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Found 10 papers, 5 papers with code
Vision-based Food Nutrition Estimation via RGB-D Fusion Network
In this paper, we designed one RGB-D fusion network, which integrated multimodal feature fusion (MMFF) and multi-scale fusion for visioin-based nutrition assessment.
Deep Learning for Logo Detection: A Survey
When logos are increasingly created, logo detection has gradually become a research hotspot across many domains and tasks.
Discriminative Semantic Feature Pyramid Network with Guided Anchoring for Logo Detection
Unlike general object detection, logo detection is a challenging task, especially for small logo objects and large aspect ratio logo objects in the real-world scenario.
FoodLogoDet-1500: A Dataset for Large-Scale Food Logo Detection via Multi-Scale Feature Decoupling Network
For that, we propose a novel food logo detection method Multi-scale Feature Decoupling Network (MFDNet), which decouples classification and regression into two branches and focuses on the classification branch to solve the problem of distinguishing multiple food logo categories.
A Holistically-Guided Decoder for Deep Representation Learning with Applications to Semantic Segmentation and Object Detection
With the proposed holistically-guided decoder, we implement the EfficientFCN architecture for semantic segmentation and HGD-FPN for object detection and instance segmentation.
Multi-organ Segmentation via Co-training Weight-averaged Models from Few-organ Datasets
In most scenarios, one might obtain annotations of a single or a few organs from one training set, and obtain annotations of the the other organs from another set of training images.
LogoDet-3K: A Large-Scale Image Dataset for Logo Detection
LogoDet-3K creates a more challenging benchmark for logo detection, for its higher comprehensive coverage and wider variety in both logo categories and annotated objects compared with existing datasets.
Ranked #1 on
Object Detection
on FlickrLogos-32
Robust Screening of COVID-19 from Chest X-ray via Discriminative Cost-Sensitive Learning
However, robust and accurate screening of COVID-19 from chest X-rays is still a globally recognized challenge because of two bottlenecks: 1) imaging features of COVID-19 share some similarities with other pneumonia on chest X-rays, and 2) the misdiagnosis rate of COVID-19 is very high, and the misdiagnosis cost is expensive.
Logo-2K+: A Large-Scale Logo Dataset for Scalable Logo Classification
Moreover, we propose a Discriminative Region Navigation and Augmentation Network (DRNA-Net), which is capable of discovering more informative logo regions and augmenting these image regions for logo classification.
Learning Deep Match Kernels for Image-Set Classification
Image-set classification has recently generated great popularity due to its widespread applications in computer vision.
