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Found 12 papers, 6 papers with code
Weakly-Supervised Detection of Bone Lesions in CT
First, we used the bone lesions that were prospectively marked by radiologists in a few 2D slices of CT volumes and converted them into weak 3D segmentation masks.
Chat-REC: Towards Interactive and Explainable LLMs-Augmented Recommender System
Large language models (LLMs) have demonstrated their significant potential to be applied for addressing various application tasks.
Modeling Global Distribution for Federated Learning with Label Distribution Skew
It introduces a global Generative Adversarial Network to model the global data distribution without access to local datasets, so the global model can be trained using the global information of data distribution without privacy leakage.
ReCo: A Dataset for Residential Community Layout Planning
In order to address the issues and advance a benchmark dataset for various intelligent spatial design and analysis applications in the development of smart city, we introduce Residential Community Layout Planning (ReCo) Dataset, which is the first and largest open-source vector dataset related to real-world community to date.
Bidirectional Regression for Arbitrary-Shaped Text Detection
Arbitrary-shaped text detection has recently attracted increasing interests and witnessed rapid development with the popularity of deep learning algorithms.
CentripetalText: An Efficient Text Instance Representation for Scene Text Detection
For the task of end-to-end scene text recognition, our method outperforms Mask TextSpotter v3 by 1. 1% on Total-Text.
Low-Power Computer Vision: Status, Challenges, Opportunities
In addition to mobile phones, many autonomous systems rely on visual data for making decisions and some of these systems have limited energy (such as unmanned aerial vehicles also called drones and mobile robots).
Low Power Inference for On-Device Visual Recognition with a Quantization-Friendly Solution
The IEEE Low-Power Image Recognition Challenge (LPIRC) is an annual competition started in 2015 that encourages joint hardware and software solutions for computer vision systems with low latency and power.
M2Det: A Single-Shot Object Detector based on Multi-Level Feature Pyramid Network
Finally, we gather up the decoder layers with equivalent scales (sizes) to develop a feature pyramid for object detection, in which every feature map consists of the layers (features) from multiple levels.
Ranked #134 on
Object Detection
on COCO test-dev
2018 Low-Power Image Recognition Challenge
The Low-Power Image Recognition Challenge (LPIRC, https://rebootingcomputing. ieee. org/lpirc) is an annual competition started in 2015.
CFENet: An Accurate and Efficient Single-Shot Object Detector for Autonomous Driving
The ability to detect small objects and the speed of the object detector are very important for the application of autonomous driving, and in this paper, we propose an effective yet efficient one-stage detector, which gained the second place in the Road Object Detection competition of CVPR2018 workshop - Workshop of Autonomous Driving(WAD).
A Quantization-Friendly Separable Convolution for MobileNets
As deep learning (DL) is being rapidly pushed to edge computing, researchers invented various ways to make inference computation more efficient on mobile/IoT devices, such as network pruning, parameter compression, and etc.
