Large, pretrained models are commonly finetuned with imagery that is heavily augmented to
mimic different conditions and scales, with the resulting models used for various tasks with …

As massively parallel computations have become broadly available with modern GPUs,
deep architectures trained on very large datasets have risen in popularity. Discriminatively …

Remote sensing images present unique challenges to image analysis due to the extensive
geographic coverage, hardware limitations, and misaligned multi-scale images. This paper …

In this paper, we address the problem of learning discriminative part detectors from image
sets with category labels. We propose a novel latent SVM model regularized by group …

Multiple kernel learning (MKL) has been proposed for kernel methods by learning the
optimal kernel from a set of predefined base kernels. However, the traditional L 1 MKL …

Retrieving videos from large repositories using image queries is important for many
applications, such as brand monitoring or content linking. We introduce a new retrieval …

In this paper, a new fine-grained image categorization system that improves spatial pyramid
matching is developed. In this method, multiple cells are combined into cellets in the …

We propose a new model for recognizing human attributes (eg wearing a suit, sitting, short
hair) and actions (eg running, riding a horse) in still images. The proposed model relies on a …

Deriving from the gradient vector of a generative model of local features, Fisher vector
coding (FVC) has been identified as an effective coding method for image classification …

Abstract We propose Regularized Max Pooling (RMP) for image classification. RMP
classifies an image (or an image region) by extracting feature vectors at multiple …