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Now showing items 11-18 of 18
Scene Graph Parsing as Dependency Parsing
(Center for Brains, Minds and Machines (CBMM), 2018-05-10)
In this paper, we study the problem of parsing structured knowledge graphs from textual descrip- tions. In particular, we consider the scene graph representation that considers objects together with their attributes and ...
Detecting Semantic Parts on Partially Occluded Objects
(Center for Brains, Minds and Machines (CBMM), 2017-09-04)
In this paper, we address the task of detecting semantic parts on partially occluded objects. We consider a scenario where the model is trained using non-occluded images but tested on occluded images. The motivation is ...
Single-Shot Object Detection with Enriched Semantics
(Center for Brains, Minds and Machines (CBMM), 2018-06-19)
We propose a novel single shot object detection network named Detection with Enriched Semantics (DES). Our motivation is to enrich the semantics of object detection features within a typical deep detector, by a semantic ...
Visual concepts and compositional voting
(Center for Brains, Minds and Machines (CBMM), 2018-03-27)
It is very attractive to formulate vision in terms of pattern theory [26], where patterns are defined hierarchically by compositions of elementary building blocks. But applying pattern theory to real world images is very ...
Deep Nets: What have they ever done for Vision?
(Center for Brains, Minds and Machines (CBMM), 2018-05-10)
This is an opinion paper about the strengths and weaknesses of Deep Nets. They are at the center of recent progress on Artificial Intelligence and are of growing importance in Cognitive Science and Neuroscience since they ...
Multi-stage Multi-recursive-input Fully Convolutional Networks for Neuronal Boundary Detection
(Center for Brains, Minds and Machines (CBMM), 2017-10-01)
In the field of connectomics, neuroscientists seek to identify cortical connectivity comprehensively. Neuronal boundary detection from the Electron Microscopy (EM) images is often done to assist the automatic reconstruction ...
Parsing Occluded People by Flexible Compositions
(Center for Brains, Minds and Machines (CBMM), arXiv, 2015-06-01)
This paper presents an approach to parsing humans when there is significant occlusion. We model humans using a graphical model which has a tree structure building on recent work [32, 6] and exploit the connectivity prior ...
Deep Captioning with Multimodal Recurrent Neural Networks (m-RNN)
(Center for Brains, Minds and Machines (CBMM), arXiv, 2015-05-07)
In this paper, we present a multimodal Recurrent Neural Network (m-RNN) model for generating novel image captions. It directly models the probability distribution of generating a word given previous words and an image. ...