Search Results for author:
Found 8 papers, 3 papers with code
EgoNCE++: Do Egocentric Video-Language Models Really Understand Hand-Object Interactions?
Due to the occurrence of diverse EgoHOIs in the real world, we propose an open-vocabulary benchmark named EgoHOIBench to reveal the diminished performance of current egocentric video-language models (EgoVLM) on fined-grained concepts, indicating that these models still lack a full spectrum of egocentric understanding.
You Can Trade Your Experience in Distributed Multi-Agent Multi-Armed Bandits
In this paper, we propose a transaction-based multi-agent MAB framework, where agents can trade their bandit experience with each other to improve their total individual rewards.
AMDET: Attention based Multiple Dimensions EEG Transformer for Emotion Recognition
In this paper, we propose a deep model called Attention-based Multiple Dimensions EEG Transformer (AMDET), which can exploit the complementarity among the spectral-spatial-temporal features of EEG data by employing the multi-dimensional global attention mechanism.
Attention-Guided Black-box Adversarial Attacks with Large-Scale Multiobjective Evolutionary Optimization
In this paper, we propose an attention-guided black-box adversarial attack based on the large-scale multiobjective evolutionary optimization, termed as LMOA.
Interaction-aware Spatio-temporal Pyramid Attention Networks for Action Classification
Furthermore, since different layers in a deep network capture feature maps of different scales, we use these feature maps to construct a spatial pyramid and then utilize multi-scale information to obtain more accurate attention scores, which are used to weight the local features in all spatial positions of feature maps to calculate attention maps.
Spatio-Temporal Self-Organizing Map Deep Network for Dynamic Object Detection From Videos
In dynamic object detection, it is challenging to construct an effective model to sufficiently characterize the spatial-temporal properties of the background.
Joint RNN Model for Argument Component Boundary Detection
Argument Component Boundary Detection (ACBD) is an important sub-task in argumentation mining; it aims at identifying the word sequences that constitute argument components, and is usually considered as the first sub-task in the argumentation mining pipeline.
On the Effectiveness of Discretizing Quantitative Attributes in Linear Classifiers
It is often motivated by the limitation of some learners to qualitative data.
