Learning viewpoint invariant object representations using a temporal coherence principle

63 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Lifelong Learning of Spatiotemporal Representations With Dual-Memory Recurrent Self-Organization (2018, background): “However, specialized mechanisms of slow feature analysis can be implemented that would yield invariance to complex input transformations such as view invariance (e.g., Berkes and Wiskott, 2005; Einhäuser et al., 2005).”
A Structured Model of Video Reproduces Primary Visual Cortical Organisation (2009, methodology): “In [80,82], the representation is invariant to frequent transformations, such as translation, and the corresponding attribute information (e.g., position) is discarded.”
Gaze allocation in natural stimuli: Comparing free exploration to head-fixed viewing conditions (2009, background): “These results highlight that temporal continuity is a key principle not only for object recognition (Einhauser, Hipp, Eggert, Korner, & Konig, 2005 ; Wallis & Rolls, 1997 ), but also for attention deployment under natural conditions.”
Learning and disrupting invariance in visual recognition (2011, methodology): “Temporal association methods are attractive solutions to the problem of invariance development or learning [1] [7] [8] [9] [10] [11] [12] [13] [14].”