Models of object recognition

Riesenhuber, Maximilian; Poggio, Tomaso

doi:10.1038/81479

Cited by 564 publications

(366 citation statements)

References 56 publications

(55 reference statements)

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“…Note that the performance of both models significantly degrades when stimuli are not presented in the center of the receptive field, in marked contrast to the same decoding task applied in the medial superior temporal (MST) area (Mineault et al, 2012), suggesting that computations described in MT represent one element of what is likely a hierarchical computation. That is, the initial selectivity developed in MT is further refined and generalized across spatial positions in higher-level areas such as area MST, as has been suggested for analogous computations in other areas (Riesenhuber and Poggio, 2000).…”

Section: Selectivity and Coding Of Complex Optic Flowmentioning

confidence: 98%

Diverse Suppressive Influences in Area MT and Selectivity to Complex Motion Features

et al. 2013

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Neuronal selectivity results from both excitatory and suppressive inputs to a given neuron. Suppressive influences can often significantly modulate neuronal responses and impart novel selectivity in the context of behaviorally relevant stimuli. In this work, we use a naturalistic optic flow stimulus to explore the responses of neurons in the middle temporal area (MT) of the alert macaque monkey; these responses are interpreted using a hierarchical model that incorporates relevant nonlinear properties of upstream processing in the primary visual cortex (V1). In this stimulus context, MT neuron responses can be predicted from distinct excitatory and suppressive components. Excitation is spatially localized and matches the measured preferred direction of each neuron. Suppression is typically composed of two distinct components: (1) a directionally untuned component, which appears to play the role of surround suppression and normalization; and (2) a direction-selective component, with comparable tuning width as excitation and a distinct spatial footprint that is usually partially overlapping with excitation. The direction preference of this direction-tuned suppression varies widely across MT neurons: approximately one-third have overlapping suppression in the opposite direction as excitation, and many other neurons have suppression with similar direction preferences to excitation. There is also a population of MT neurons with orthogonally oriented suppression. We demonstrate that direction-selective suppression can impart selectivity of MT neurons to more complex velocity fields and that it can be used for improved estimation of the three-dimensional velocity of moving objects. Thus, considering MT neurons in a complex stimulus context reveals a diverse set of computations likely relevant for visual processing in natural visual contexts.

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Section: Selectivity and Coding Of Complex Optic Flowmentioning

confidence: 98%

Diverse Suppressive Influences in Area MT and Selectivity to Complex Motion Features

et al. 2013

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“…The response of an S3 unit can be thought of as encoding the similarity of the input to a previously encountered face at a particular viewpoint. The S3 units are analogous to the "view-tuned units" in [5,6].The cells in the C3 layer pool over the S3 cells preferring each viewpoint of the same face. Thus the pattern of activity over all the C3 units is a vector of similarities to previously encountered template faces invariantly of viewpoint.…”

Section: Simulation Proceduresmentioning

confidence: 99%

How can cells in the anterior medial face patch be viewpoint invariant?

2011

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IntroductionIn a recent paper, Freiwald and Tsao [1] found evidence that the responses of cells in the macaque anterior medial (AM) face patch are invariant to significant changes in viewpoint. The monkey subjects had no prior experience with the individuals depicted in the stimuli and were never given an opportunity to view the same individual from different viewpoints sequentially. These results cannot be explained by a mechanism based only on the temporal association of experienced views.Affine transformations such as translation and scaling can be computed independently of the particular object and are thus generic. On the other hand, non-generic transformations including changes in viewpoint and illumination depend on the 3D structure or material properties of the objects [2].Within a restricted class of objects having a similar 3D structure -like faces -it may be possible to have model cells which are identity-specific and invariant to non-generic transformations using class-specific features [3]. Freiwald and Tsao's finding of a population of cells that appear to be invariant to changes in viewpoint could constitute evidence that the brain is employing face-specific features for this purpose. Simulation ProcedureWe simulate a same-different task by presenting images that contain either the target object or a distractor object on a neutral background. The object may appear at a range of viewpoints. We trained a simple correlation-based classifier on a single view of the target object presented frontally (rotation of 0). The task was for the classifier to rank transformed versions of the target object as being more similar to the trained view than to distractors and rotated distractors. We tested the model with increasing ranges over which we allowed targets and distractors to rotate. The range of viewpoints tested in each condition is plotted as the abscissa in the following figures. The classifier only used the output of the model (C2, S3 or C3) and no other information.

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“…Because familiar real-world objects are more natural stimuli for the visual system, they might have more bound representations than objects that are made up of entirely dissociable low-level features that seem to be stored independently even at the lowest levels of the visual system (e.g., orientation, color, spatial frequency; Magnussen, 2000) and that can be attended separately at encoding (Maunsell & Treue, 2006). Research on object recognition and long-term memory provide some proposals regarding the underlying representations of real-world objects (e.g., Diana, Yonelinas, & Ranganath, 2007;DiCarlo & Cox, 2007;Hummel, 2000;Riesenhuber & Poggio, 2000). In particular, these models typically assume "bound" representations of real-world objects.…”

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Real-world objects are not represented as bound units: Independent forgetting of different object details from visual memory.

Brady¹,

Konkle²,

Alvarez³

et al. 2013

Journal of Experimental Psychology: General

118

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Are real-world objects represented as bound units? Although a great deal of research has examined binding between the feature dimensions of simple shapes, little work has examined whether the featural properties of real-world objects are stored in a single unitary object representation. In a first experiment, we found that information about an object's color is forgotten more rapidly than the information about an object's state (e.g., open, closed), suggesting that observers do not forget objects as entirely bound units. In a second and third experiment, we examined whether state and exemplar information are forgotten separately or together. If these properties are forgotten separately, the probability of getting one feature correct should be independent of whether the other feature was correct. We found that after a short delay, observers frequently remember both state and exemplar information about the same objects, but after a longer delay, memory for the two properties becomes independent. This indicates that information about object state and exemplar are forgotten separately over time. We thus conclude that real-world objects are not represented in a single unitary representation in visual memory.

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Models of object recognition

Cited by 564 publications

References 56 publications

Diverse Suppressive Influences in Area MT and Selectivity to Complex Motion Features

Diverse Suppressive Influences in Area MT and Selectivity to Complex Motion Features

How can cells in the anterior medial face patch be viewpoint invariant?

Real-world objects are not represented as bound units: Independent forgetting of different object details from visual memory.

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