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Found 18 papers, 5 papers with code
RTNet: Relation Transformer Network for Diabetic Retinopathy Multi-lesion Segmentation
Automatic diabetic retinopathy (DR) lesions segmentation makes great sense of assisting ophthalmologists in diagnosis.
Delving into Sample Loss Curve to Embrace Noisy and Imbalanced Data
In practice, however, biased samples with corrupted labels and of tailed classes commonly co-exist in training data.
PAPooling: Graph-based Position Adaptive Aggregation of Local Geometry in Point Clouds
Fine-grained geometry, captured by aggregation of point features in local regions, is crucial for object recognition and scene understanding in point clouds.
Pyramid Correlation based Deep Hough Voting for Visual Object Tracking
Through experiments we find that, without regression, the performance could be equally promising as long as we delicately design the network to suit the training objective.
The 2nd Anti-UAV Workshop & Challenge: Methods and Results
The 2nd Anti-UAV Workshop \& Challenge aims to encourage research in developing novel and accurate methods for multi-scale object tracking.
Updatable Siamese Tracker with Two-stage One-shot Learning
Offline Siamese networks have achieved very promising tracking performance, especially in accuracy and efficiency.
Temporal Graph Modeling for Skeleton-based Action Recognition
Specifically, the constructed temporal relation graph explicitly builds connections between semantically related temporal features to model temporal relations between both adjacent and non-adjacent time steps.
Attribute-conditioned Layout GAN for Automatic Graphic Design
In this paper, we introduce Attribute-conditioned Layout GAN to incorporate the attributes of design elements for graphic layout generation by forcing both the generator and the discriminator to meet attribute conditions.
Local Grid Rendering Networks for 3D Object Detection in Point Clouds
The performance of 3D object detection models over point clouds highly depends on their capability of modeling local geometric patterns.
LayoutGAN: Generating Graphic Layouts with Wireframe Discriminator
Layouts are important for graphic design and scene generation.
LayoutGAN: Generating Graphic Layouts with Wireframe Discriminators
Layout is important for graphic design and scene generation.
Integrated Face Analytics Networks through Cross-Dataset Hybrid Training
Specifically, iFAN achieves an overall F-score of 91. 15% on the Helen dataset for face parsing, a normalized mean error of 5. 81% on the MTFL dataset for facial landmark localization and an accuracy of 45. 73% on the BNU dataset for emotion recognition with a single model.
Dual Path Networks
In this work, we present a simple, highly efficient and modularized Dual Path Network (DPN) for image classification which presents a new topology of connection paths internally.
Perceptual Generative Adversarial Networks for Small Object Detection
In this work, we address the small object detection problem by developing a single architecture that internally lifts representations of small objects to "super-resolved" ones, achieving similar characteristics as large objects and thus more discriminative for detection.
Multi-stage Object Detection with Group Recursive Learning
Most of existing detection pipelines treat object proposals independently and predict bounding box locations and classification scores over them separately.
Attentive Contexts for Object Detection
We provide preliminary answers to these questions through developing a novel Attention to Context Convolution Neural Network (AC-CNN) based object detection model.
Seq-NMS for Video Object Detection
Video object detection is challenging because objects that are easily detected in one frame may be difficult to detect in another frame within the same clip.
Scale-aware Fast R-CNN for Pedestrian Detection
Taking pedestrian detection as an example, we illustrate how we can leverage this philosophy to develop a Scale-Aware Fast R-CNN (SAF R-CNN) framework.
Ranked #21 on
Pedestrian Detection
on Caltech
