Towards a unified paradigm of visual questions and information domain modeling – The use of object-space models to deal with uncertainty and ambiguity arises in many applications, such as the task of human navigation. A new approach based on the notion of object-space model is proposed, which can be used to model uncertainty for any uncertainty associated with the object. The paradigm of object-space modeling is based on a two-stage approach, where the model is used to model uncertainty and a set of models are extracted from the two steps. The models are then combined in order to achieve a two-stage representation based on a joint posterior distribution, whereby the model is used to model uncertainty. The representation of uncertainty is derived from the number of examples represented in the training data. The proposed approach is tested for two-way interaction between two-dimensional (1,2) and three-dimensional (3,4,5) sets of objects. The experimental results show that our method consistently achieves superior performance compared to state-of-the-art approaches.
In this work, we propose a semi-supervised learning algorithm to learn image features based on a knowledge set from video representations. This knowledge is learned automatically from the learned feature vectors by using a combination of discriminative and discriminative filtering, and the algorithm is trained using a supervised model trained jointly with the video model and deep neural network. Our proposed semi-supervised classification algorithm combines two different aspects: (i) model-free features learning and video feature learning; (ii) feature learning and deep neural network learning (DNN) based learning. The proposed model-free classifier learns the semantic object from the visual scene, and the video representation from this semantic object. The algorithm is trained using a supervised model trained jointly with the video model and DNN. Experiments on various datasets demonstrate that our algorithm outperforms the state-of-the-art semi-supervised classifiers both in the learning task and the embedding task.
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Towards a unified paradigm of visual questions and information domain modeling
On the Emergence of Context-Aware Contextive Reinforcement Learning for Action Recognition
Unsupervised Domain Adaptation for Object DetectionIn this work, we propose a semi-supervised learning algorithm to learn image features based on a knowledge set from video representations. This knowledge is learned automatically from the learned feature vectors by using a combination of discriminative and discriminative filtering, and the algorithm is trained using a supervised model trained jointly with the video model and deep neural network. Our proposed semi-supervised classification algorithm combines two different aspects: (i) model-free features learning and video feature learning; (ii) feature learning and deep neural network learning (DNN) based learning. The proposed model-free classifier learns the semantic object from the visual scene, and the video representation from this semantic object. The algorithm is trained using a supervised model trained jointly with the video model and DNN. Experiments on various datasets demonstrate that our algorithm outperforms the state-of-the-art semi-supervised classifiers both in the learning task and the embedding task.
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