With inference time being a crucial factor, particularly in dense prediction tasks such as semantic segmentation, knowledge distillation has emerged as a successful technique for improving the accuracy of lightweight student networks.
To this end, we define 14 point cloud features and use multiple linear regression to examine whether these features can be used for model-free adversarial point prediction, and which combination of features is best suited for this purpose.
Although 3D point cloud classification has recently been widely deployed in different application scenarios, it is still very vulnerable to adversarial attacks.
The obtained results show that it can perform successful attacks and achieve state-of-the-art results by only a limited number of point modifications while preserving the appearance of the point cloud.
A strong visual object tracker nowadays relies on its well-crafted modules, which typically consist of manually-designed network architectures to deliver high-quality tracking results.
For the upper bound, the optimization is further constrained to use $R$ bits from the training set, a setting which relates MER to information-theoretic bounds on the generalization gap in frequentist learning.
It also reduces the model accuracy by an average of 73% on six datasets MNIST, FMNIST, SVHN, CIFAR10, CIFAR100, and ImageNet.
This approach provides an insight into learning algorithms by considering the mutual information between the model and the training set.
The proposed method has been devoted to both lightweight image classification and encoder-decoder architectures to boost the performance of small and compact models without incurring extra computational overhead at the inference process.
Third, the generalization of the proposed method is validated on various tracking datasets as well as CNN models with similar architectures.
To address this problem, we introduce a context-aware IoU-guided tracker (COMET) that exploits a multitask two-stream network and an offline reference proposal generation strategy.
In recent years, the background-aware correlation filters have achie-ved a lot of research interest in the visual target tracking.
In recent years, visual tracking methods that are based on discriminative correlation filters (DCF) have been very promising.
Then, the proposed method extracts deep semantic information from a fully convolutional FEN and fuses it with the best ResNet-based feature maps to strengthen the target representation in the learning process of continuous convolution filters.
Extremely efficient convolutional neural network architectures are one of the most important requirements for limited-resource devices (such as embedded and mobile devices).
With the rapid progress of deep convolutional neural networks, in almost all robotic applications, the availability of 3D point clouds improves the accuracy of 3D semantic segmentation methods.
One of the high-level tasks in 3D scene understanding is semantic segmentation of RGB-Depth images.
Ranked #5 on Semantic Segmentation on Stanford2D3D - RGBD
Second, popular visual tracking benchmarks and their respective properties are compared, and their evaluation metrics are summarized.
In this work, a novel method for multiple human 3D pose estimation using evidences in multiview images is proposed.
Ranked #14 on 3D Multi-Person Pose Estimation on Campus
It then uses a classic boundary matching criterion or the proposed boundary matching criterion adaptively to identify matching distortion in each boundary of candidate MB.
Our Bayesian framework estimates a posterior distribution for the sparse codes and the dictionaries from labeled training data.