Search Results for author:
Found 14 papers, 3 papers with code
Cascaded Self-supervised Learning for Subject-independent EEG-based Emotion Recognition
Inspired by recent efforts in combining low-level and high-level tasks in deep learning, we propose a cascaded self-supervised architecture for EEG emotion recognition.
Baichuan 2: Open Large-scale Language Models
Large language models (LLMs) have demonstrated remarkable performance on a variety of natural language tasks based on just a few examples of natural language instructions, reducing the need for extensive feature engineering.
SC-NeRF: Self-Correcting Neural Radiance Field with Sparse Views
By combining these modules, our approach successfully tackles the challenges of outdoor scene generalization, producing high-quality rendering results.
Spatio-Temporal Domain Awareness for Multi-Agent Collaborative Perception
In this paper, we propose SCOPE, a novel collaborative perception framework that aggregates the spatio-temporal awareness characteristics across on-road agents in an end-to-end manner.
Multi-scale Spatial-temporal Interaction Network for Video Anomaly Detection
To address these deficiencies, we propose a Multi-scale Spatial-Temporal Interaction Network (MSTI-Net) for VAD.
A novel efficient Multi-view traffic-related object detection framework
With the rapid development of intelligent transportation system applications, a tremendous amount of multi-view video data has emerged to enhance vehicle perception.
Generalized Video Anomaly Event Detection: Systematic Taxonomy and Comparison of Deep Models
Video Anomaly Detection (VAD) serves as a pivotal technology in the intelligent surveillance systems, enabling the temporal or spatial identification of anomalous events within videos.
Learning Appearance-motion Normality for Video Anomaly Detection
Video anomaly detection is a challenging task in the computer vision community.
Rethinking Saliency Map: An Context-aware Perturbation Method to Explain EEG-based Deep Learning Model
The results of the experiments support the advantages of our method.
Learning from Attacks: Attacking Variational Autoencoder for Improving Image Classification
We propose an algorithmic framework that leverages the advantages of the DNNs for data self-expression and task-specific predictions, to improve image classification.
Towards Understanding the Cause of Error in Few-Shot Learning
We first introduce and derive a theoretical upper bound of error rate which is constrained to 1) linear separability in the learned embedding space and 2) discrepancy of task-specific and task-independent classifier.
BiOpt: Bi-Level Optimization for Few-Shot Segmentation
On each task, the inner loop aims to learn optimized prototypes from the query images.
Generalized Adaptation for Few-Shot Learning
Many Few-Shot Learning research works have two stages: pre-training base model and adapting to novel model.
Prototype Rectification for Few-Shot Learning
Few-shot learning requires to recognize novel classes with scarce labeled data.
