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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 ...
Single units in a deep neural network functionally correspond with neurons in the brain: preliminary results
(Center for Brains, Minds and Machines (CBMM), 2018-11-02)
Deep neural networks have been shown to predict neural responses in higher visual cortex. The mapping from the model to a neuron in the brain occurs through a linear combination of many units in the model, leaving open the ...
DeepVoting: A Robust and Explainable Deep Network for Semantic Part Detection under Partial Occlusion
(Center for Brains, Minds and Machines (CBMM), 2018-06-19)
In this paper, we study the task of detecting semantic parts of an object, e.g., a wheel of a car, under partial occlusion. We propose that all models should be trained without seeing occlusions while being able to transfer ...
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 ...
Biologically-Plausible Learning Algorithms Can Scale to Large Datasets
(Center for Brains, Minds and Machines (CBMM), 2018-09-27)
The backpropagation (BP) algorithm is often thought to be biologically implausible in the brain. One of the main reasons is that BP requires symmetric weight matrices in the feedforward and feed- back pathways. To address ...
When Is Handcrafting Not a Curse?
(2018-12-31)
Recently, with the proliferation of deep learning, there is a strong trend of abandoning handcrafted sys- tems/features in machine learning and AI by replacing them with “end-to-end” systems “learned from scratch”. These ...
Representations That Learn vs. Learning Representations
(2018-12-31)
During the last decade, we have witnessed tremendous progress in Machine Learning and especially the area of Deep Learning, a.k.a. “Learning Representations” (LearnRep for short). There is even an International Conference ...
Constant Modulus Algorithms via Low-Rank Approximation
(Center for Brains, Minds and Machines (CBMM), 2018-04-12)
We present a novel convex-optimization-based approach to the solutions of a family of problems involving constant modulus signals. The family of problems includes the constant modulus and the constrained constant modulus, ...
Image interpretation above and below the object level
(Center for Brains, Minds and Machines (CBMM), 2018-05-10)
Computational models of vision have advanced in recent years at a rapid rate, rivaling in some areas human- level performance. Much of the progress to date has focused on analyzing the visual scene at the object level – ...
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 ...