475,729 research outputs found
Learning Multi-Scale Representations for Material Classification
The recent progress in sparse coding and deep learning has made unsupervised
feature learning methods a strong competitor to hand-crafted descriptors. In
computer vision, success stories of learned features have been predominantly
reported for object recognition tasks. In this paper, we investigate if and how
feature learning can be used for material recognition. We propose two
strategies to incorporate scale information into the learning procedure
resulting in a novel multi-scale coding procedure. Our results show that our
learned features for material recognition outperform hand-crafted descriptors
on the FMD and the KTH-TIPS2 material classification benchmarks
Cortical mechanisms of sensory learning and object recognition
Learning about the world through our senses constrains our ability to recognise our surroundings. Experience shapes perception. What is the neural basis for object recognition and how are learning-induced changes in recognition manifested in neural populations? We consider first the location of neurons that appear to be critical for object recognition, before describing what is known about their function. Two complementary processes of object recognition are considered: discrimination among diagnostic object features and generalization across non-diagnostic features. Neural plasticity appears to underlie the development of discrimination and generalization for a given set of features, though tracking these changes directly over the course of learning has remained an elusive task
Multi-level 3D CNN for Learning Multi-scale Spatial Features
3D object recognition accuracy can be improved by learning the multi-scale
spatial features from 3D spatial geometric representations of objects such as
point clouds, 3D models, surfaces, and RGB-D data. Current deep learning
approaches learn such features either using structured data representations
(voxel grids and octrees) or from unstructured representations (graphs and
point clouds). Learning features from such structured representations is
limited by the restriction on resolution and tree depth while unstructured
representations creates a challenge due to non-uniformity among data samples.
In this paper, we propose an end-to-end multi-level learning approach on a
multi-level voxel grid to overcome these drawbacks. To demonstrate the utility
of the proposed multi-level learning, we use a multi-level voxel representation
of 3D objects to perform object recognition. The multi-level voxel
representation consists of a coarse voxel grid that contains volumetric
information of the 3D object. In addition, each voxel in the coarse grid that
contains a portion of the object boundary is subdivided into multiple
fine-level voxel grids. The performance of our multi-level learning algorithm
for object recognition is comparable to dense voxel representations while using
significantly lower memory.Comment: CVPR 2019 workshop on Deep Learning for Geometric Shape Understandin
- …