In this story, Physical Integrity Attack Detection of Surveillance Camera with Deep Learning based Video Frame Interpolation, by Nanyang Technological University, is reviewed. In this paper:

• Video Frame Interpolation Using ConvLSTM is used to predict a video frame.
• The predicted frame is compared with the current frame to decide if there is a (cyber or physical) tamper occurred.

This is a paper in 2019 IoTaIS. (Sik-Ho Tsang @ Medium)

Outline

1. Cyber Attacks and Physical Tampering Against Surveillance Cameras
2. Proposed Video Frame Interpolation Using ConvLSTM
3. Experimental Results

1. Cyber Attacks and Physical Tampering Against Surveillance Cameras

• Surveillance cameras, like all Internet of Things (IoT) devices, are also at risk to a…

In this story, Selective Kernel Networks, SKNet, by Nanjing University of Science and Technology, Momenta, Nanjing University, and Tsinghua University, is reviewed.

In the visual cortex, the receptive field (RF) sizes of neurons in the same area (e.g., V1 region) are different, which enables the neurons to collect multi-scale spatial information in the same processing stage.

RF sizes of neurons are not fixed but modulated by stimulus.

In this paper:

• A building block called Selective Kernel (SK) unit is designed, in which multiple branches with different kernel sizes are fused using softmax attention that is guided by the information in…

Happy Chinese New Year of the Ox 2021 !!

In this story, Reducing the Dimensionality of Data with Neural Networks, autoencoder, by University of Toronto, is briefly reviewed. This is a paper by Prof. Hinton. In this paper:

• An autoencoder is trained to reduce the data dimensions for data visualization.

This is a paper in 2006 JSCIENCE with over 14000 citations. (Sik-Ho Tsang @ Medium)

Outline

1. Pretraining by Training Restricted Boltzmann Machine (RBM)
2. Unrolling to Form an Autoencoder
3. Fine-tuning the Autoencoder
4. Experimental Results

1. Pretraining by Training Restricted Boltzmann Machine (RBM)

In this story, UHCTD: A Comprehensive Dataset for Camera Tampering Detection, UHCTD, by University of Houston, is reviewed. In this paper:

• A synthetic dataset with over 6.8 million annotated images is proposed.
• The problem of tampering detection is formulated as a classification problem. Deep learning architectures, such as AlexNet, ResNet, and DenseNet, are used for evaluation.

This is a paper in 2019 AVSS. (Sik-Ho Tsang @ Medium)

Outline

1. Camera Tampering
2. UHCTD: University of Houston Camera Tampering Detection Dataset
3. Tampering Synthesis
4. Tampering Detection as a Classification Problem

1. Camera Tampering

• There are many types of camera tampering. For example:
• Covered tampering occurs when the view…

In this paper, Visualizing Similarity Data with a Mixture of Maps, UNI-SNE, by University of Toronto, is briefly reviewed since UNI-SNE is mentioned in t-SNE. This is a paper by Prof. Hinton. In this paper:

• Aspect Maps are introduced for Data with a Mixture of Maps.
• A Background Map is used to solve the crowding problem.

This is a paper in 2007 ICAIS with over 100 citations. (Sik-Ho Tsang @ Medium)

Outline

1. Brief Review of SNE & Symmetric SNE
2. Aspect Maps
3. UNI-SNE: A Background Map

1. Brief Review of SNE & Symmetric SNE

1.1. SNE

• To visualize the high dimensional data, we need to map those data to a low dimensional…

In this story, Visualizing Data using t-SNE, t-SNE, by Tilburg University, and University of Toronto, is briefly reviewed. It is a very famous paper by Prof. Hinton. In this paper:

• t-SNE is proposed, compared to SNE, it is much easier to optimize.
• t-SNE reduces the crowding problem, compared to SNE.
• t-SNE has been used in various fields for data visualization.

This is a paper in 2008 JMLR with over 17000 citations. (Sik-Ho Tsang @ Medium) It was also presented in 2013 Google TechTalk by author.

Outline

1. Dimensionality Reduction for Data Visualization
2. Brief Review of SNE
3. t-Distributed Stochastic Neighbor Embedding (t-SNE)
4. Experimental…

In this story, Stochastic Neighbor Embedding, SNE, by University of Toronto, is briefly reviewed. Obviously, this paper is by Prof. Hinton. In this paper:

• A probabilistic approach, by pairwise dissimilarity, using Gaussian, is used to map the high-dimensional data distribution on the low-dimensional space, e.g. 2D space, for data visualization.
• Cost function, a sum of Kullback-Leibler divergences, is minimized using gradient descent.

This is a paper in 2002 NIPS with over 1400 citations. (Sik-Ho Tsang @ Medium)

Outline

1. Basic Stochastic Neighbor Embedding (SNE)
2. Experimental Results

1. Basic Stochastic Neighbor Embedding (SNE)

The aim of SNE is to map the high-dimensional data to low-dimensional space for data visualization.

…

In this story, R²MRF: Defocus Blur Detection via Recurrently Refining Multi-Scale Residual Features, R²MRF, by China University of Geosciences, National University of Defense Technology, Zhejiang Normal University, Alibaba Group (U.S.) Inc, and University of Sydney, is reviewed. In this paper:

• A novel recurrent residual refinement branch embedded with multiple residual refinement modules (RRMs) is designed.
• The deep features from different layers are aggregated to learn the residual between the intermediate prediction and the ground truth for each recurrent step in each residual refinement branch.
• The side output of each branch is fused to obtain the final blur detection map.

This…