Search Results for author:
Found 20 papers, 11 papers with code
State Space Model for New-Generation Network Alternative to Transformers: A Survey
In this paper, we give the first comprehensive review of these works and also provide experimental comparisons and analysis to better demonstrate the features and advantages of SSM.
A Transformer-Based Model for the Prediction of Human Gaze Behavior on Videos
Eye-tracking applications that utilize the human gaze in video understanding tasks have become increasingly important.
Gaze-Guided Graph Neural Network for Action Anticipation Conditioned on Intention
We introduce the Gaze-guided Action Anticipation algorithm, which establishes a visual-semantic graph from the video input.
Stepwise Self-Consistent Mathematical Reasoning with Large Language Models
SSC-CoT employs a strategy of selecting intermediate steps based on the intersection of various reasoning chains.
I-CEE: Tailoring Explanations of Image Classification Models to User Expertise
Informed by existing work, I-CEE explains the decisions of image classification models by providing the user with an informative subset of training data (i. e., example images), corresponding local explanations, and model decisions.
Unleashing the Power of CNN and Transformer for Balanced RGB-Event Video Recognition
It is intuitive to combine them for high-performance RGB-Event based video recognition, however, existing works fail to achieve a good balance between the accuracy and model parameters, as shown in Fig.~\ref{firstimage}.
SSTFormer: Bridging Spiking Neural Network and Memory Support Transformer for Frame-Event based Recognition
Secondly, they adopt either Spiking Neural Networks (SNN) for energy-efficient recognition with suboptimal results, or Artificial Neural Networks (ANN) for energy-intensive, high-performance recognition.
Efficient GNN Explanation via Learning Removal-based Attribution
A strategy of subgraph sampling is designed in LARA to improve the scalability of the training process.
DynStatF: An Efficient Feature Fusion Strategy for LiDAR 3D Object Detection
In this work, we propose a novel feature fusion strategy, DynStaF (Dynamic-Static Fusion), which enhances the rich semantic information provided by the multi-frame (dynamic branch) with the accurate location information from the current single-frame (static branch).
Towards Human-centered Explainable AI: A Survey of User Studies for Model Explanations
A better understanding of the needs of XAI users, as well as human-centered evaluations of explainable models are both a necessity and a challenge.
Explainable Artificial Intelligence (XAI)
Explainable Models
+2
When are Post-hoc Conceptual Explanations Identifiable?
Interest in understanding and factorizing learned embedding spaces through conceptual explanations is steadily growing.
Where and What: Driver Attention-based Object Detection
Human drivers use their attentional mechanisms to focus on critical objects and make decisions while driving.
A Consistent and Efficient Evaluation Strategy for Attribution Methods
With a variety of local feature attribution methods being proposed in recent years, follow-up work suggested several evaluation strategies.
Human Attention in Fine-grained Classification
The way humans attend to, process and classify a given image has the potential to vastly benefit the performance of deep learning models.
Ranked #41 on
Fine-Grained Image Classification
on CUB-200-2011
Artificial Intelligence Methods in In-Cabin Use Cases: A Survey
As interest in autonomous driving increases, efforts are being made to meet requirements for the high-level automation of vehicles.
Explainable Online Validation of Machine Learning Models for Practical Applications
The presented algorithm based on conditional probabilities is also online capable and requires only a fraction of memory compared to the kNN algorithm.
Driver Intention Anticipation Based on In-Cabin and Driving SceneMonitoring
Numerous car accidents are caused by improper driving maneuvers.
DriverMHG: A Multi-Modal Dataset for Dynamic Recognition of Driver Micro Hand Gestures and a Real-Time Recognition Framework
In this work, we propose an HCI system for dynamic recognition of driver micro hand gestures, which can have a crucial impact in automotive sector especially for safety related issues.
Fully Convolutional Neural Networks for Raw Eye Tracking Data Segmentation, Generation, and Reconstruction
In this paper, we use fully convolutional neural networks for the semantic segmentation of eye tracking data.
Talking With Your Hands: Scaling Hand Gestures and Recognition With CNNs
The use of hand gestures provides a natural alternative to cumbersome interface devices for Human-Computer Interaction (HCI) systems.
