The ImageNet Large Scale Visual Recognition Challenge is a benchmark in object category classification and detection on hundreds of object categories and millions of images. The challenge has been run annually from 2010 to present, attracting participation from more than fifty institutions. This paper describes the creation of this benchmark dataset and the advances in object recognition that have been possible as a result. We discuss the challenges of collecting large-scale ground truth annotation, highlight key breakthroughs in categorical object recognition, provide a detailed analysis of the current state of the field of large-scale image classification and object detection, and compare the state-of-the-art computer vision accuracy with human accuracy. We conclude with lessons learned in the 5 years of the challenge, and propose future directions and improvements.

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ImageNet Large Scale Visual Recognition Challenge

Prelude. Cycle 1. Introduction. Cycle 2. Structure defines function. Cycle 3. Diversity of cortical functions is provided by inhibition. Cycle 4. Windows on the brain. Cycle 5. A system of rhythms: from simple to complex dynamics. Cycle 6. Synchronization by oscillation. Cycle 7. The brain's default state: self-organized oscillations in rest and sleep. Cycle 8. Perturbation of the default patterns by experience. Cycle 9. The gamma buzz: gluing by oscillations in the waking brain. Cycle 10. Perceptions and actions are brain state-dependent. Cycle 11. Oscillations in the "other cortex:" navigation in real and memory space. Cycle 12. Coupling of systems by oscillations. Cycle 13. The tough problem. References.

Rhythms of the brain

We propose a novel approach to learn and recognize natural scene categories. Unlike previous work, it does not require experts to annotate the training set. We represent the image of a scene by a collection of local regions, denoted as codewords obtained by unsupervised learning. Each region is represented as part of a "theme". In previous work, such themes were learnt from hand-annotations of experts, while our method learns the theme distributions as well as the codewords distribution over the themes without supervision. We report satisfactory categorization performances on a large set of 13 categories of complex scenes.

/pdf/a-bayesian-hierarchical-model-for-learning-natural-scene-zzqtm1es9k.pdf

A Bayesian hierarchical model for learning natural scene categories

Visual processing in cortex is classically modeled as a hierarchy of increasingly sophisticated representations, naturally extending the model of simple to complex cells of Hubel and Wiesel. Surprisingly, little quantitative modeling has been done to explore the biological feasibility of this class of models to explain aspects of higher-level visual processing such as object recognition. We describe a new hierarchical model consistent with physiological data from inferotemporal cortex that accounts for this complex visual task and makes testable predictions. The model is based on a MAX-like operation applied to inputs to certain cortical neurons that may have a general role in cortical function.

/pdf/hierarchical-models-of-object-recognition-in-cortex-33mmp5yhnv.pdf

Hierarchical models of object recognition in cortex

Classical theories of sensory processing view the brain as a passive, stimulus-driven device. By contrast, more recent approaches emphasize the constructive nature of perception, viewing it as an active and highly selective process. Indeed, there is ample evidence that the processing of stimuli is controlled by top-down influences that strongly shape the intrinsic dynamics of thalamocortical networks and constantly create predictions about forthcoming sensory events. We discuss recent experiments indicating that such predictions might be embodied in the temporal structure of both stimulus-evoked and ongoing activity, and that synchronous oscillations are particularly important in this process. Coherence among subthreshold membrane potential fluctuations could be exploited to express selective functional relationships during states of expectancy or attention, and these dynamic patterns could allow the grouping and selection of distributed neuronal responses for further processing.

/pdf/dynamic-predictions-oscillations-and-synchrony-in-top-down-g571k2ka9j.pdf

Dynamic predictions: Oscillations and synchrony in top–down processing

How long does it take for the human visual system to process a complex natural image? Subjectively, recognition of familiar objects and scenes appears to be virtually instantaneous, but measuring this processing time experimentally has proved difficult. Behavioural measures such as reaction times can be used, but these include not only visual processing but also the time required for response execution. However, event-related potentials (ERPs) can sometimes reveal signs of neural processing well before the motor output. Here we use a go/no-go categorization task in which subjects have to decide whether a previously unseen photograph, flashed on for just 20 ms, contains an animal. ERP analysis revealed a frontal negativity specific to no-go trials that develops roughly 150 ms after stimulus onset. We conclude that the visual processing needed to perform this highly demanding task can be achieved in under 150 ms.

Speed of processing in the human visual system.

Speed of processing in the human visual system

Processing scene context: fast categorization and object interference.

Whereas most scientists agree that scene context can influence object recognition, the time course of such object/context interactions is still unknown. To determine the earliest interactions between object and context processing, we used a rapid go/no-go categorization task in which natural scenes were briefly flashed and subjects required to respond as fast as possible to animal targets. Targets were pasted on congruent (natural) or incongruent (urban) contexts. Experiment 1 showed that pasting a target on another congruent background induced performance impairments, whereas segregation of targets on a blank background had very little effect on behavior. Experiment 2 used animals pasted on congruent or incongruent contexts. Context incongruence induced a 10% drop of correct hits and a 16-ms increase in median reaction times, affecting even the earliest behavioral responses. Experiment 3 replicated the congruency effect with other subjects and other stimuli, thus demonstrating its robustness. Object and context must be processed in parallel with continuous interactions possibly through feed-forward co-activation of populations of visual neurons selective to diagnostic features. Facilitation would be induced by the customary co-activation of "congruent" populations of neurons whereas interference would take place when conflictual populations of neurons fire simultaneously.

Early interference of context congruence on object processing in rapid visual categorization of natural scenes.

This study aimed to determine the extent to which rapid visual context categorization relies on global scene statistics, such as diagnostic amplitude spectrum information. We measured performance in a Natural vs. Man-made context categorization task using a set of achromatic photographs of natural scenes equalized in average luminance, global contrast, and spectral energy. Results suggest that the visual system might use amplitude spectrum characteristics of the scenes to speed up context categorization processes. In a second experiment, we measured performance impairments with a parametric degradation of phase information applied to power spectrum averaged scenes. Results showed that performance accuracy was virtually unaffected up to 50% of phase blurring, but then rapidly fell to chance level following a sharp sigmoid curve. Response time analysis showed that subjects tended to make their fastest responses based on the presence of diagnostic man-made information; if no man-made characteristics enable to reach rapidly a decision threshold, because of a natural scene display or a high level of noise, the alternative decision for a natural response became increasingly favored. This two-phase strategy could maximize categorization performance if the diagnostic features of man-made environments tolerate higher levels of noise than natural features, as proposed recently.\r\n\r\nfree access to paper here: http://journalofvision.org/9/1/2/\r\n

Denis Fize

Papers

Speed of processing in the human visual system.

Speed of processing in the human visual system

Processing scene context: fast categorization and object interference.

Early interference of context congruence on object processing in rapid visual categorization of natural scenes.

Rapid visual categorization of natural scene contexts with equalized amplitude spectrum and increasing phase noise.