Geodesy

Quantum mechanics

Benchmark (surveying)

Scale (ratio)

Computer graphics (images)

Economics

Computer science

Physics

Task (project management)

Pattern recognition (psychology)

Management

Geography

Object (grammar)

RGB color model

Artificial intelligence

Deep learning

Computer graphics

Cognitive neuroscience of visual object recognition

Class (philosophy)

Convolutional neural network

Human–computer interaction

Machine learning

Computer vision

Adversarial system

The Visual Computer

This paper researches on the problem of object recognition using RGB-D data. Although deep convolutional neural networks have so far made progress in this area, they are still suffering a lot from lack of large-scale manually labeled RGB-D data. Labeling large-scale RGB-D dataset is a time-consuming and boring task. More importantly, such large-scale datasets often exist a long tail, and those hard positive examples of the tail can hardly be recognized. To solve these problems, we propose a multimodal self-augmentation and adversarial network (MSANet) for RGB-D object recognition, which can augment the data effectively at two levels while keeping the annotations. Toward the first level, series of transformations are leveraged to generate class-agnostic examples for each instance, which supports the training of our MSANet. Toward the second level, an adversarial network is proposed to generate class-specific hard positive examples while learning to classify them correctly to further improve the performance of our MSANet. Via the above schemes, the proposed approach wins the best results on several available RGB-D object recognition datasets, e.g., our experimental results indicate a 1.5% accuracy boost on benchmark Washington RGB-D object dataset compared with the current state of the art.

MSANet: multimodal self-augmentation and adversarial network for RGB-D object recognition