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Found 11 papers, 2 papers with code
From Actions to Events: A Transfer Learning Approach Using Improved Deep Belief Networks
In the last decade, exponential data growth supplied machine learning-based algorithms' capacity and enabled their usage in daily-life activities.
Parameter Sharing in Budget-Aware Adapters for Multi-Domain Learning
This work proposes a method capable of adapting to a user-defined budget while encouraging parameter sharing among domains.
Mixup-based Deep Metric Learning Approaches for Incomplete Supervision
Deep learning architectures have achieved promising results in different areas (e. g., medicine, agriculture, and security).
Improving Transferability of Domain Adaptation Networks Through Domain Alignment Layers
Deep learning (DL) has been the primary approach used in various computer vision tasks due to its relevant results achieved on many tasks.
Multi-Source Unsupervised Domain Adaptation
Unsupervised Domain Adaptation
Less is More: Accelerating Faster Neural Networks Straight from JPEG
Most image data available are often stored in a compressed format, from which JPEG is the most widespread.
How Far Can We Get with Neural Networks Straight from JPEG?
In this paper, we investigate the usage of CNNs that are designed to work directly with the DCT coefficients available in JPEG compressed images, proposing a handcrafted and data-driven techniques for reducing the computational complexity and the number of parameters for these models in order to keep their computational cost similar to their RGB baselines.
Faster and Accurate Compressed Video Action Recognition Straight from the Frequency Domain
For this reason, a preliminary decoding process is required, since video data are often stored in a compressed format.
Low-Budget Label Query through Domain Alignment Enforcement
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 capabilities.
Multimedia Retrieval Through Unsupervised Hypergraph-Based Manifold Ranking
In this paper, a novel manifold ranking algorithm is proposed based on the hypergraphs for unsupervised multimedia retrieval tasks.
Bag of Attributes for Video Event Retrieval
This model is used to map low-level frame vectors into high-level vectors (e. g., classifier probability scores).
Bag of Genres for Video Retrieval
The Bag of Genres video vector contains a summary of the activations of each genre in the video content.
