Addressing issues related to multi-label classification is relevant in many fields of
applications. In this work. We present a multi-label classification architecture based on Multi …

Techniques from sparse signal representation are beginning to see significant impact in
computer vision, often on nontraditional applications where the goal is not just to obtain a …

Image reranking is effective for improving the performance of a text-based image search.
However, existing reranking algorithms are limited for two main reasons: 1) the textual meta …

A simple yet effective multilabel learning method, called label powerset (LP), considers each
distinct combination of labels that exist in the training set as a different class value of a single …

In this paper, we present an operational system for cyber threat intelligence gathering from
various social platforms on the Internet particularly sites on the darknet and deepnet. We …

Sparse coding exhibits good performance in many computer vision applications. However,
due to the overcomplete codebook and the independent coding process, the locality and the …

Sparse coding which encodes the original signal in a sparse signal space, has shown its
state-of-the-art performance in the visual codebook generation and feature quantization …

Recent research has shown the effectiveness of using sparse coding (Sc) to solve many
computer vision problems. Motivated by the fact that kernel trick can capture the nonlinear …

In this work, we develop a shared multi-attention model for multi-label zero-shot learning.
We argue that designing attention mechanism for recognizing multiple seen and unseen …

Recent research has shown the initial success of sparse coding (Sc) in solving many
computer vision tasks. Motivated by the fact that kernel trick can capture the nonlinear …