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Found 9 papers, 3 papers with code
Approximate Nearest Neighbour Search on Dynamic Datasets: An Investigation
For online feature learning, the Scalable Nearest Neighbours method is faster than baseline for recall rates below 75%.
Delta Descriptors: Change-Based Place Representation for Robust Visual Localization
Visual place recognition is challenging because there are so many factors that can cause the appearance of a place to change, from day-night cycles to seasonal change to atmospheric conditions.
Temporally Coherent Embeddings for Self-Supervised Video Representation Learning
The proposed method exploits inherent structure of unlabeled video data to explicitly enforce temporal coherency in the embedding space, rather than indirectly learning it through ranking or predictive proxy tasks.
Ranked #37 on
Self-Supervised Action Recognition
on UCF101
Generalised Zero-Shot Learning with Domain Classification in a Joint Semantic and Visual Space
Most state-of-the-art GZSL approaches rely on a mapping between latent visual and semantic spaces without considering if a particular sample belongs to the set of seen or unseen classes.
Generalised Zero-Shot Learning with a Classifier Ensemble over Multi-Modal Embedding Spaces
In the context of GZSL, semantic information is non-visual data such as a text description of both seen and unseen classes.
Nearest Neighbour Radial Basis Function Solvers for Deep Neural Networks
The same loss function is used for both the metric learning and classification problems.
Deep Metric Learning and Image Classification with Nearest Neighbour Gaussian Kernels
We present a Gaussian kernel loss function and training algorithm for convolutional neural networks that can be directly applied to both distance metric learning and image classification problems.
Smart Mining for Deep Metric Learning
In this paper, we propose a novel deep metric learning method that combines the triplet model and the global structure of the embedding space.
FANNG: Fast Approximate Nearest Neighbour Graphs
We also provide an efficient search algorithm that uses this graph to rapidly find the nearest neighbour to a query with high probability.
