This framework, combined with the prediction bands for each policy, provides a real-time decision support tool for policymakers.
Cost volume is an essential component of recent deep models for optical flow estimation and is usually constructed by calculating the inner product between two feature vectors.
To the best of our knowledge, PSNet is the first work to explicitly address scale limitation and feature similarity in multi-column design.
In a high-dimensional setting, sparse model has shown its power in computational and statistical efficiency.
Optimization and Control Computation
Embodiment is an important characteristic for all intelligent agents (creatures and robots), while existing scene description tasks mainly focus on analyzing images passively and the semantic understanding of the scenario is separated from the interaction between the agent and the environment.
A wealth of angle problems occur when facial recognition is performed: At present, the feature extraction network presents eigenvectors with large differences between the frontal face and profile face recognition of the same person in many cases.
Recent advances in convolutional neural networks(CNNs) usually come with the expense of excessive computational overhead and memory footprint.
We propose a deep convolutional neural network called DeepDualMapper which fuses the aerial image and trajectory data in a more seamless manner to extract the digital map.
Due to the lack of supervision in the target domain, it is crucial to identify the underlying similarity-and-dissimilarity relationships among the unlabelled samples in the target domain.
The learning problem of the sample generation (i. e., diversity transfer) is solved via minimizing an effective meta-classification loss in a single-stage network, instead of the generative loss in previous works.
The augmentation policy network attempts to increase the training loss of a target network through generating adversarial augmentation policies, while the target network can learn more robust features from harder examples to improve the generalization.
Ranked #228 on Image Classification on ImageNet
The effectiveness of the triplet loss heavily relies on the triplet selection, in which a common practice is to first sample intra-class patches (positives) from the dataset for batch construction and then mine in-batch negatives to form triplets.
Finally, we aggregate the global appearance and part features to improve the feature performance further.
In this work, we develop a self-training method with progressive augmentation framework (PAST) to promote the model performance progressively on the target dataset.
Ranked #8 on Unsupervised Domain Adaptation on Market to Duke
We evaluate the Res2Net block on all these models and demonstrate consistent performance gains over baseline models on widely-used datasets, e. g., CIFAR-100 and ImageNet.
Ranked #5 on RGB Salient Object Detection on PASCAL-S
Recent progress on salient object detection mainly aims at exploiting how to effectively integrate multi-scale convolutional features in convolutional neural networks (CNNs).
In support of such applications, various FPGA accelerator architectures have been proposed for convolutional neural networks (CNNs) that enable high performance for classification tasks at lower power than CPU and GPU processors.