Recently, several direct processing point cloud models have achieved state-of-the-art performances for classification and segmentation tasks. However, these methods lack rotation robustness, and their performances degrade severely under random rotations, failing to extend to real-world applications with varying orientations… To address this problem, we propose a method named Self Contour-based Transformation (SCT), which can be flexibly integrated into a variety of existing point cloud recognition models against arbitrary rotations without any extra modifications. The SCT provides efficient and mathematically proved […]

Radar sensor provides lighting and weather invariant sensing, which is naturally suitable for long-term localization in outdoor scenes. On the other hand, the most popular available map currently is built by lidar… In this paper, we propose a deep neural network for end-to-end learning of radar localization on lidar map to bridge the gap. We first embed both sensor modals into a common feature space by a neural network. Then multiple offsets are added to the map modal for similarity […]

We propose Attention Grounder (AttnGrounder), a single-stage end-to-end trainable model for the task of visual grounding. Visual grounding aims to localize a specific object in an image based on a given natural language text query… Unlike previous methods that use the same text representation for every image region, we use a visual-text attention module that relates each word in the given query with every region in the corresponding image for constructing a region dependent text representation. Furthermore, for improving the […]

Machine Learning in general and Deep Learning in particular has gained much interest in the recent decade and has shown significant performance improvements for many Computer Vision or Natural Language Processing tasks. In order to deal with databases which have just a small amount of training samples or to deal with models which have large amount of parameters, the regularization is indispensable… In this paper, we enforce the manifold preservation (manifold learning) from the original data into latent presentation by […]

Learning ensembles by bagging can substantially improve the generalization performance of low-bias high-variance estimators, including those evolved by Genetic Programming (GP). Yet, the best way to learn ensembles in GP remains to be determined… This work attempts to fill the gap between existing GP ensemble learning algorithms, which are often either simple but expensive, or efficient but complex. We propose a new algorithm that is both simple and efficient, named Simple Simultaneous Ensemble Genetic Programming (2SEGP). 2SEGP is obtained by […]

In open-domain dialogue response generation, a dialogue context can be continued with diverse responses, and the dialogue models should capture such one-to-many relations. In this work, we first analyze the training objective of dialogue models from the view of Kullback-Leibler divergence (KLD) and show that the gap between the real world probability distribution and the single-referenced data’s probability distribution prevents the model from learning the one-to-many relations efficiently… Then we explore approaches to multi-referenced training in two aspects. Data-wise, we […]

Annotating seismic data is expensive, laborious and subjective due to the number of years required for seismic interpreters to attain proficiency in interpretation. In this paper, we develop a framework to automate annotating pixels of a seismic image to delineate geological structural elements given image-level labels assigned to each image… Our framework factorizes the latent space of a deep encoder-decoder network by projecting the latent space to learned sub-spaces. Using constraints in the pixel space, the seismic image is further […]