Social negative bootstrapping for visual categorization

2011 ◽  
Author(s):  
Xirong Li ◽  
Cees G. M. Snoek ◽  
Marcel Worring ◽  
Arnold W. M. Smeulders
Keyword(s):  
Visual Categorization

Two-stage local constrained sparse coding for fine-grained visual categorization

2017 ◽  
Vol 61 (1) ◽  
Author(s):  
Lihua Guo ◽  
Chenggang Guo ◽  
Lei Li ◽  
Qinghua Huang ◽  
Yanshan Li ◽  
...  
Keyword(s):  
Sparse Coding ◽  
Two Stage ◽  
Visual Categorization ◽  
Fine Grained

scholarly journals Is Perception Stimulus-Dependent?

2021 ◽  
Author(s):  
Sergio Cermeño-Aínsa
Keyword(s):  
Amodal Completion ◽  
Visual Categorization ◽  
Australasian Journal ◽  
Dependence Criterion ◽  
The Senses ◽  
Natural Way

AbstractThe most natural way to distinguish perception from cognition is by considering perception as stimulus-dependent. Perception is tethered to the senses in a way that cognition is not. Beck Australasian Journal of Philosophy 96(2): 319-334 (2018) has recently argued in this direction. He develops this idea by accommodating two potential counterexamples to his account: hallucinations and demonstrative thoughts. In this paper, I examine this view. First, I detect two general problems with movement to accommodate these awkward cases. Subsequently, I place two very common mental phenomena under the prism of the stimulus-dependence criterion: amodal completion and visual categorization. The result is that the stimulus-dependent criterion is too restrictive, it leaves the notion of perception extremely cramped. I conclude that even the criterion of stimulus-dependence fails to mark a clearly defined border between perception and cognition.

scholarly journals A Public Dataset for Fine-Grained Ship Classification in Optical Remote Sensing Images

2021 ◽  
Vol 13 (4) ◽  
pp. 747
Author(s):  
Yanghua Di ◽  
Zhiguo Jiang ◽  
Haopeng Zhang
Keyword(s):  
Remote Sensing ◽  
Image Data ◽  
Remote Sensing Image ◽  
Google Earth ◽  
Optical Remote Sensing ◽  
Remote Sensing Images ◽  
Visual Categorization ◽  
Class Differences ◽  
Fine Grained ◽  
Ship Classification

Fine-grained visual categorization (FGVC) is an important and challenging problem due to large intra-class differences and small inter-class differences caused by deformation, illumination, angles, etc. Although major advances have been achieved in natural images in the past few years due to the release of popular datasets such as the CUB-200-2011, Stanford Cars and Aircraft datasets, fine-grained ship classification in remote sensing images has been rarely studied because of relative scarcity of publicly available datasets. In this paper, we investigate a large amount of remote sensing image data of sea ships and determine most common 42 categories for fine-grained visual categorization. Based our previous DSCR dataset, a dataset for ship classification in remote sensing images, we collect more remote sensing images containing warships and civilian ships of various scales from Google Earth and other popular remote sensing image datasets including DOTA, HRSC2016, NWPU VHR-10, We call our dataset FGSCR-42, meaning a dataset for Fine-Grained Ship Classification in Remote sensing images with 42 categories. The whole dataset of FGSCR-42 contains 9320 images of most common types of ships. We evaluate popular object classification algorithms and fine-grained visual categorization algorithms to build a benchmark. Our FGSCR-42 dataset is publicly available at our webpages.

scholarly journals Effects of stimulus duration and choice delay on visual categorization in pigeons

2009 ◽  
Vol 40 (2) ◽  
pp. 132-146 ◽  
Author(s):  
Olga F. Lazareva ◽  
Edward A. Wasserman
Keyword(s):  
Stimulus Duration ◽  
Visual Categorization

Visual Categorization and Object Representation in Monkeys and Humans

2002 ◽  
Vol 14 (2) ◽  
pp. 187-198 ◽  
Author(s):  
N. Sigala ◽  
F. Gabbiani ◽  
N. K. Logothetis
Keyword(s):  
Object Representation ◽  
Rating Task ◽  
Linear Boundary ◽  
Conditional Probabilities ◽  
Diagnostic Features ◽  
Categorization Task ◽  
Visual Categorization ◽  
Perceptual Features ◽  
Subordinate Categorization

We investigated the influence of a categorization task on the extraction and representation of perceptual features in humans and monkeys. The use of parameterized stimuli (schematic faces and fish) with fixed diagnostic features in combination with a similarity-rating task allowed us to demonstrate perceptual sensitization to the diagnostic dimensions of the categorization task for the monkeys. Moreover, our results reveal important similarities between human and monkey visual subordinate categorization strategies. Neither the humans nor the monkeys compared the new stimuli to class prototypes or based their decisions on conditional probabilities along stimulus dimensions. Instead, they classified each object according to its similarity to familiar members of the alternative categories, or with respect to its position to a linear boundary between the learned categories.

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