A vision architecture for unconstrained and incremental learning of multiple categories
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Citations
A life-long learning vector quantization approach for interactive learning of multiple categories
Self-Understanding and Self-Extension: A Systems and Representational Approach
A system for interactive learning in dialogue with a tutor
Metric learning for sequences in relational LVQ
An integrated system for interactive continuous learning of categorical knowledge
References
A growing neural gas network learns topologies. G. Tesauro, DS Touretzky, and TK Leen, editors
Categorizing Nine Visual Classes using Local Appearance Descriptors
Learning optimized features for hierarchical models of invariant object recognition
Life-long learning cell structures —continuously learning without catastrophic interference
2005 Special issue: Incremental learning of feature space and classifier for face recognition
Related Papers (5)
Frequently Asked Questions (16)
Q2. What is the purpose of the forward feature selection method?
The forward feature selection method is used to find low dimensional subsets of category-specific features by predominately selecting features, which occur almost exclusively for a certain category.
Q3. Why do the authors remove the noise from the initial foreground hypothesis?
Due to the fact that the objects are presented by hand, skin color parts in the segment are systematic noise, which the authors remove from the initial foreground hypothesis based on the detection method proposed by Fritsch et al. (2002).
Q4. What is the main drawback of those architectures commonly used for identification tasks?
The major drawback of those architectures commonly used for identification tasks is the inefficient separation of cooccuring categories.
Q5. What is the reason for the indepen-dent representation of categories?
It seems to be that for their categorization task the indepen-dent representation of categories somehow weakens the forgetting effect of SLP networks.
Q6. What is the common strategy for life-long learning architectures?
A common strategy for life-long learning architectures e.g. (Hamker, 2001; Furao & Hasegawa, 2006; Kirstein et al., 2008) is the usage of a node specific learning rate combined with an incremental node insertion rule.
Q7. What are the advantages of these approaches?
The advantages of these approaches are their robustness against partial occlusion, scale changes, and the ability to deal with cluttered environments.
Q8. What is the reason for the poor performance of color categories?
For color categories the effect of imprecise foreground masks on the categorization performance seems also to be only minor, otherwise the performance would be considerably lower.
Q9. What is the way to test the representation of objects?
This allows that object views can be first used to test the STM and LTM representation and after providing confirmed labels the same views can also be used to enhance the representation by transferring them into the STM, even if they where recorded before the confirmation.
Q10. How is the learning of the category specific LTM based on the current available feature vectors?
Based on the currently available feature vectors, the learning methods are used to incorporate this STM knowledge into the LTM by applying the learning dynamics of the cLVQ method described in Section 4.2.3.
Q11. How do the authors relax the separation of object views?
To relax this separation and to make the most efficient use of object views, the authors introduce a sensory memory concept for temporarily remembering views of the currently attended object, by using the same one-shot learning method as used for the STM.
Q12. What is the computation of local edge responses?
(1)This computation of local edge responses is restricted to the positions in the foreground mask with ξi(x, y) > 0, whereas the ∗ denotes the inner product of two vectors.
Q13. What is the main problem when dealing with learning in unconstrained environments?
One of the essential problems when dealing with learning in unconstrained environments is the definition of a shared attention concept between the learning system and the human tutor.
Q14. How is the hypothesis list communicated to the user?
Additionally the hypothesis list is repeatedly communicated to the user (in 5 second intervals), while newly acquired segments are also used to refine this list.
Q15. What is the effect of the constraint on the learning task?
Additionally this constraint strongly reduces the appearance variations of the presented objects and therefore makes the category learning task much easier.
Q16. How does the learning system perform the necessary processing steps?
The authors could show that their learning system can efficiently perform all necessary processing steps including figure-ground segregation, feature extraction and incremental learning.