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Human visual system model

About: Human visual system model is a research topic. Over the lifetime, 8697 publications have been published within this topic receiving 259440 citations.


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TL;DR: In this article, an attention-based few-shot classification weight generator was proposed to unify the recognition of both novel and base categories, which leads to feature representations that generalize better on unseen categories.
Abstract: The human visual system has the remarkably ability to be able to effortlessly learn novel concepts from only a few examples. Mimicking the same behavior on machine learning vision systems is an interesting and very challenging research problem with many practical advantages on real world vision applications. In this context, the goal of our work is to devise a few-shot visual learning system that during test time it will be able to efficiently learn novel categories from only a few training data while at the same time it will not forget the initial categories on which it was trained (here called base categories). To achieve that goal we propose (a) to extend an object recognition system with an attention based few-shot classification weight generator, and (b) to redesign the classifier of a ConvNet model as the cosine similarity function between feature representations and classification weight vectors. The latter, apart from unifying the recognition of both novel and base categories, it also leads to feature representations that generalize better on "unseen" categories. We extensively evaluate our approach on Mini-ImageNet where we manage to improve the prior state-of-the-art on few-shot recognition (i.e., we achieve 56.20% and 73.00% on the 1-shot and 5-shot settings respectively) while at the same time we do not sacrifice any accuracy on the base categories, which is a characteristic that most prior approaches lack. Finally, we apply our approach on the recently introduced few-shot benchmark of Bharath and Girshick [4] where we also achieve state-of-the-art results. The code and models of our paper will be published on: this https URL

88 citations

Journal ArticleDOI
TL;DR: A new color illusion is reported, motion-induced color mixing, in which moving bars are perceived as the mixed color even though the two colors are never superimposed on the retina, indicating that color signals are integrated not only at the same retinal location, but also along a motion trajectory.

88 citations

Book ChapterDOI
15 Dec 1994
TL;DR: This work demonstrates that motion recognition can be accomplished using lower-level motion features, without the use of abstract object models or trajectory representations, and presents a novel low-level computational approach for detecting and recognizing temporally repetitive movements, such as those characteristic of walking people, or flying birds.
Abstract: The goal of this thesis is to demonstrate the utility of low-level motion features for the purpose of recognition. Although motion plays an important role in biological recognition tasks, motion recognition, in general, has received little attention in the literature compared to the volume of work on static object recognition. It has been shown that in some cases, motion information alone is sufficient for human visual system to achieve reliable recognition. Previous attempts at duplicating such capability in machine vision have been based on abstract higher-level models of objects, or have required building intermediate representations such as the trajectories of certain feature points of the object. In this work we demonstrate that motion recognition can be accomplished using lower-level motion features, without the use of abstract object models or trajectory representations. First, we show that certain statistical spatial and temporal features derived from the optic flow field have invariant properties, and can be used to classify regional motion patterns such as ripples on water, fluttering of leaves, and chaotic fluid flow. We then present a novel low-level computational approach for detecting and recognizing temporally repetitive movements, such as those characteristic of walking people, or flying birds, on the basis of the periodic nature of their motion signatures. We demonstrate the techniques on a number of real-world image sequences containing complex non-rigid motion patterns. We also show that the proposed techniques are reliable and efficient by implementing a real-time activity recognition system.

88 citations

Journal ArticleDOI
TL;DR: The experimental results demonstrate the superiority of the proposed reversible visible watermarking scheme compared to the existing methods, and adopts data compression for further reduction in the recovery packet size and improvement in embedding capacity.
Abstract: A reversible (also called lossless, distortion-free, or invertible) visible watermarking scheme is proposed to satisfy the applications, in which the visible watermark is expected to combat copyright piracy but can be removed to losslessly recover the original image. We transparently reveal the watermark image by overlapping it on a user-specified region of the host image through adaptively adjusting the pixel values beneath the watermark, depending on the human visual system-based scaling factors. In order to achieve reversibility, a reconstruction/recovery packet, which is utilized to restore the watermarked area, is reversibly inserted into non-visibly-watermarked region. The packet is established according to the difference image between the original image and its approximate version instead of its visibly watermarked version so as to alleviate its overhead. For the generation of the approximation, we develop a simple prediction technique that makes use of the unaltered neighboring pixels as auxiliary information. The recovery packet is uniquely encoded before hiding so that the original watermark pattern can be reconstructed based on the encoded packet. In this way, the image recovery process is carried out without needing the availability of the watermark. In addition, our method adopts data compression for further reduction in the recovery packet size and improvement in embedding capacity. The experimental results demonstrate the superiority of the proposed scheme compared to the existing methods.

87 citations

Proceedings ArticleDOI
04 Oct 1998
TL;DR: An objective image quality assessment technique which is based on the properties of the human visual system and consists of an early vision model and a visual attention model which indicates regions of interest in a scene through the use of importance maps.
Abstract: We present an objective image quality assessment technique which is based on the properties of the human visual system (HVS). It consists of two major components: an early vision model (multi-channel and designed specifically for complex natural images), and a visual attention model which indicates regions of interest in a scene through the use of importance maps. Visible errors are then weighted, depending on the perceptual importance of the region in which they occur. We show that this technique produces a high correlation with subjective test data (0.93), compared to only 0.65 for PSNR. This technique is particularly useful for images coded with spatially varying quality.

87 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202349
202294
2021279
2020311
2019351
2018348