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Found 10 papers, 4 papers with code
A baseline on continual learning methods for video action recognition
Continual learning has recently attracted attention from the research community, as it aims to solve long-standing limitations of classic supervisedly-trained models.
Neural Transformers for Intraductal Papillary Mucosal Neoplasms (IPMN) Classification in MRI images
Early detection of precancerous cysts or neoplasms, i. e., Intraductal Papillary Mucosal Neoplasms (IPMN), in pancreas is a challenging and complex task, and it may lead to a more favourable outcome.
Correct block-design experiments mitigate temporal correlation bias in EEG classification
We argue that the reason why Li et al. [1] observe such high correlation in EEG data is their unconventional experimental design and settings that violate the basic cognitive neuroscience design recommendations, first and foremost the one of limiting the experiments' duration, as instead done in [2].
Hierarchical Domain-Adapted Feature Learning for Video Saliency Prediction
When the base hierarchical model is empowered with domain-specific modules, performance improves, outperforming state-of-the-art models on three out of five metrics on the DHF1K benchmark and reaching the second-best results on the other two.
Domain Adaptation for Outdoor Robot Traversability Estimation from RGB data with Safety-Preserving Loss
Being able to estimate the traversability of the area surrounding a mobile robot is a fundamental task in the design of a navigation algorithm.
Decoding Brain Representations by Multimodal Learning of Neural Activity and Visual Features
After verifying that visual information can be extracted from EEG data, we introduce a multimodal approach that uses deep image and EEG encoders, trained in a siamese configuration, for learning a joint manifold that maximizes a compatibility measure between visual features and brain representations.
Generative Adversarial Networks Conditioned by Brain Signals
In this work, we build on the latter class of approaches and investigate the possibility of driving and conditioning the image generation process by means of brain signals recorded, through an electroencephalograph (EEG), while users look at images from a set of 40 ImageNet object categories with the objective of generating the seen images.
Deep Learning Human Mind for Automated Visual Classification
In particular, we employ EEG data evoked by visual object stimuli combined with Recurrent Neural Networks (RNN) to learn a discriminative brain activity manifold of visual categories.
Gamifying Video Object Segmentation
Video object segmentation can be considered as one of the most challenging computer vision problems.
Superpixel-Based Video Object Segmentation Using Perceptual Organization and Location Prior
In this paper we present an approach for segmenting objects in videos taken in complex scenes with multiple and different targets.
