Compositional visual question answering requires reasoning over both semantic and geometry object relations.
Past work on distillation for GNNs proposed the Local Structure Preserving loss (LSP), which matches local structural relationships across the student and teacher's node embedding spaces.
It aims to transfer the pose of a source mesh to a target mesh and keep the identity (e. g., body shape) of the target mesh.
In this work, we argue that there are common latent features between the head and tailed classes that can be used to give better feature representation.
To this end, we first propose a prior extractor to learn the query information from the unlabeled images with our proposed global-local contrastive learning.
In this work we propose a point discriminative learning method for unsupervised representation learning on 3D point clouds, which is specially designed for point cloud data and can learn local and global shape features.
While dense labeling on 3D data is expensive and time-consuming, only a few works address weakly supervised semantic point cloud segmentation methods to relieve the labeling cost by learning from simpler and cheaper labels.
Optical flow, which expresses pixel displacement, is widely used in many computer vision tasks to provide pixel-level motion information.
Recent progress in semantic segmentation is driven by deep Convolutional Neural Networks and large-scale labeled image datasets.
Ranked #9 on Few-Shot Semantic Segmentation on PASCAL-5i (1-Shot)
In this work, we propose a novel hybrid method for scene text detection namely Correlation Propagation Network (CPN).
In this fashion, we easily achieve nonlinear learning of potential functions on both unary and pairwise terms in CRFs.
Our column generation based method can be further generalized from the triplet loss to a general structured learning based framework that allows one to directly optimize multivariate performance measures.
We exemplify the usefulness of the proposed model on multi-class semantic labelling (discrete) and the robust depth estimation (continuous) problems.
The deep CNN is trained on the ImageNet dataset and transferred to image segmentations here for constructing potentials of superpixels.
Therefore, here we present a deep convolutional neural field model for estimating depths from single monocular images, aiming to jointly explore the capacity of deep CNN and continuous CRF.
Therefore, we in this paper present a deep convolutional neural field model for estimating depths from a single image, aiming to jointly explore the capacity of deep CNN and continuous CRF.
In this work, we propose to learn deep convolutional image features using unsupervised and supervised learning.
This finding not only enables us to design new ensemble learning methods directly from kernel methods, but also makes it possible to take advantage of those highly-optimized fast linear SVM solvers for ensemble learning.
Feature encoding with respect to an over-complete dictionary learned by unsupervised methods, followed by spatial pyramid pooling, and linear classification, has exhibited powerful strength in various vision applications.
In this work, we propose a novel multiple kernel learning framework to combine multi-modal features for AD classification, which is scalable and easy to implement.