributes recognition due to the inherently high intra-class variances and low inter-class variance…

cs.CV20191 cited

Multi-attention Networks for Temporal Localization of Video-level Labels

Lijun Zhang, Srinath Nizampatnam, Ahana Gangopadhyay +1

Temporal localization remains an important challenge in video understanding. In this work, we present our solution to the 3rd YouTube-8M Video Understanding Challenge organized by…