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Found 8 papers, 1 papers with code
Deep Metric Learning with Soft Orthogonal Proxies
Deep Metric Learning (DML) models rely on strong representations and similarity-based measures with specific loss functions.
Image-based eeg classification of brain responses to song recordings
Classifying EEG responses to naturalistic acoustic stimuli is of theoretical and practical importance, but standard approaches are limited by processing individual channels separately on very short sound segments (a few seconds or less).
Multi-Head Deep Metric Learning Using Global and Local Representations
On the other hand, the proxy-based loss functions often lead to significant speedups in convergence during training, while the rich relations among data points are often not fully explored by the proxy-based losses.
End-to-End Auditory Object Recognition via Inception Nucleus
Machine learning approaches to auditory object recognition are traditionally based on engineered features such as those derived from the spectrum or cepstrum.
WW-Nets: Dual Neural Networks for Object Detection
Inspired by this structure, we have proposed an object detection framework involving the integration of a "What Network" and a "Where Network".
Ventral-Dorsal Neural Networks: Object Detection via Selective Attention
The coarse functional distinction between these streams is between object recognition -- the "what" of the signal -- and extracting location related information -- the "where" of the signal.
FAST OBJECT LOCALIZATION VIA SENSITIVITY ANALYSIS
We demonstrate that a simple linear mapping can be learned from sensitivity maps to bounding box coordinates, localizing the recognized object.
Image captioning with weakly-supervised attention penalty
Ideally, attention maps predicted by captioning models should be consistent with intrinsic attentions from visual models for any given visual concept.
