Ecker, Alexander S.

[["dc.bibliographiccitation.firstpage","1"],["dc.bibliographiccitation.issue","183"],["dc.bibliographiccitation.journal","Journal of Machine Learning Research"],["dc.bibliographiccitation.lastpage","61"],["dc.bibliographiccitation.volume","22"],["dc.contributor.author","Weis, Marissa A."],["dc.contributor.author","Chitta, Kashyap"],["dc.contributor.author","Sharma, Yash"],["dc.contributor.author","Brendel, Wieland"],["dc.contributor.author","Bethge, Matthias"],["dc.contributor.author","Geiger, Andreas"],["dc.contributor.author","Ecker, Alexander S."],["dc.date.accessioned","2021-11-01T07:45:24Z"],["dc.date.available","2021-11-01T07:45:24Z"],["dc.date.issued","2020-06-12"],["dc.description.abstract","Perceiving the world in terms of objects and tracking them through time is a crucial prerequisite for reasoning and scene understanding. Recently, several methods have been proposed for unsupervised learning of object-centric representations. However, since these models were evaluated on different downstream tasks, it remains unclear how they compare in terms of basic perceptual abilities such as detection, figure-ground segmentation and tracking of objects. To close this gap, we design a benchmark with four data sets of varying complexity and seven additional test sets featuring challenging tracking scenarios relevant for natural videos. Using this benchmark, we compare the perceptual abilities of four object-centric approaches: ViMON, a video-extension of MONet, based on recurrent spatial attention, OP3, which exploits clustering via spatial mixture models, as well as TBA and SCALOR, which use explicit factorization via spatial transformers. Our results suggest that the architectures with unconstrained latent representations learn more powerful representations in terms of object detection, segmentation and tracking than the spatial transformer based architectures. We also observe that none of the methods are able to gracefully handle the most challenging tracking scenarios despite their synthetic nature, suggesting that our benchmark may provide fruitful guidance towards learning more robust object-centric video representations."],["dc.identifier.arxiv","2006.07034v2"],["dc.identifier.isi","000700307700001"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/92495"],["dc.title","Benchmarking Unsupervised Object Representations for Video Sequences"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","e1009028"],["dc.bibliographiccitation.issue","6"],["dc.bibliographiccitation.journal","PLoS Computational Biology"],["dc.bibliographiccitation.volume","17"],["dc.contributor.author","Burg, Max F."],["dc.contributor.author","Cadena, Santiago A."],["dc.contributor.author","Denfield, George H."],["dc.contributor.author","Walker, Edgar Y."],["dc.contributor.author","Tolias, Andreas S."],["dc.contributor.author","Bethge, Matthias"],["dc.contributor.author","Ecker, Alexander S."],["dc.date.accessioned","2021-08-12T07:45:37Z"],["dc.date.available","2021-08-12T07:45:37Z"],["dc.date.issued","2021"],["dc.description.abstract","Divisive normalization (DN) is a prominent computational building block in the brain that has been proposed as a canonical cortical operation. Numerous experimental studies have verified its importance for capturing nonlinear neural response properties to simple, artificial stimuli, and computational studies suggest that DN is also an important component for processing natural stimuli. However, we lack quantitative models of DN that are directly informed by measurements of spiking responses in the brain and applicable to arbitrary stimuli. Here, we propose a DN model that is applicable to arbitrary input images. We test its ability to predict how neurons in macaque primary visual cortex (V1) respond to natural images, with a focus on nonlinear response properties within the classical receptive field. Our model consists of one layer of subunits followed by learned orientation-specific DN. It outperforms linear-nonlinear and wavelet-based feature representations and makes a significant step towards the performance of state-of-the-art convolutional neural network (CNN) models. Unlike deep CNNs, our compact DN model offers a direct interpretation of the nature of normalization. By inspecting the learned normalization pool of our model, we gained insights into a long-standing question about the tuning properties of DN that update the current textbook description: we found that within the receptive field oriented features were normalized preferentially by features with similar orientation rather than non-specifically as currently assumed."],["dc.description.sponsorship","Open-Access-Publikationsfonds 2021"],["dc.identifier.doi","10.1371/journal.pcbi.1009028"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/88509"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-448"],["dc.relation.eissn","1553-7358"],["dc.relation.orgunit","Institut für Informatik"],["dc.rights","CC BY 4.0"],["dc.title","Learning divisive normalization in primary visual cortex"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]