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Found 10 papers, 1 papers with code
DNN-Compressed Domain Visual Recognition with Feature Adaptation
For comparison, in addition to the performance results that are obtained using our proposed latent-based compressed-domain method, we also present performance results using compressed but fully decoded images in the pixel domain as well as original uncompressed images.
Learning-based Compression for Material and Texture Recognition
This motivated the development of new learning-based visual compression standards such as JPEG-AI.
Towards Imperceptible Universal Attacks on Texture Recognition
Although deep neural networks (DNNs) have been shown to be susceptible to image-agnostic adversarial attacks on natural image classification problems, the effects of such attacks on DNN-based texture recognition have yet to be explored.
A Study for Universal Adversarial Attacks on Texture Recognition
Given the outstanding progress that convolutional neural networks (CNNs) have made on natural image classification and object recognition problems, it is shown that deep learning methods can achieve very good recognition performance on many texture datasets.
Frequency-Tuned Universal Adversarial Attacks
Researchers have shown that the predictions of a convolutional neural network (CNN) for an image set can be severely distorted by one single image-agnostic perturbation, or universal perturbation, usually with an empirically fixed threshold in the spatial domain to restrict its perceivability.
It GAN DO Better: GAN-based Detection of Objects on Images with Varying Quality
The resulting deep neural network maintains the exact architecture as the selected baseline model without adding to the model parameter complexity or inference speed.
A Locally Weighted Fixation Density-Based Metric for Assessing the Quality of Visual Saliency Predictions
To compare the performance of our proposed metric at assessing the quality of saliency prediction with other existing metrics, we construct a ground-truth subjective database in which saliency maps obtained from 17 different VA models are evaluated by 16 human observers on a 5-point categorical scale in terms of their visual resemblance with corresponding ground-truth fixation density maps obtained from eye-tracking data.
Spatially-Varying Blur Detection Based on Multiscale Fused and Sorted Transform Coefficients of Gradient Magnitudes
The detection of spatially-varying blur without having any information about the blur type is a challenging task.
The Effect of Distortions on the Prediction of Visual Attention
Furthermore, given that one potential application of visual saliency prediction is to aid pooling of objective visual quality metrics, it is important to compare the performance of existing saliency models on distorted images.
Is Bottom-Up Attention Useful for Scene Recognition?
The human visual system employs a selective attention mechanism to understand the visual world in an eficient manner.
