Search Results for author:
Found 33 papers, 15 papers with code
Human Interaction Learning on 3D Skeleton Point Clouds for Video Violence Recognition
This paper introduces a new method for recognizing violent behavior by learning contextual relationships between related people from human skeleton points.
Ranked #5 on
Activity Recognition
on RWF-2000
Spatial-Semantic Collaborative Cropping for User Generated Content
A large amount of User Generated Content (UGC) is uploaded to the Internet daily and displayed to people world-widely through the client side (e. g., mobile and PC).
Variance-insensitive and Target-preserving Mask Refinement for Interactive Image Segmentation
We introduce a novel method, Variance-Insensitive and Target-Preserving Mask Refinement to enhance segmentation quality with fewer user inputs.
SARA: Controllable Makeup Transfer with Spatial Alignment and Region-Adaptive Normalization
To address this problem, we propose a novel Spatial Alignment and Region-Adaptive normalization method (SARA) in this paper.
Typhoon Intensity Prediction with Vision Transformer
Predicting typhoon intensity accurately across space and time is crucial for issuing timely disaster warnings and facilitating emergency response.
DI-Net : Decomposed Implicit Garment Transfer Network for Digital Clothed 3D Human
3D virtual try-on enjoys many potential applications and hence has attracted wide attention.
Semantic-Constraint Matching Transformer for Weakly Supervised Object Localization
Weakly supervised object localization (WSOL) strives to learn to localize objects with only image-level supervision.
Occlusion-Aware Detection and Re-ID Calibrated Network for Multi-Object Tracking
Multi-Object Tracking (MOT) is a crucial computer vision task that aims to predict the bounding boxes and identities of objects simultaneously.
Improving Video Violence Recognition with Human Interaction Learning on 3D Skeleton Point Clouds
(ii) Global-SPIL: to better learn and refine the features of the unordered and unstructured skeleton points, Global-SPIL employs the self-attention layer that operates directly on the sampled points, which can help to make the output more permutation-invariant and well-suited for our task.
WMFormer++: Nested Transformer for Visible Watermark Removal via Implict Joint Learning
Existing watermark removal methods mainly rely on UNet with task-specific decoder branches--one for watermark localization and the other for background image restoration.
Dual Progressive Transformations for Weakly Supervised Semantic Segmentation
Weakly supervised semantic segmentation (WSSS), which aims to mine the object regions by merely using class-level labels, is a challenging task in computer vision.
A Unified Transformer Framework for Group-based Segmentation: Co-Segmentation, Co-Saliency Detection and Video Salient Object Detection
Besides, they fail to take full advantage of the cues among inter- and intra-feature within a group of images.
Ranked #1 on
Video Salient Object Detection
on FBMS-59
Debiased Visual Question Answering from Feature and Sample Perspectives
From the sample perspective, we construct two types of negative samples to assist the training of the models, without introducing additional annotations.
Calibrating Class Activation Maps for Long-Tailed Visual Recognition
First, we present a Class Activation Map Calibration (CAMC) module to improve the learning and prediction of network classifiers, by enforcing network prediction based on important image regions.
MV-TON: Memory-based Video Virtual Try-on network
With the development of Generative Adversarial Network, image-based virtual try-on methods have made great progress.
Context Decoupling Augmentation for Weakly Supervised Semantic Segmentation
Data augmentation is vital for deep learning neural networks.
CycleSegNet: Object Co-segmentation with Cycle Refinement and Region Correspondence
Image co-segmentation is an active computer vision task that aims to segment the common objects from a set of images.
Self-Supervised 3D Skeleton Action Representation Learning With Motion Consistency and Continuity
Recently, self-supervised learning (SSL) has been proved very effective and it can help boost the performance in learning representations from unlabeled data in the image domain.
StackRec: Efficient Training of Very Deep Sequential Recommender Models by Iterative Stacking
We validate the performance of StackRec by instantiating it with four state-of-the-art SR models in three practical scenarios with real-world datasets.
Double Forward Propagation for Memorized Batch Normalization
Although the standard BN can significantly accelerate the training of DNNs and improve the generalization performance, it has several underlying limitations which may hamper the performance in both training and inference.
Graph Edit Distance Reward: Learning to Edit Scene Graph
Scene Graph, as a vital tool to bridge the gap between language domain and image domain, has been widely adopted in the cross-modality task like VQA.
Improving Generative Adversarial Networks with Local Coordinate Coding
In this paper, rather than sampling from the predefined prior distribution, we propose an LCCGAN model with local coordinate coding (LCC) to improve the performance of generating data.
Collaborative Unsupervised Domain Adaptation for Medical Image Diagnosis
Deep learning based medical image diagnosis has shown great potential in clinical medicine.
COVID-DA: Deep Domain Adaptation from Typical Pneumonia to COVID-19
There are two main challenges: 1) the discrepancy of data distributions between domains; 2) the task difference between the diagnosis of typical pneumonia and COVID-19.
Cost-Sensitive Portfolio Selection via Deep Reinforcement Learning
This task, however, has two main difficulties: (i) the non-stationary price series and complex asset correlations make the learning of feature representation very hard; (ii) the practicality principle in financial markets requires controlling both transaction and risk costs.
Online Adaptive Asymmetric Active Learning with Limited Budgets
In these problems, there are two key challenges: the query budget is often limited; the ratio between classes is highly imbalanced.
Collaborative Unsupervised Domain Adaptation for Medical Image Diagnosis
In this paper, we seek to exploit rich labeled data from relevant domains to help the learning in the target task with unsupervised domain adaptation (UDA).
Attention Guided Network for Retinal Image Segmentation
Learning structural information is critical for producing an ideal result in retinal image segmentation.
Auto-Embedding Generative Adversarial Networks for High Resolution Image Synthesis
To address this issue, we develop a novel GAN called Auto-Embedding Generative Adversarial Network (AEGAN), which simultaneously encodes the global structure features and captures the fine-grained details.
Discrimination-aware Channel Pruning for Deep Neural Networks
Channel pruning is one of the predominant approaches for deep model compression.
Adversarial Learning with Local Coordinate Coding
Generative adversarial networks (GANs) aim to generate realistic data from some prior distribution (e. g., Gaussian noises).
Visual Grounding via Accumulated Attention
There are three main challenges in VG: 1) what is the main focus in a query; 2) how to understand an image; 3) how to locate an object.
A Self-Balanced Min-Cut Algorithm for Image Clustering
In the new method, a self-balanced min-cut model is proposed in which the Exclusive Lasso is implicitly introduced as a balance regularizer in order to produce balanced partition.
