Abstract: In the computer vision literature, many effective histogram-based features have been developed. These engineered features include local binary patterns and edge histogram descriptors among others and they have been shown to be informative features for a variety of computer… Read More
Tag: #Image Classification
Bag-level classification network for infrared target detection accepted to SPIE, 2022!
Congratulations to our labmates and collaborators: Connor H. McCurley, Daniel Rodriguez, Chandler Trousdale, Arielle Stevens, Anthony Baldino, Eugene Li, Isabella Perlmutter, and Alina Zare. Their paper, “Bag-level classification network for infrared target detection”, was recently accepted to Proc. SPIE 12096, Automatic… Read More
Lace for image classification accepted to WACV 2022!
Congratulations to our labmates: Joshua Peeples, Connor McCurley, Sarah Walker, Dylan Stewart and Alina Zare! Their paper, “LEARNABLE ADAPTIVE COSINE ESTIMATOR (LACE) FOR IMAGE CLASSIFICATION “, was recently accepted to IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2022.… Read More
Learnable Adaptive Cosine Estimator (LACE) for Image Classification
Abstract: In this work, we propose a new loss to improve feature discriminability and classification performance. Motivated by the adaptive cosine/coherence estimator [42] (ACE), our proposed method incorporates angular information that is inherently learned by artificial neural networks. Our learnable… Read More