Search Results for author:
Found 52 papers, 13 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.
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.
Reducing Flipping Errors in Deep Neural Networks
Deep neural networks (DNNs) have been widely applied in various domains in artificial intelligence including computer vision and natural language processing.
Deep Hybrid Models for Out-of-Distribution Detection
We propose a principled and practical method for out-of-distribution (OoD) detection with deep hybrid models (DHMs), which model the joint density p(x, y) of features and labels with a single forward pass.
Ranked #1 on
Out-of-Distribution Detection
on CIFAR-10
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
Comprehensive Knowledge Distillation with Causal Intervention
Knowledge distillation (KD) addresses model compression by distilling knowledge from a large model (teacher) to a smaller one (student).
Stable Prediction on Graphs with Agnostic Distribution Shift
The results demonstrate that our method outperforms various SOTA GNNs for stable prediction on graphs with agnostic distribution shift, including shift caused by node labels and attributes.
Complementary Calibration: Boosting General Continual Learning with Collaborative Distillation and Self-Supervision
Furthermore, we explore a collaborative self-supervision idea to leverage pretext tasks and supervised contrastive learning for addressing the feature deviation problem by learning complete and discriminative features for all classes.
Self-Taught Cross-Domain Few-Shot Learning with Weakly Supervised Object Localization and Task-Decomposition
However, the target domain is absolutely unknown during the training on the source domain, which results in lacking directed guidance for target tasks.
cross-domain few-shot learning
Weakly-Supervised Object Localization
Graph-Free Knowledge Distillation for Graph Neural Networks
In this paper, we propose to our best knowledge the first dedicated approach to distilling knowledge from a GNN without graph data.
Can Students Outperform Teachers in Knowledge Distillation based Model Compression?
By designing exploratory experiments, we find that model capacity differences are not necessarily the root reason, and the distillation data matters when the student capacity is greater than a threshold.
Learning with Retrospection
Deep neural networks have been successfully deployed in various domains of artificial intelligence, including computer vision and natural language processing.
CoCosNet v2: Full-Resolution Correspondence Learning for Image Translation
We present the full-resolution correspondence learning for cross-domain images, which aids image translation.
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.
Sparsity-Control Ternary Weight Networks
However, the existing approaches to training ternary weight networks cannot control the sparsity (i. e., percentage of 0s) of the ternary weights, which undermines the advantage of ternary weights.
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.
Multitask Non-Autoregressive Model for Human Motion Prediction
Human motion prediction, which aims at predicting future human skeletons given the past ones, is a typical sequence-to-sequence problem.
Is the Meta-Learning Idea Able to Improve the Generalization of Deep Neural Networks on the Standard Supervised Learning?
In this paper, we propose a novel meta-learning based training procedure (MLTP) for DNNs and demonstrate that the meta-learning idea can indeed improve the generalization abilities of DNNs.
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.
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.
Episode-based Prototype Generating Network for Zero-Shot Learning
We introduce a simple yet effective episode-based training framework for zero-shot learning (ZSL), where the learning system requires to recognize unseen classes given only the corresponding class semantics.
A Semantics-Guided Class Imbalance Learning Model for Zero-Shot Classification
Towards alleviating the class imbalance issue in ZSC, we propose a sample-balanced training process to encourage all training classes to contribute equally to the learned model.
Spatiotemporal graph routing for skeleton-based action recognition
With the representation effectiveness, skeleton-based human action recognition has received considerable research attention, and has a wide range of real applications.
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.
Cross-relation Cross-bag Attention for Distantly-supervised Relation Extraction
Distant supervision leverages knowledge bases to automatically label instances, thus allowing us to train relation extractor without human annotations.
Perceiving Physical Equation by Observing Visual Scenarios
To tackle this challenge, we present a novel pipeline comprised of an Observer Engine and a Physicist Engine by respectively imitating the actions of an observer and a physicist in the real world.
Bi-Adversarial Auto-Encoder for Zero-Shot Learning
Existing generative Zero-Shot Learning (ZSL) methods only consider the unidirectional alignment from the class semantics to the visual features while ignoring the alignment from the visual features to the class semantics, which fails to construct the visual-semantic interactions well.
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.
Stacked Pooling: Improving Crowd Counting by Boosting Scale Invariance
In this work, we explore the cross-scale similarity in crowd counting scenario, in which the regions of different scales often exhibit high visual similarity.
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.
Stacked Semantic-Guided Attention Model for Fine-Grained Zero-Shot Learning
To this end, we propose a novel stacked semantics-guided attention (S2GA) model to obtain semantic relevant features by using individual class semantic features to progressively guide the visual features to generate an attention map for weighting the importance of different local regions.
GNAS: A Greedy Neural Architecture Search Method for Multi-Attribute Learning
A key problem in deep multi-attribute learning is to effectively discover the inter-attribute correlation structures.
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.
A Deep Learning Approach for Expert Identification in Question Answering Communities
This approach uses the convolutional neural network and combines user feature representations with question feature representations to compute scores that the user who gets the highest score is the expert on this question.
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.
Transductive Zero-Shot Learning with a Self-training dictionary approach
As an important and challenging problem in computer vision, zero-shot learning (ZSL) aims at automatically recognizing the instances from unseen object classes without training data.
Structural Correspondence Learning for Cross-lingual Sentiment Classification with One-to-many Mappings
For simplicity, however, it assumes that the word translation oracle maps each pivot feature in source language to exactly only one word in target language.
Cross-Lingual Sentiment Classification
General Classification
+4
S3Pool: Pooling with Stochastic Spatial Sampling
We view the pooling operation in CNNs as a two-step procedure: first, a pooling window (e. g., $2\times 2$) slides over the feature map with stride one which leaves the spatial resolution intact, and second, downsampling is performed by selecting one pixel from each non-overlapping pooling window in an often uniform and deterministic (e. g., top-left) manner.
Generative Adversarial Networks as Variational Training of Energy Based Models
We propose VGAN, which works by minimizing a variational lower bound of the negative log likelihood (NLL) of an energy based model (EBM), where the model density $p(\mathbf{x})$ is approximated by a variational distribution $q(\mathbf{x})$ that is easy to sample from.
Doubly Convolutional Neural Networks
Building large models with parameter sharing accounts for most of the success of deep convolutional neural networks (CNNs).
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.
Zero-Shot Learning with Multi-Battery Factor Analysis
Zero-shot learning (ZSL) extends the conventional image classification technique to a more challenging situation where the test image categories are not seen in the training samples.
Deep Structured Energy Based Models for Anomaly Detection
In this paper, we attack the anomaly detection problem by directly modeling the data distribution with deep architectures.
Deep Learning Driven Visual Path Prediction from a Single Image
The highly effective visual representation and deep context models ensure that our framework makes a deep semantic understanding of the scene and motion pattern, consequently improving the performance of the visual path prediction task.
Semisupervised Autoencoder for Sentiment Analysis
In this paper, we investigate the usage of autoencoders in modeling textual data.
Dropout Training of Matrix Factorization and Autoencoder for Link Prediction in Sparse Graphs
Matrix factorization (MF) and Autoencoder (AE) are among the most successful approaches of unsupervised learning.
Manifold Regularized Discriminative Neural Networks
The first one, named Label-Aware Manifold Regularization, assumes the availability of labels and penalizes large norms of the loss function w. r. t.
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.
Online Metric-Weighted Linear Representations for Robust Visual Tracking
Object identification results for an entire video sequence are achieved by systematically combining the tracking information and visual recognition at each frame.
Context-Aware Hypergraph Construction for Robust Spectral Clustering
Using both CAHSM and DHPC, a robust spectral clustering algorithm is developed.
