Search Results for author:
Found 22 papers, 2 papers with code
Learning cross space mapping via DNN using large scale click-through logs
The image and query are mapped to a common vector space via these two parts respectively, and image-query similarity is naturally defined as an inner product of their mappings in the space.
Learning From Biased Soft Labels
We present two more comprehensive indicators to measure the effectiveness of such soft labels.
Delving Globally into Texture and Structure for Image Inpainting
Our model is orthogonal to the fashionable arts, such as Convolutional Neural Networks (CNNs), Attention and Transformer model, from the perspective of texture and structure information for image inpainting.
Self-Supervised Graph Neural Network for Multi-Source Domain Adaptation
Domain adaptation (DA) tries to tackle the scenarios when the test data does not fully follow the same distribution of the training data, and multi-source domain adaptation (MSDA) is very attractive for real world applications.
Graph Attention Transformer Network for Multi-Label Image Classification
Subsequently, we design the graph attention transformer layer to transfer this adjacency matrix to adapt to the current domain.
A Distributed Approach towards Discriminative Distance Metric Learning
Distance metric learning is successful in discovering intrinsic relations in data.
A Survey on Food Computing
This is the first comprehensive survey that targets the study of computing technology for the food area and also offers a collection of research studies and technologies to benefit researchers and practitioners working in different food-related fields.
Computers and Society Multimedia
AI Oriented Large-Scale Video Management for Smart City: Technologies, Standards and Beyond
To practically facilitate deep neural network models in the large-scale video analysis, there are still unprecedented challenges for the large-scale video data management.
Multi-Level Attention Networks for Visual Question Answering
To solve the challenges, we propose a multi-level attention network for visual question answering that can simultaneously reduce the semantic gap by semantic attention and benefit fine-grained spatial inference by visual attention.
Enhancing Person Re-identification in a Self-trained Subspace
To address this problem, in this paper, we propose a self-trained subspace learning paradigm for person re-ID which effectively utilizes both labeled and unlabeled data to learn a discriminative subspace where person images across disjoint camera views can be easily matched.
MSR-VTT: A Large Video Description Dataset for Bridging Video and Language
In this paper we present MSR-VTT (standing for "ABC-Video to Text") which is a new large-scale video benchmark for video understanding, especially the emerging task of translating video to text.
Joint Multiview Segmentation and Localization of RGB-D Images Using Depth-Induced Silhouette Consistency
In this paper, we propose an RGB-D camera localization approach which takes an effective geometry constraint, i. e. silhouette consistency, into consideration.
Highlight Detection With Pairwise Deep Ranking for First-Person Video Summarization
The emergence of wearable devices such as portable cameras and smart glasses makes it possible to record life logging first-person videos.
Network Morphism
The second requirement for this network morphism is its ability to deal with non-linearity in a network.
Relaxing From Vocabulary: Robust Weakly-Supervised Deep Learning for Vocabulary-Free Image Tagging
The development of deep learning has empowered machines with comparable capability of recognizing limited image categories to human beings.
Query Adaptive Similarity Measure for RGB-D Object Recognition
The reasons are in two-fold: (1) existing similarity measures are sensitive to object pose and scale changes, as well as intra-class variations; and (2) effectively fusing RGB and depth cues is still an open problem.
MeshStereo: A Global Stereo Model With Mesh Alignment Regularization for View Interpolation
We present a novel global stereo model designed for view interpolation.
Jointly Modeling Embedding and Translation to Bridge Video and Language
Our proposed LSTM-E consists of three components: a 2-D and/or 3-D deep convolutional neural networks for learning powerful video representation, a deep RNN for generating sentences, and a joint embedding model for exploring the relationships between visual content and sentence semantics.
Visualizing and Comparing Convolutional Neural Networks
Convolutional Neural Networks (CNNs) have achieved comparable error rates to well-trained human on ILSVRC2014 image classification task.
Efficient 2D-to-3D Correspondence Filtering for Scalable 3D Object Recognition
3D model-based object recognition has been a noticeable research trend in recent years.
