Search Results for author:
Found 19 papers, 2 papers with code
BERT-enhanced Relational Sentence Ordering Network
In this paper, we introduce a novel BERT-enhanced Relational Sentence Ordering Network (referred to as BRSON) by leveraging BERT for capturing better dependency relationship among sentences to enhance the coherence modeling for the entire paragraph.
Enabling Collaborative Clinical Diagnosis of Infectious Keratitis by Integrating Expert Knowledge and Interpretable Data-driven Intelligence
This study investigates the performance, interpretability, and clinical utility of KGDM in the diagnosis of infectious keratitis (IK), which is the leading cause of corneal blindness.
Dense Affinity Matching for Few-Shot Segmentation
Few-Shot Segmentation (FSS) aims to segment the novel class images with a few annotated samples.
Multi-Content Interaction Network for Few-Shot Segmentation
Few-Shot Segmentation (FSS) is challenging for limited support images and large intra-class appearance discrepancies.
Pseudo-Label Generation-Evaluation Framework For Cross Domain Weakly Supervised Object Detection
Cross domain weakly supervised object detection (CDWSOD), where we can get access to instance-level annotations in the source domain while only image-level annotations are available in the target domain, adapts object detectors from label-rich to label-poor domains.
Deep Metric Learning with Spherical Embedding
Deep metric learning has attracted much attention in recent years, due to seamlessly combining the distance metric learning and deep neural network.
Dual Low-Rank Multimodal Fusion
Tensor-based fusion methods have been proven effective in multimodal fusion tasks.
SBAT: Video Captioning with Sparse Boundary-Aware Transformer
However, video captioning is a multimodal learning problem, and the video features have much redundancy between different time steps.
Fine-tune BERT with Sparse Self-Attention Mechanism
In this paper, we develop a novel Sparse Self-Attention Fine-tuning model (referred as SSAF) which integrates sparsity into self-attention mechanism to enhance the fine-tuning performance of BERT.
Low-Rank HOCA: Efficient High-Order Cross-Modal Attention for Video Captioning
This paper addresses the challenging task of video captioning which aims to generate descriptions for video data.
Text Guided Person Image Synthesis
This paper presents a novel method to manipulate the visual appearance (pose and attribute) of a person image according to natural language descriptions.
Deep Attentive Sentence Ordering Network
In this paper, we propose a novel deep attentive sentence ordering network (referred as ATTOrderNet) which integrates self-attention mechanism with LSTMs in the encoding of input sentences.
Partially Shared Multi-Task Convolutional Neural Network With Local Constraint for Face Attribute Learning
Consequently, we present a local constraint regularized multi-task network, called Partially Shared Multi-task Convolutional Neural Network with Local Constraint (PS-MCNN-LC), where PS structure and local constraint are integrated together to help the framework learn better attribute representations.
Pyramid Person Matching Network for Person Re-identification
In this work, we present a deep convolutional pyramid person matching network (PPMN) with specially designed Pyramid Matching Module to address the problem of person re-identification.
Multi-Channel Pyramid Person Matching Network for Person Re-Identification
In particular, we learn separate deep representations for semantic-components and color-texture distributions from two person images and then employ pyramid person matching network (PPMN) to obtain correspondence representations.
Text Coherence Analysis Based on Deep Neural Network
In this paper, we propose a novel deep coherence model (DCM) using a convolutional neural network architecture to capture the text coherence.
Tensor Decomposition via Variational Auto-Encoder
Another assumption of these methods is that a predefined rank should be known.
A Survey of Multi-View Representation Learning
Consequently, we first review the representative methods and theories of multi-view representation learning based on the perspective of alignment, such as correlation-based alignment.
Multimodal Skip-gram Using Convolutional Pseudowords
This work studies the representational mapping across multimodal data such that given a piece of the raw data in one modality the corresponding semantic description in terms of the raw data in another modality is immediately obtained.
