Search Results for author:
Found 9 papers, 1 papers with code
Point Contrastive Prediction with Semantic Clustering for Self-Supervised Learning on Point Cloud Videos
Instead of contrasting the representations of clips or frames, in this paper, we propose a unified self-supervised framework by conducting contrastive learning at the point level.
Exploring the Physical World Adversarial Robustness of Vehicle Detection
Through this pipeline, we establish the Discrete and Continuous Instant-level (DCI) dataset, enabling comprehensive experiments involving three detection models and three physical adversarial attacks.
Contrastive Predictive Autoencoders for Dynamic Point Cloud Self-Supervised Learning
We present a new self-supervised paradigm on point cloud sequence understanding.
A Survey of Knowledge Enhanced Pre-trained Models
Pre-trained language models learn informative word representations on a large-scale text corpus through self-supervised learning, which has achieved promising performance in fields of natural language processing (NLP) after fine-tuning.
NAS-TC: Neural Architecture Search on Temporal Convolutions for Complex Action Recognition
Accordingly, because of the automated design of its network structure, Neural architecture search (NAS) has achieved great success in the image processing field and attracted substantial research attention in recent years.
A Comprehensive Evaluation Framework for Deep Model Robustness
To mitigate this problem, we establish a model robustness evaluation framework containing 23 comprehensive and rigorous metrics, which consider two key perspectives of adversarial learning (i. e., data and model).
Robust Facial Landmark Detection by Cross-order Cross-semantic Deep Network
Moreover, a novel cross-order cross-semantic (COCS) regularizer is designed to drive the network to learn cross-order cross-semantic features from different activation for facial landmark detection.
VIFB: A Visible and Infrared Image Fusion Benchmark
In this paper, after briefly reviewing recent advances of visible and infrared image fusion, we present a visible and infrared image fusion benchmark (VIFB) which consists of 21 image pairs, a code library of 20 fusion algorithms and 13 evaluation metrics.
SeriesNet:A Generative Time Series Forecasting Model
This model can learn multi-range and multi-level features from time series data, and has higher predictive accuracy compared those models using fixed time intervals.
