Deep active object recognition by joint label and action prediction
نویسندگان
چکیده
منابع مشابه
Deep active object recognition by joint label and action prediction
An active object recognition system has the advantage of acting in the environment to capture images that are more suited for training and lead to better performance at test time. In this paper, we utilize deep convolutional neural networks for active object recognition by simultaneously predicting the object label and the next action to be performed on the object with the aim of improving reco...
متن کاملObject Recognition by Active
Today's computer vision applications often have to deal with multiple, uncertain, and incomplete visual information. In this paper, we apply a new method, termedàctive fusion', to the problem of generic object recognition. Active fusion provides a common framework for active selection and combination of information from multiple sources in order to arrive at a reliable result at reasonable cost...
متن کاملAction Recognition with Joint Attention on Multi-Level Deep Features
We propose a novel deep supervised neural network for the task of action recognition in videos, which implicitly takes advantage of visual tracking and shares the robustness of both deep Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN). In our method, a multi-branch model is proposed to suppress noise from background jitters. Specifically, we firstly extract multi-level dee...
متن کاملActive Object Recognition Integrating
We present an active object recognition strategy which combines the use of an attention mechanism for focusing the search for a 3-D object in a 2-D image, with a viewpoint control strategy for disambiguating recovered object features. The attention mechanism consists of a probabilistic search through a hierarchy of predicted feature observations, taking objects into a set of regions classiied a...
متن کاملActive learning by label uncertainty for acoustic emotion recognition
Speech data is in principle available in large amounts for the training of acoustic emotion recognisers. However, emotional labelling is usually not given and the distribution is heavily unbalanced, as most data is ‘rather neutral’ than truly ‘emotional’. In the ‘hay stack’ of speech data, Active Learning automatically identifies the ‘needles’, i.e., the more informative instances to reduce hum...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Computer Vision and Image Understanding
سال: 2017
ISSN: 1077-3142
DOI: 10.1016/j.cviu.2016.10.011