7 accepted papers
Passive object detectors, trained on large-scale static datasets, often overlook the feedback from object detection to image acquisition. Embodied vision and active detection mitigate this issue by interacting with the environment. Nevertheless, the materialization of activeness hinges on resource-i…
Humans are proficient at continuously acquiring and integrating new knowledge. By contrast, deep models forget catastrophically, especially when tackling highly long task sequences. Inspired by the way our brains constantly rewrite and consolidate past recollections, we propose a novel Bilateral Mem…
Training Deep Neural Networks (DNNs) is inherently subject to sensitive hyper-parameters and untimely feedbacks of performance evaluation. To solve these two difficulties, an efficient parallel hyper-parameter optimization model is proposed under the framework of Deep Reinforcement Learning (DRL). T…
Exploiting convolutional neural networks for point cloud processing is quite challenging, due to the inherent irregular distribution and discrete shape representation of point clouds. To address these problems, many handcrafted convolution variants have sprung up in recent years. Though with elabora…
Object monitoring can be performed by change detection algorithms. However, for the image pair with a large perspective difference, the change detection performance is usually impacted by inaccurate image registration. To address the above difficulties, a novel object-specific change detection appro…
State-of-the-art object detection methods based on deep learning achieved promising performances in recent years. However, the performances are limited by the passive nature of the traditional object recognition framework in ignoring the relationship between imaging configuration and recognition per…
Hashing is very effective for many tasks in reducing the processing time and in compressing massive databases. Although lots of approaches have been developed to learn data-dependent hash functions in recent years, how to learn hash functions to yield good performance with acceptable computational a…