Recent years have witnessed the great success of convolutional neural network (CNN) based models in the field of computer vision. CNN is able to learn hierarchically abstracted features from images in an end-to-end training manner. However, most of the existing CNN models only learn features through a feedforward structure and no feedback information from top to bottom layers is exploited to enable the networks to refine themselves. In this paper, we propose a "Learning with Rethinking" algorithm. By adding a feedback layer and producing the emphasis vector, the model is able to recurrently boost the performance based on previous prediction. Particularly, it can be employed to boost any pre-trained models. This algorithm is tested on four object classification benchmark datasets: CIFAR-100, CIFAR-10, MNIST-background-image and ILSVRC-2012 dataset. These results have demonstrated the advantage of training CNN models with the proposed feedback mechanism.
Based on the analysis of response latencies to a newly-presented image, there are two stages of visual processing: a pre-attentive phase and an attentional phase, corresponding to feedforward and recurrent processing respectively @cite_50 . And the feedback connections play an important role in the attentional phase @cite_15 @cite_36 . Different with feedforward connections which directly carry information, the feedback connections primarily play a modulatory role @cite_44 . Experiments have shown that recurrent processing contributes to making object recognition in degraded images more robust @cite_25 .
@cite_36 @cite_44 @cite_50 @cite_15 @cite_25
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A single visual stimulus activates neurons in many different cortical areas. A major challenge in cortical physiology is to understand how the neural activity in these numerous active zones leads to a unified percept of the visual scene. The anatomical basis for these interactions is the dense network of connections that link the visual areas. Within this network, feedforward connections transmit signals from lower-order areas such as V1 or V2 to higher-order areas. In addition, there is a dense web of feedback connections which, despite their anatomical prominence1,2,3,4, remain functionally mysterious5,6,7,8. Here we show, using reversible inactivation of a higher-order area (monkey area V5 MT), that feedback connections serve to amplify and focus activity of neurons in lower-order areas, and that they are important in the differentiation of figure from ground, particularly in the case of stimuli of low visibility. More specifically, we show that feedback connections facilitate responses to objects moving within the classical receptive field; enhance suppression evoked by background stimuli in the surrounding region; and have the strongest effects for stimuli of low salience. This paper reports a dynamic causal modeling study of electrocorticographic (ECoG) data that addresses functional asymmetries between forward and backward connections in the visual cortical hierarchy. Specifically, we ask whether forward connections employ gamma-band frequencies, while backward connections preferentially use lower (beta-band) frequencies. We addressed this question by modeling empirical cross spectra using a neural mass model equipped with superficial and deep pyramidal cell populations—that model the source of forward and backward connections, respectively. This enabled us to reconstruct the transfer functions and associated spectra of specific subpopulations within cortical sources. We first established that Bayesian model comparison was able to discriminate between forward and backward connections, defined in terms of their cells of origin. We then confirmed that model selection was able to identify extrastriate (V4) sources as being hierarchically higher than early visual (V1) sources. Finally, an examination of the auto spectra and transfer functions associated with superficial and deep pyramidal cells confirmed that forward connections employed predominantly higher (gamma) frequencies, while backward connections were mediated by lower (alpha beta) frequencies. We discuss these findings in relation to current views about alpha, beta, and gamma oscillations and predictive coding in the brain. An analysis of response latencies shows that when an image is presented to the visual system, neuronal activity is rapidly routed to a large number of visual areas.However,the activity of cortical neurons is not determined by this feedforward sweep alone. Horizontal connections within areas, and higher areas providing feedback, result in dynamic changes in tuning.The differences between feedforward and recurrent processing could prove pivotal in understanding the distinctions between attentive and pre-attentive vision as well as between conscious and unconscious vision. The feedforward sweep rapidly groups feature constellations that are hardwired in the visual brain, yet is probably incapable of yielding visual awareness; in many cases, recurrent processing is necessary before the features of an object are attentively grouped and the stimulus can enter consciousness. Trends Neurosci. (2000) 23, 571-579 Everyday vision requires robustness to a myriad of environmental factors that degrade stimuli. Foreground clutter can occlude objects of interest, and complex lighting and shadows can decrease the contrast of items. How does the brain recognize visual objects despite these low-quality inputs? On the basis of predictions from a model of object recognition that contains excitatory feedback, we hypothesized that recurrent processing would promote robust recognition when objects were degraded by strengthening bottom-up signals that were weakened because of occlusion and contrast reduction. To test this hypothesis, we used backward masking to interrupt the processing of partially occluded and contrast reduced images during a categorization experiment. As predicted by the model, we found significant interactions between the mask and occlusion and the mask and contrast, such that the recognition of heavily degraded stimuli was differentially impaired by masking. The model provided a close fit of these results in an isomorphic version of the experiment with identical stimuli. The model also provided an intuitive explanation of the interactions between the mask and degradations, indicating that masking interfered specifically with the extensive recurrent processing necessary to amplify and resolve highly degraded inputs, whereas less degraded inputs did not require much amplification and could be rapidly resolved, making them less susceptible to masking. Together, the results of the experiment and the accompanying model simulations illustrate the limits of feedforward vision and suggest that object recognition is better characterized as a highly interactive, dynamic process that depends on the coordination of multiple brain areas.