Search Results for author:
Found 11 papers, 3 papers with code
ASCM: An Answer Space Clustered Prompting Method without Answer Engineering
We also propose a stable semi-supervised method named stair learning (SL) that orderly distills knowledge from better models to weaker models.
UN-DETR: Promoting Objectness Learning via Joint Supervision for Unknown Object Detection
A key component of UOD is learning a generalized representation, i. e. objectness for both known and unknown categories to distinguish and localize objects from the background in a class-agnostic manner.
Design and Implementation of Emergency Simulated Lighting System Based on Tello UAV
Third, the flight control module has designed a specialized command control framework based on Tello UAV's API, which converts the planned flight path into command statements, forms flight text, and controls the flight of unmanned aerial vehicles accordingly.
Actor-Critic Policy Optimization in a Large-Scale Imperfect-Information Game
The deep policy gradient method has demonstrated promising results in many large-scale games, where the agent learns purely from its own experience.
LGA-RCNN: Loss-Guided Attention for Object Detection
Object detection is widely studied in computer vision filed.
Self-Paced Uncertainty Estimation for One-shot Person Re-Identification
The one-shot Person Re-ID scenario faces two kinds of uncertainties when constructing the prediction model from $X$ to $Y$.
Two-Step Image Dehazing with Intra-domain and Inter-domain Adaptation
To alleviate the intra-domain gap of the synthetic domain, we propose an intra-domain adaptation to align distributions of other subsets to the optimal subset by adversarial learning.
AFD-Net: Adaptive Fully-Dual Network for Few-Shot Object Detection
In this paper, we carefully analyze the characteristics of FSOD, and present that a general few-shot detector should consider the explicit decomposition of two subtasks, as well as leveraging information from both of them to enhance feature representations.
Detecting Code Clones with Graph Neural Networkand Flow-Augmented Abstract Syntax Tree
As far as we have concerned, we are the first to apply graph neural networks on the domain of code clone detection.
Linearization to Nonlinear Learning for Visual Tracking
Building on the theory of globally linear approximations to nonlinear functions, we introduce an elegant method that jointly learns a nonlinear classifier and a visual dictionary for tracking objects in a semi-supervised sparse coding fashion.
