341,318 research outputs found
Learning to recognize novel words and novel objects
Reading seems as easy and natural as listening. It is still not clear how we acquire this skill, and how visual word identification mechanisms are refined through reading experience. Theoretical models of word recognition describe general principles of skilled reading behaviour. However, these models have been based on averaged data from relatively small samples of skilled readers, mainly English native speakers, and are based on the assumption that skilled reading involves a specialized system of word identification. In this thesis it is proposed that expert reading requires the development and refinement of basic visual processing mechanisms originally employed to identify everyday objects, and then adapted to reading. To test this hypothesis, I carried out three experiments investigating: (i) how L2 visual word recognition changes with growing proficiency; (ii) how novel lexical memories are integrated into the lexicon, i.e., how they interact with previously existing words; and (iii) how sensitivity to the lexicon statistics plays out in the process of learning a novel set of visual stimuli, either in the language and non--language domain
Learning to recognize occluded and small objects with partial inputs
Recognizing multiple objects in an image is challenging due to occlusions,
and becomes even more so when the objects are small. While promising, existing
multi-label image recognition models do not explicitly learn context-based
representations, and hence struggle to correctly recognize small and occluded
objects. Intuitively, recognizing occluded objects requires knowledge of
partial input, and hence context. Motivated by this intuition, we propose
Masked Supervised Learning (MSL), a single-stage, model-agnostic learning
paradigm for multi-label image recognition. The key idea is to learn
context-based representations using a masked branch and to model label
co-occurrence using label consistency. Experimental results demonstrate the
simplicity, applicability and more importantly the competitive performance of
MSL against previous state-of-the-art methods on standard multi-label image
recognition benchmarks. In addition, we show that MSL is robust to random
masking and demonstrate its effectiveness in recognizing non-masked objects.
Code and pretrained models are available on GitHub
Complex Feature Recognition: A Bayesian Approach for Learning to Recognize Objects
We have developed a new Bayesian framework for visual object recognition which is based on the insight that images of objects can be modeled as a conjunction of local features. This framework can be used to both derive an object recognition algorithm and an algorithm for learning the features themselves. The overall approach, called complex feature recognition or CFR, is unique for several reasons: it is broadly applicable to a wide range of object types, it makes constructing object models easy, it is capable of identifying either the class or the identity of an object, and it is computationally efficient--requiring time proportional to the size of the image. Instead of a single simple feature such as an edge, CFR uses a large set of complex features that are learned from experience with model objects. The response of a single complex feature contains much more class information than does a single edge. This significantly reduces the number of possible correspondences between the model and the image. In addition, CFR takes advantage of a type of image processing called 'oriented energy'. Oriented energy is used to efficiently pre-process the image to eliminate some of the difficulties associated with changes in lighting and pose
Normal and Amnesic Learning, Recognition, and Memory by a Neural Model of Cortico-Hippocampal Interactions
The processes by which humans and other primates learn to recognize objects have been the subject of many models. Processes such as learning, categorization, attention, memory search, expectation, and novelty detection work together at different stages to realize object recognition. In this article, Gail Carpenter and Stephen Grossberg describe one such model class (Adaptive Resonance Theory, ART) and discuss how its structure and function might relate to known neurological learning and memory processes, such as how inferotemporal cortex can recognize both specialized and abstract information, and how medial temporal amnesia may be caused by lesions in the hippocampal formation. The model also suggests how hippocampal and inferotemporal processing may be linked during recognition learning.Air Force Office of Scientific Research (90-0175); British Petroleum (89A-1204); Defense Advanced Research Projects Agency (90-0083); National Science Foundation (IRI-90-00530); Office of Naval Research (N00014-91-J-4100
Learning Membership Functions in a Function-Based Object Recognition System
Functionality-based recognition systems recognize objects at the category
level by reasoning about how well the objects support the expected function.
Such systems naturally associate a ``measure of goodness'' or ``membership
value'' with a recognized object. This measure of goodness is the result of
combining individual measures, or membership values, from potentially many
primitive evaluations of different properties of the object's shape. A
membership function is used to compute the membership value when evaluating a
primitive of a particular physical property of an object. In previous versions
of a recognition system known as Gruff, the membership function for each of the
primitive evaluations was hand-crafted by the system designer. In this paper,
we provide a learning component for the Gruff system, called Omlet, that
automatically learns membership functions given a set of example objects
labeled with their desired category measure. The learning algorithm is
generally applicable to any problem in which low-level membership values are
combined through an and-or tree structure to give a final overall membership
value.Comment: See http://www.jair.org/ for any accompanying file
- …