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Found 19 papers, 0 papers with code
Doubly Reparameterized Importance Weighted Structure Learning for Scene Graph Generation
As a structured prediction task, scene graph generation, given an input image, aims to explicitly model objects and their relationships by constructing a visually-grounded scene graph.
Single-cell Subcellular Protein Localisation Using Novel Ensembles of Diverse Deep Architectures
Unravelling protein distributions within individual cells is key to understanding their function and state and indispensable to developing new treatments.
Importance Weighted Structure Learning for Scene Graph Generation
Scene graph generation is a structured prediction task aiming to explicitly model objects and their relationships via constructing a visually-grounded scene graph for an input image.
Efficient Hybrid Network: Inducting Scattering Features
Recent work showed that hybrid networks, which combine predefined and learnt filters within a single architecture, are more amenable to theoretical analysis and less prone to overfitting in data-limited scenarios.
Constrained Structure Learning for Scene Graph Generation
As a structured prediction task, scene graph generation aims to build a visually-grounded scene graph to explicitly model objects and their relationships in an input image.
Neural Belief Propagation for Scene Graph Generation
Scene graph generation aims to interpret an input image by explicitly modelling the potential objects and their relationships, which is predominantly solved by the message passing neural network models in previous methods.
Learning PAC-Bayes Priors for Probabilistic Neural Networks
We experiment on 6 datasets with different strategies and amounts of data to learn data-dependent PAC-Bayes priors, and we compare them in terms of their effect on test performance of the learnt predictors and tightness of their risk certificate.
Understanding the Distributions of Aggregation Layers in Deep Neural Networks
However, this requires the knowledge of the distributions of the activations of aggregation layers.
ACTNET: end-to-end learning of feature activations and multi-stream aggregation for effective instance image retrieval
We propose a novel CNN architecture called ACTNET for robust instance image retrieval from large-scale datasets.
REMAP: Multi-layer entropy-guided pooling of dense CNN features for image retrieval
On image retrieval datasets Holidays, Oxford and MPEG, the REMAP descriptor achieves mAP of 95. 5%, 91. 5%, and 80. 1% respectively, outperforming any results published to date.
Automatic Delineation of Kidney Region in DCE-MRI
To address this issue, we present Dynamic Mode Decomposition (DMD) coupled with thresholding and blob analysis as a framework for automatic delineation of the kidney region.
Functional Segmentation through Dynamic Mode Decomposition: Automatic Quantification of Kidney Function in DCE-MRI Images
Traditionally, human experts are required to manually delineate the kidney ROI across multiple images in the dynamic sequence.
The Lexical Gap: An Improved Measure of Automated Image Description Quality
The challenge of automatically describing images and videos has stimulated much research in Computer Vision and Natural Language Processing.
Visual Semantic Information Pursuit: A Survey
Since it helps to enhance the accuracy and the consistency of the resulting interpretation, visual context reasoning is often incorporated with visual perception in current deep end-to-end visual semantic information pursuit methods.
Deep Architectures and Ensembles for Semantic Video Classification
This work addresses the problem of accurate semantic labelling of short videos.
Cultivating DNN Diversity for Large Scale Video Labelling
We investigate factors controlling DNN diversity in the context of the Google Cloud and YouTube-8M Video Understanding Challenge.
Siamese Network of Deep Fisher-Vector Descriptors for Image Retrieval
To achieve this, we propose a novel Siamese network.
Can DMD obtain a Scene Background in Color?
A background model describes a scene without any foreground objects and has a number of applications, ranging from video surveillance to computational photography.
Improved Hamming Distance Search Using Variable Length Substrings
To this end, we propose a novel, unsupervised approach to thresholded search in Hamming space, supporting long codes (e. g. 512-bits) with a wide-range of Hamming distance radii.
