In this story, Energy-based Generative Adversarial Network, (EBGAN), by New York University, and Facebook Artificial Intelligence Research, is briefly reviewed. In this paper:

• EBGAN views the discriminator as an energy function that attributes low energies to the regions near the data manifold and higher energies to other regions.
• Similar to the probabilistic GANs, the generator is seen as being trained to produce contrastive samples with minimal energies, while the discriminator is trained to assign high energies to these generated samples.

This is a paper in 2017 ICLR with over 1000 citations. (Sik-Ho Tsang @ Medium)

Outline

1. Energy-Based Model
2. Loss Functions
3. Autoencoder…

In this story, EfficientDet: Scalable and Efficient Object Detection, (EfficientDet), by Google Research, Brain Team, is reviewed. In this paper:

• First, a weighted bi-directional feature pyramid network (BiFPN) is proposed, which allows easy and fast multi-scale feature fusion.
• Then, a compound scaling method is also proposed which can uniformly scale the resolution, depth, and width for all backbone, feature network, and box/class prediction networks at the same time.
• Finally, with EfficientNet as backbones, a family of object detectors, EfficientDet, is formed, consistently achieve much better efficiency than prior art, as shown above.

This is a paper in 2020 CVPR with…

In this story, NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection, (NAS-FPN), by Google Brain, is reviewed.

• FPN is widely used in object detection task as it efficiently searched for small and large objects via multi-scale feature maps in pyramid-style.

In this paper:

• While there are prior arts searching the feature extraction parts or the backbone, Neural architecture search (NAS), this time, is used to search the FPN architecture.
• Finally, the discovered architecture, named NAS-FPN, consists of a combination of top-down and bottom-up connections to fuse features across scales.
• This NAS-FPN can be stacked N times for better accuracy…

In this story, Desoiling Dataset: Restoring Soiled Areas on Automotive Fisheye Cameras, (Desoiling Dataset), by Valeo, is reviewed.

• Surround-view cameras can get soiled easily. When cameras get soiled, the degradation of performance is usually more dramatic compared to other sensors.

In this paper:

• First, a Desoiling Dataset is constructed, which contains 40+ approximately 1 minute long video sequences with paired image information of both clean and soiled nature.
• Then, CycleGAN is used for desoiling.

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

Outline

1. Motivation
2. Desoiling Dataset
3. CycleGAN for Desoiling
4. Experimental Results

1. Motivation

• Surround view cameras are becoming de facto…

In this story, Least Squares Generative Adversarial Networks, (LSGAN), by City University of Hong Kong, The Education University of Hong Kong, Northwestern Polytechnical University, and CodeHatch Corp., is briefly reviewed.

• Regular GANs hypothesize the discriminator as a classifier with the sigmoid cross entropy loss function may lead to the vanishing gradients problem during the learning process.

In this paper:

• LSGAN is proposed, in which the least squares loss function is adopted for the discriminator.
• First, LSGANs are able to generate higher quality images than regular GANs. Second, LSGANs perform more stable during the learning process.

This is a paper in…

In this story, Multimodal Unsupervised Image-to-Image Translation, (MUNIT), by Cornell University, and NVIDIA, is reviewed. In this paper:

• In MUNIT, it is assumed that the image representation can be decomposed into a content code that is domain-invariant, and a style code that captures domain-specific properties.
• To translate an image to another domain, its content code is recombined with a random style code sampled from the style space of the target domain.
• Finally, MUNIT allows users to control the style of translation outputs by providing an example style image.

This is a paper in 2018 ECCV with over 1100 citations. …

In this story, Semantic Understanding of Scenes Through the ADE20K Dataset, (ADE20K), by The Chinese University of Hong Kong, Massachusetts Institute of Technology, Peking University, and University of Toronto, is briefly reviewed. In this paper:

• ADE20K dataset is constructed as the benchmarks for scene parsing and instance segmentation.
• The effect of synchronized batch normalization is evaluated and it is found that a reasonably large batch size is crucial for the semantic segmentation performance.

This is a paper in 2019 IJCV over 500 citations. (Sik-Ho Tsang @ Medium)

Outline

1. ADE20K Dataset
2. Scene Parsing Benchmark

1. ADE20K Dataset

In this story, Scene Parsing through ADE20K Dataset, (Cascade-SegNet & Cascade-DilatedNet), by Massachusetts Institute of Technology, and University of Toronto, is briefly reviewed. In this paper:

• Cascade Segmentation Module is proposed to parse a scene into stuff, objects, and object parts in a cascade and improve over the baselines.
• This module is integrated with SegNet and DilatedNet to form the Cascade-SegNet and Cascade-DilatedNet respectively.

This is a paper in 2017 CVPR with over 1000 citations. (Sik-Ho Tsang @ Medium)

Outline

1. Cascade Segmentation Module
2. Experimental Results

1. Cascade Segmentation Module