[HTML][HTML] New ideas and trends in deep multimodal content understanding: A review
The focus of this survey is on the analysis of two modalities of multimodal deep learning:
image and text. Unlike classic reviews of deep learning where monomodal image classifiers …
image and text. Unlike classic reviews of deep learning where monomodal image classifiers …
Modeling multi-label action dependencies for temporal action localization
Real world videos contain many complex actions with inherent relationships between action
classes. In this work, we propose an attention-based architecture that model these action …
classes. In this work, we propose an attention-based architecture that model these action …
Order-free rnn with visual attention for multi-label classification
We propose a recurrent neural network (RNN) based model for image multi-label
classification. Our model uniquely integrates and learning of visual attention and Long Short …
classification. Our model uniquely integrates and learning of visual attention and Long Short …
Wavelet convolutional neural networks
S Fujieda, K Takayama, T Hachisuka - ar**_urban_impervious_surface_by_fusing_optical_and_SAR_data_based_on_the_random_forests_and_D-S_theory/links/5b2e86d94585150d23ca9ef3/Map**-urban-impervious-surface-by-fusing-optical-and-SAR-data-based-on-the-random-forests-and-D-S-theory.pdf" data-clk="hl=en&sa=T&oi=gga&ct=gga&cd=5&d=6114350579894116231&ei=aMKlZ5mwLpmp6rQPqKK8iA8" data-clk-atid="h9_KCnWJ2lQJ" target="_blank">[PDF] researchgate.net
A survey and analysis on automatic image annotation
Q Cheng, Q Zhang, P Fu, C Tu, S Li - Pattern Recognition, 2018 - Elsevier
In recent years, image annotation has attracted extensive attention due to the explosive
growth of image data. With the capability of describing images at the semantic level, image …
growth of image data. With the capability of describing images at the semantic level, image …
[PDF][PDF] Semi-Supervised Robust Deep Neural Networks for Multi-Label Classification.
In this paper, we propose a robust method for semisupervised training of deep neural
networks for multi-label image classification. To this end, we use ramp loss, which is more …
networks for multi-label image classification. To this end, we use ramp loss, which is more …
Semantic regularisation for recurrent image annotation
The" CNN-RNN" design pattern is increasingly widely applied in a variety of image
annotation tasks including multi-label classification and captioning. Existing models use the …
annotation tasks including multi-label classification and captioning. Existing models use the …
Tensor normalization and full distribution training
W Fuhl - arxiv preprint arxiv:2109.02345, 2021 - arxiv.org
In this work, we introduce pixel wise tensor normalization, which is inserted after rectifier
linear units and, together with batch normalization, provides a significant improvement in the …
linear units and, together with batch normalization, provides a significant improvement in the …
Double attention based on graph attention network for image multi-label classification
The task of image multi-label classification is to accurately recognize multiple objects in an
input image. Most of the recent works need to leverage the label co-occurrence matrix …
input image. Most of the recent works need to leverage the label co-occurrence matrix …