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Found 13 papers, 4 papers with code
Label Budget Allocation in Multi-Task Learning
How should the label budget (i. e. the amount of money spent on labeling) be allocated among different tasks to achieve optimal multi-task performance?
PseudoSeg: Designing Pseudo Labels for Semantic Segmentation
We demonstrate the effectiveness of the proposed pseudo-labeling strategy in both low-data and high-data regimes.
Feature Space Augmentation for Long-Tailed Data
Real-world data often follow a long-tailed distribution as the frequency of each class is typically different.
Ranked #24 on
Long-tail Learning
on Places-LT
Detection and Tracking Meet Drones Challenge
We provide a large-scale drone captured dataset, VisDrone, which includes four tracks, i. e., (1) image object detection, (2) video object detection, (3) single object tracking, and (4) multi-object tracking.
VisDrone-DET2019: The Vision Meets Drone Object Detection in Image Challenge Results
Results of 33 object detection algorithms are presented.
Learning Non-Uniform Hypergraph for Multi-Object Tracking
The majority of Multi-Object Tracking (MOT) algorithms based on the tracking-by-detection scheme do not use higher order dependencies among objects or tracklets, which makes them less effective in handling complex scenarios.
Evolvement Constrained Adversarial Learning for Video Style Transfer
Video style transfer is a useful component for applications such as augmented reality, non-photorealistic rendering, and interactive games.
Robust Adversarial Perturbation on Deep Proposal-based Models
Adversarial noises are useful tools to probe the weakness of deep learning based computer vision algorithms.
Exploring the Vulnerability of Single Shot Module in Object Detectors via Imperceptible Background Patches
In this paper, we focus on exploring the vulnerability of the Single Shot Module (SSM) commonly used in recent object detectors, by adding small perturbations to patches in the background outside the object.
Occlusion-aware R-CNN: Detecting Pedestrians in a Crowd
Pedestrian detection in crowded scenes is a challenging problem since the pedestrians often gather together and occlude each other.
Ranked #10 on
Pedestrian Detection
on Caltech
(using extra training data)
Vision Meets Drones: A Challenge
In this paper we present a large-scale visual object detection and tracking benchmark, named VisDrone2018, aiming at advancing visual understanding tasks on the drone platform.
Single-Shot Refinement Neural Network for Object Detection
For object detection, the two-stage approach (e. g., Faster R-CNN) has been achieving the highest accuracy, whereas the one-stage approach (e. g., SSD) has the advantage of high efficiency.
Ranked #164 on
Object Detection
on COCO test-dev
Robust Subspace Recovery via Bi-Sparsity Pursuit
Successful applications of sparse models in computer vision and machine learning imply that in many real-world applications, high dimensional data is distributed in a union of low dimensional subspaces.
