This work explores the application of Lambda networks, an alternative framework for capturing long-range interactions without attention, for the keyword spotting task.
In this work, we propose an effective approach for training unique embedding representations by combining three simultaneous modalities: image and spoken and textual narratives.
To nevertheless find those relations which can be reliably utilized for learning, we follow a divide-and-conquer strategy: We find reliable similarities by extracting compact groups of images and reliable dissimilarities by partitioning these groups into subsets, converting the complicated overall problem into few reliable local subproblems.
State of the art methods for semantic image segmentation are trained in a supervised fashion using a large corpus of fully labeled training images.
Cell assemblies, originally proposed by Donald Hebb (1949), are subsets of neurons firing in a temporally coordinated way that gives rise to repeated motifs supposed to underly neural representations and information processing.
The major challenges of road detection are dealing with shadows and lighting variations and the presence of other objects in the scene.
We use weakly supervised structured learning to track and disambiguate the identity of multiple indistinguishable, translucent and deformable objects that can overlap for many frames.