research
∙
08/07/2023
TPTU: Task Planning and Tool Usage of Large Language Model-based AI Agents
With recent advancements in natural language processing, Large Language ...
share
research
∙
03/23/2023
Explore the Power of Synthetic Data on Few-shot Object Detection
Few-shot object detection (FSOD) aims to expand an object detector for n...
research
∙
03/15/2023
SeqCo-DETR: Sequence Consistency Training for Self-Supervised Object Detection with Transformers
Self-supervised pre-training and transformer-based networks have signifi...
research
∙
02/28/2023
An Effective Crop-Paste Pipeline for Few-shot Object Detection
Few-shot object detection (FSOD) aims to expand an object detector for n...
research
∙
01/26/2023
Explore the Power of Dropout on Few-shot Learning
The generalization power of the pre-trained model is the key for few-sho...
research
∙
10/12/2022
A Unified Framework with Meta-dropout for Few-shot Learning
Conventional training of deep neural networks usually requires a substan...
research
∙
08/11/2020
Rethinking Pseudo-LiDAR Representation
The recently proposed pseudo-LiDAR based 3D detectors greatly improve th...
research
∙
03/16/2020
Adapting Object Detectors with Conditional Domain Normalization
Real-world object detectors are often challenged by the domain gaps betw...
research
∙
02/05/2020
Monocular 3D Object Detection with Decoupled Structured Polygon Estimation and Height-Guided Depth Estimation
Monocular 3D object detection task aims to predict the 3D bounding boxes...
research
∙
03/26/2019
GS3D: An Efficient 3D Object Detection Framework for Autonomous Driving
We present an efficient 3D object detection framework based on a single ...
research
∙
10/08/2016
Crafting GBD-Net for Object Detection
The visual cues from multiple support regions of different sizes and res...
research
∙
04/09/2016
T-CNN: Tubelets with Convolutional Neural Networks for Object Detection from Videos
The state-of-the-art performance for object detection has been significa...
research
∙
12/09/2015
Window-Object Relationship Guided Representation Learning for Generic Object Detections
In existing works that learn representation for object detection, the re...
research
∙
12/17/2014
DeepID-Net: Deformable Deep Convolutional Neural Networks for Object Detection
In this paper, we propose deformable deep convolutional neural networks ...
research
∙
09/11/2014
