16 accepted papers
A non-negative matrix factorization (NMF) is effectively applied to analyze data in an unsupervised way. Though non-negative factors are endowed with favorable interpretability, such as part-based representation, NMF lacks flexibility, being only applicable to data composed of non-negative values. I…
While deep models are effectively trained based on a softmax cross-entropy loss, a cosine-based softmax loss also works for producing favorable feature embedding.In the cosine-based softmax, temperature plays a crucial role in properly scaling the logits of cosine similarities, though being manually…
This paper proposes a time-series data embedding technique that preserves curvature and orientation, with a focus on visualizing temporal manifold-valued data. Manifold-valued data provide pair-wise local distances on which the proposed method is built. First, we introduce a simpler form of our meth…
Transformer models developed in NLP make a great impact on computer vision fields producing promising performance on various tasks. While multi-head attention a characteristic mechanism of the transformer attracts keen research interest such as for reducing computation cost we analyze the transforme…
A natural image frequently contains multiple classification targets, accordingly providing multiple class labels rather than a single label per image. While the single-label classification is effectively addressed by applying a softmax cross-entropy loss, the multi-label task is tackled mainly in a…
"In various visual classification tasks, we enjoy significant performance improvement by deep convolutional neural networks (CNNs). To further boost performance, it is effective to regularize feature representation learning of CNNs such as by considering margin to improve feature distribution across…
Deep convolutional neural networks (CNNs) leverage large-scale training dataset to produce remarkable performance on various image classification tasks. It, however, is difficult to effectively train the CNNs on some realistic learning situations such as regarding class imbalance, small-scale and la…
Convolutional neural networks (CNNs) contain local pooling to effectively downsize feature maps for increasing computation efficiency as well as robustness to input variations. The local pooling methods are generally formulated in a form of convex combination of local neuron activations for retainin…
In deep convolutional neural networks (CNNs), local pooling operation is a key building block to effectively downsize feature maps for reducing computation cost as well as increasing robustness against input variation. There are several types of pooling operation, such as average/max-pooling, from w…
Deep convolutional neural network (ConvNet) is a promising approach for high-performance image classification. The behavior of ConvNet is analyzed mainly based on the neuron activations, such as by visualizing them. In this paper, in contrast to the activations, we focus on filters which are main co…
A deep neural network is one of the promising approach to produce state-of-the-art performance on various fields such as pattern recognition and signal processing. While the network architecture is intensively studied, as to the network components, non-linear activation functions are the main subjec…
In action recognition, local motion descriptors contribute to effectively representing video sequences where target actions appear in localized spatio-temporal regions. For robust recognition, those fundamental descriptors are required to be invariant against horizontal (mirror) flipping in video fr…
Filter banks on a frequency domain are widely applied and studied mainly for MFCC and its variant methods on speech recognition tasks. In recent years, other types of acoustic features which are derived from image classification literature have attracted attentions for the tasks regarding environmen…
A histogram is an effective form for extracting various types of features and has been attracting keen attention in pattern recognition fields. In visual recognition, however, the histogram features suffer from smoothing due to the processes of quantizing continuous input patterns into discrete code…
Feature matching is a fundamental process in a variety of computer vision tasks. Beyond the standard L2 metric, various methods to measure similarity between features have been proposed mainly on the assumption that the features are defined in a histogram form. On the other hand, in a field of image…
In contrast to category-level or cluster-level classifiers, exemplar SVM is successfully applied to classifying (or detecting) a target object as well as transferring instance-level annotations. The method, however, is formulated in a highly biased classification problem where only one positive samp…