On Aggregation of Unsupervised Deep Binary Descriptor With Weak Bits

Unsupervised Object Discovery and Co-Localization by Deep Descriptor Transforming

Reusable model design becomes desirable with the rapid expansion of computer vision and machine learning applications. In this paper, we focus on the reusability of pre-trained deep convolutional models. Specifically, different from treating pre-trained models as feature extractors, we reveal more treasures beneath convolutional layers, i.e., the convolutional activations could act as a detecto...

Unsupervised Semantic-based Aggregation of Deep Convolutional Features

In this paper, we propose a simple but effective semantic-based aggregation (SBA) method. The proposed SBA utilizes the discriminative filters of deep convolutional layers as semantic detectors. Moreover, we propose the effective unsupervised strategy to select some semantic detectors to generate the “probabilistic proposals”, which highlight certain discriminative pattern of objects and suppre...

Unsupervised self-organizing texture descriptor

We propose a local texture descriptor based on a pyramidal composition of Self Organizing Map (SOM). As with the SOM model, our visual descriptor presents two operational steps: a first unsupervised learning phase and a second mapping phase involving a dimensionality reduction of the input data. During the first step a large number of image patches, including different classes of textures, are ...

Unbiased Bits from Sources of Weak

? A new model for weak random physical sources is presented. The new model strictly generalizes previous models (e.g. the Santha and Vazirani model 24]). The sources considered output strings according to probability distributions in which no single string is too probable. The new model provides a fruitful viewpoint on problems studied previously as: Extracting almost perfect bits from sources ...

Prime Numbers with a Fixed Number of One Bits or Zero Bits in Their Binary Representation