23 citations · 59 across the 13 of their papers we have counts for
13 papers
CNNs for JPEGs: A Study in Computational Cost
Samuel Felipe dos Santos, Nicu Sebe, Jurandy Almeida
Convolutional neural networks (CNNs) have achieved astonishing advances over the past decade, defining state-of-the-art in several computer vision tasks. CNNs are capable of learni…
Productive Crop Field Detection: A New Dataset and Deep Learning Benchmark Results
Eduardo Nascimento, John Just, Jurandy Almeida +1
In precision agriculture, detecting productive crop fields is an essential practice that allows the farmer to evaluate operating performance separately and compare different seed v…
From Actions to Events: A Transfer Learning Approach Using Improved Deep Belief Networks
Mateus Roder, Jurandy Almeida, Gustavo H. de Rosa +3
In the last decade, exponential data growth supplied machine learning-based algorithms' capacity and enabled their usage in daily-life activities. Additionally, such an improvement…
Budget-Aware Pruning for Multi-Domain Learning
Samuel Felipe dos Santos, Rodrigo Berriel, Thiago Oliveira-Santos +2
Deep learning has achieved state-of-the-art performance on several computer vision tasks and domains. Nevertheless, it still has a high computational cost and demands a significant…
Mixup-based Deep Metric Learning Approaches for Incomplete Supervision
Luiz H. Buris, Daniel C. G. Pedronette, Joao P. Papa +3
Deep learning architectures have achieved promising results in different areas (e.g., medicine, agriculture, and security). However, using those powerful techniques in many real ap…
Improving Transferability of Domain Adaptation Networks Through Domain Alignment Layers
Lucas Fernando Alvarenga e Silva, Daniel Carlos Guimarães Pedronette, Fábio Augusto Faria +2
Deep learning (DL) has been the primary approach used in various computer vision tasks due to its relevant results achieved on many tasks. However, on real-world scenarios with par…
Deep Learning in Beyond 5G Networks with Image-based Time-Series Representation
Lucas Fernando Alvarenga e Silva, Bruno Yuji Lino Kimura, Jurandy Almeida
Towards the network innovation, the Beyond Five-Generation (B5G) networks envision the use of machine learning (ML) methods to predict the network conditions and performance indica…
Less is More: Accelerating Faster Neural Networks Straight from JPEG
Samuel Felipe dos Santos, Jurandy Almeida
Most image data available are often stored in a compressed format, from which JPEG is the most widespread. To feed this data on a convolutional neural network (CNN), a preliminary…
Faster and Accurate Compressed Video Action Recognition Straight from the Frequency Domain
Samuel Felipe dos Santos, Jurandy Almeida
Human action recognition has become one of the most active field of research in computer vision due to its wide range of applications, like surveillance, medical, industrial enviro…
CNNs for JPEGs: A Study in Computational Cost
Samuel Felipe dos Santos, Nicu Sebe, Jurandy Almeida
Convolutional neural networks (CNNs) have achieved astonishing advances over the past decade, defining state-of-the-art in several computer vision tasks. CNNs are capable of learni…
Low-Budget Label Query through Domain Alignment Enforcement
Jurandy Almeida, Cristiano Saltori, Paolo Rota +1
Deep learning revolution happened thanks to the availability of a massive amount of labelled data which have contributed to the development of models with extraordinary inference c…
Bag of Attributes for Video Event Retrieval
Leonardo A. Duarte, Otávio A. B. Penatti, Jurandy Almeida
In this paper, we present the Bag-of-Attributes (BoA) model for video representation aiming at video event retrieval. The BoA model is based on a semantic feature space for represe…
Bag of Genres for Video Retrieval
Leonardo A. Duarte, Otávio A. B. Penatti, Jurandy Almeida
Often, videos are composed of multiple concepts or even genres. For instance, news videos may contain sports, action, nature, etc. Therefore, encoding the distribution of such conc…