Search Results for author:
Found 16 papers, 2 papers with code
Mutual Distillation Learning For Person Re-Identification
With the rapid advancements in deep learning technologies, person re-identification (ReID) has witnessed remarkable performance improvements.
Uncovering the human motion pattern: Pattern Memory-based Diffusion Model for Trajectory Prediction
To uncover latent motion patterns in human behavior, we introduce a novel memory-based method, named Motion Pattern Priors Memory Network.
Multi-Stage Contrastive Regression for Action Quality Assessment
In recent years, there has been growing interest in the video-based action quality assessment (AQA).
Efficient Cloud-edge Collaborative Inference for Object Re-identification
Therefore, we pioneer a cloud-edge collaborative inference framework for ReID systems and particularly propose a distribution-aware correlation modeling network (DaCM) to make the desired image return to the cloud server as soon as possible via learning to model the spatial-temporal correlations among instances.
VIoTGPT: Learning to Schedule Vision Tools towards Intelligent Video Internet of Things
Video Internet of Things (VIoT) has shown full potential in collecting an unprecedented volume of video data.
RDFC-GAN: RGB-Depth Fusion CycleGAN for Indoor Depth Completion
Raw depth images captured in indoor scenarios frequently exhibit extensive missing values due to the inherent limitations of the sensors and environments.
Weakly-Supervised Temporal Action Localization by Inferring Salient Snippet-Feature
Weakly-supervised temporal action localization aims to locate action regions and identify action categories in untrimmed videos simultaneously by taking only video-level labels as the supervision.
Pseudo Label
Weakly-supervised Temporal Action Localization
+1
SGFormer: Semantic Graph Transformer for Point Cloud-based 3D Scene Graph Generation
In this paper, we propose a novel model called SGFormer, Semantic Graph TransFormer for point cloud-based 3D scene graph generation.
Unsupervised Self-Driving Attention Prediction via Uncertainty Mining and Knowledge Embedding
Predicting attention regions of interest is an important yet challenging task for self-driving systems.
Coarse-to-Fine Video Denoising with Dual-Stage Spatial-Channel Transformer
Meanwhile, we design a Multi-Scale Residual Structure to preserve multiple aspects of information at different stages, which contains a Temporal Features Aggregation Module to summarize the dynamic representation.
RGB-Depth Fusion GAN for Indoor Depth Completion
In this paper, we design a novel two-branch end-to-end fusion network, which takes a pair of RGB and incomplete depth images as input to predict a dense and completed depth map.
Unsupervised Domain Adaptation with Temporal-Consistent Self-Training for 3D Hand-Object Joint Reconstruction
Deep learning-solutions for hand-object 3D pose and shape estimation are now very effective when an annotated dataset is available to train them to handle the scenarios and lighting conditions they will encounter at test time.
Generative Adversarial Network
Unsupervised Domain Adaptation
Imitative Non-Autoregressive Modeling for Trajectory Forecasting and Imputation
Trajectory forecasting and imputation are pivotal steps towards understanding the movement of human and objects, which are quite challenging since the future trajectories and missing values in a temporal sequence are full of uncertainties, and the spatial-temporally contextual correlation is hard to model.
KE-GAN: Knowledge Embedded Generative Adversarial Networks for Semi-Supervised Scene Parsing
In recent years, scene parsing has captured increasing attention in computer vision.
Attentive Relational Networks for Mapping Images to Scene Graphs
Scene graph generation refers to the task of automatically mapping an image into a semantic structural graph, which requires correctly labeling each extracted object and their interaction relationships.
stagNet: An Attentive Semantic RNN for Group Activity Recognition
Group activity recognition plays a fundamental role in a variety of applications, e. g. sports video analysis and intelligent surveillance.
