The images captured by under-display cameras (UDCs) are degraded by the screen in front of them. We model this degradation in terms of a) diffraction by the pixel grid, which attenuates high-spatial-frequency components of the image; and b) diffuse intensity and color changes caused by the multiple thin-film layers in an OLED, which modulate the low-spatial-frequency components of the image. We introduce a deep neural network with two branches to reverse each type of degradation, which is more effective than performing both restorations in a single forward network. We also propose an affine transform connection to replace the skip connection used in most existing DNNs for restoring UDC images. Confining the solution space to the linear transform domain reduces the blurring caused by convolution; and any gross color shift in the training images is eliminated by inverse color filtering. Trained on three datasets of UDC images, our network outperformed existing methods in terms of measures of distortion and of perceived image quality.

Existing face authentication methods rely heavily on the general feature extractor trained with face verification algorithms without considering the target identity. No consideration of the target identity causes inefficiency in that the face authentication works on a specific target identity. Personalized face authentication systems that integrate target identity information is demanded to yield an efficient and superior performance than current methods. With the help of fast adaptation in few-shot meta-learning, we propose a novel personalized Face Authentication based on few-shot Meta-Learning (FAML). FAML introduces a unique person-specific binary classification task and trains an initialization model that can evolve into the personalized face authentication model for any target identity with few data and a small number of iterations. We evaluate the FAML on two public datasets, and it outperforms the baselines by a large margin and demonstrates the practicability as a security application.

The main obstacle to weakly supervised semantic image segmentation is the difficulty of obtaining pixel-level information from coarse image-level annotations. Most methods based on image-level annotations use localization maps obtained from the classifier, but these only focus on the small discriminative parts of objects and do not capture precise boundaries. FickleNet explores diverse combinations of locations on feature maps created by generic deep neural networks. It selects hidden units randomly and then uses them to obtain activation scores for image classification. FickleNet implicitly learns the coherence of each location in the feature maps, resulting in a localization map which identifies both discriminative and other parts of objects. The ensemble effects are obtained from a single network by selecting random hidden unit pairs, which means that a variety of localization maps are generated from a single image. Our approach does not require any additional training steps and only adds a simple layer to a standard convolutional neural network; nevertheless it outperforms recent comparable techniques on the Pascal VOC 2012 benchmark in both weakly and semi-supervised settings.

When a deep neural network is trained on data with only image-level labeling, the regions activated in each image tend to identify only a small region of the target object. We propose a method of using videos automatically harvested from the web to identify a larger region of the target object by using temporal information, which is not present in the static image. The temporal variations in a video allow different regions of the target object to be activated. We obtain an activated region in each frame of a video, and then aggregate the regions from successive frames into a single image, using a warping technique based on optical flow. The resulting localization maps cover more of the target object, and can then be used as proxy ground-truth to train a segmentation network. This simple approach outperforms existing methods under the same level of supervision, and even approaches relying on extra annotations. Based on VGG-16 and ResNet 101 backbones, our method achieves the mIoU of 65.0 and 67.4, respectively, on PASCAL VOC 2012 test images, which represents a new state-of-the-art.

This paper presents a technique to integrate energy efficient ubiquitous sensors to work in a network for optimum control. This optimization technique can reduce power consumption by using wireless communicating sensors with a supervisor control. This supervisory control of the sensors gives it an advantage of being controllable from any embedded, desktop or Internet platform. Sensors for different purposes can work together to form a network which can be implemented in many environment like homes, city, hospitals and remote locations like mountains, forests etc. We employed an optimized sensing system and algorithm which reduced the number of sensors and power consumption.