Hand-held objects play an important role in human-human and human-machine interaction. It can be used as a reference for understanding user intentions or user requirements. In this technical demonstration, we introduce an object recognition system called Hand-Object Sense that can automatically recognize the object held by user. This system first detects and segments the hand-held object by exploiting skeleton information combined with depth information. Second, in the object recognition stage, this system exploits features computed in different ways and fuses them to improve the recognition accuracy. Our system can recognize objects in real-time and have a good tolerance to angle and scale transformation. Furthermore, it has a good generalization capability for unknown objects.

Abstract Automatically describing the content of an image is a fundamental problem in artificial intelligence that connects computer vision and natural language processing. In contrast to the previous image description methods that focus on describing the whole image, this paper presents a method of generating rich image descriptions from image regions. First, we detect regions with R-CNN (regions with convolutional neural network features) framework. We then utilize the RNN (recurrent neural networks) to generate sentences for image regions. Finally, we propose an optimization method to select one suitable region. The proposed model generates several sentence description of regions in an image, which has sufficient representative power of the whole image and contains more detailed information. Comparing to general image level description, generating more specific and accurate sentences on the different regions can satisfy more personal requirements for different people. Experimental evaluations validate the effectiveness of the proposed method.