Different types of visual object categories can be found in real-world applications. Some categories are very heterogeneous in terms of local features (broad categories) while oth...
Objects vary in their visual complexity, yet existing discovery methods perform “batch” clustering, paying equal attention to all instances simultaneously—regardless of the ...
We present a category learning vector quantization (cLVQ) approach for incremental and life-long learning of multiple visual categories where we focus on approaching the stability-...
Stephan Kirstein, Heiko Wersing, Horst-Michael Gro...
We consider the problem of visual categorization with minimal supervision during training. We propose a partbased model that loosely captures structural information. We represent ...
We present a novel method for the discovery and detection of visual object categories based on decompositions using topic models. The approach is capable of learning a compact and...