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Found 9 papers, 3 papers with code
Efficient Keyword Spotting by capturing long-range interactions with Temporal Lambda Networks
This work explores the application of Lambda networks, an alternative framework for capturing long-range interactions without attention, for the keyword spotting task.
Transcription-Enriched Joint Embeddings for Spoken Descriptions of Images and Videos
In this work, we propose an effective approach for training unique embedding representations by combining three simultaneous modalities: image and spoken and textual narratives.
Unsupervised Representation Learning by Discovering Reliable Image Relations
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.
CEREALS - Cost-Effective REgion-based Active Learning for Semantic Segmentation
State of the art methods for semantic image segmentation are trained in a supervised fashion using a large corpus of fully labeled training images.
Cost efficient gradient boosting
In contrast to previous approaches to learning with cost penalties, our method can grow very deep trees that on average are nonetheless cheap to compute.
Sparse convolutional coding for neuronal assembly detection
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.
Structured Regression Gradient Boosting
We propose a new way to train a structured output prediction model.
Road Detection via On--line Label Transfer
The major challenges of road detection are dealing with shadows and lighting variations and the presence of other objects in the scene.
Tracking Indistinguishable Translucent Objects over Time using Weakly Supervised Structured Learning
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.
