In this story, Learning from Simulated and Unsupervised Images through Adversarial Training, (SimGAN), by Apple Inc., is reviewed. In this paper:

• Simulated+Unsupervised (S+U) learning is proposed, where the task is to learn a model to improve the realism of a simulator’s output using unlabeled real data, while preserving the annotation information from the simulator.
• Synthetic images are used as inputs instead of random vectors.
• A ‘self-regularization’ term, a local adversarial loss, and update of the discriminator using a history of refined images, are also suggested.

This is a paper in 2017 CVPR with over 1300 citations. (Sik-Ho Tsang @ Medium)

Outline

1. …

In this story, CenterNet: Keypoint Triplets for Object Detection, (CenterNet), by University of Chinese Academy of Sciences, Huazhong University of Science and Technology, Huawei Noah’s Ark Lab, CAS, and Peng Cheng Laboratory, is reviewed. In this story:

• CenterNet detects each object as a triplet of keypoints, rather than a pair of keypoints like CornerNet, which improves both precision and recall.
• Two customized modules, cascade corner pooling, and center pooling, that enrich information collected by both the top-left and bottom-right corners and provide more recognizable information from the central regions.

This is a paper in 2019 ICCV over 380 citations. …

In this story, Unsupervised Image-to-Image Translation Networks, (UNIT), by NVIDIA, is reviewed. In this paper:

• A shared-latent space assumption is made, which assumes a pair of corresponding images in different domains can be mapped to a same latent representation in a shared-latent space.
• Image-to-Image Translation is performed through that shared-latent space. (At that moment, there exist no paired examples showing how an image could be translated to a corresponding image in another domain.)

This is a paper in 2017 NIPS with over 1500 citations. (Sik-Ho Tsang @ Medium)

Outline

1. Shared-Latent Space Assumption
2. UNIT: Framework
3. Training Loss
4. Image-to-image Translation Results
5. Domain Adaptation…

In this story, CornerNet: Detecting Objects as Paired Keypoints, (CornerNet), by University of Michigan, is reviewed. In this paper:

• An object bounding box is detected as a pair of keypoints, the top-left corner and the bottom-right corner, eliminating the need for designing a set of anchor boxes commonly used in prior single-stage detectors.

This is a paper in 2018 ECCV with over 900 citations. (Sik-Ho Tsang @ Medium)

Outline

1. CornerNet: Network Architecture
2. Corner Detection (Heatmap & Offsets)
3. Corner Grouping (Embedding)
4. Corner Pooling
5. Comparisons with State-Of-The-Art Detectors

1. CornerNet: Network Architecture

In this story, Single-Shot Refinement Neural Network for Object Detection, (RefineDet), by Chinese Academy of Sciences, University of Chinese Academy of Sciences, and GE Global Research, is reviewed. In this paper:

• RefineDet is proposed, which consists of two inter-connected modules, namely, the anchor refinement module (ARM) and the object detection module (ODM).
• ARM filters out negative anchors to reduce search space for the classifier, and coarsely adjust the locations and sizes of anchors.
• ODM takes the refined anchors as the input from ARM to further improve the regression and predict multi-class label.

This is a paper in 2018 CVPR with…

In this story, CoupleNet: Coupling Global Structure with Local Parts for Object Detection, (CoupleNet), by Chinese Academy of Sciences, University of Chinese Academy of Sciences, Nanjing Audit University, and Indiana University, is reviewed. In this paper:

• The object proposals obtained by the Region Proposal Network (RPN) are fed into the the coupling module which consists of two branches.
• One branch adopts the position-sensitive RoI (PSRoI) pooling to capture the local part information of the object.
• The other employs the RoI pooling to encode the global and context information.

This is a paper in 2017 ICCV with over 130 citations. …

In this story, A Comprehensive Benchmark for Single Image Compression Artifact Reduction, (LIU4K), by Peking University, and University of Science and Technology of China, is reviewed. In this paper:

• LIU4K: Large-Scale Ideal Ultra high-definition 4K, a new dataset, is proposed.
• A summary of state-of-the-art single image filtering approaches are also reviewed and evaluated using both full reference and no reference metric for objective and subjective quality measurement respectively.

This is a paper in 2020 TIP where TIP has a high impact factor of 9.34. (Sik-Ho Tsang @ Medium)

Outline

1. LIU4K: Large-Scale Ideal Ultra high-definition 4K
2. Summary of State-Of-The-Art Single Image Filtering…

In this story, Training Region-based Object Detectors with Online Hard Example Mining, (OHEM), by Carnegie Mellon University, and Facebook AI Research (FAIR), is reviewed.

Detection datasets contain an overwhelming number of easy examples and a small number of hard examples.

In this paper:

• OHEM automatic selects these hard examples can make training more effective and efficient.
• OHEM eliminates several heuristics and hyperparameters in common use.

This is a paper in 2016 CVPR with over 1300 citations. (Sik-Ho Tsang @ Medium)

Outline

1. Brief Review of Fast R-CNN (FRCN)
2. Several Heuristics in FRCN
3. Online Hard Example Mining (OHEM)
4. Experimental Results

1. Brief Review of Fast R-CNN (FRCN)

1. FRCN takes as…