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Found 9 papers, 6 papers with code
From Isolated Islands to Pangea: Unifying Semantic Space for Human Action Understanding
By aligning the classes of previous datasets to our semantic space, we gather (image/video/skeleton/MoCap) datasets into a unified database in a unified label system, i. e., bridging "isolated islands" into a "Pangea".
Improving Long-tailed Object Detection with Image-Level Supervision by Multi-Task Collaborative Learning
Specifically, there are an object detection task (consisting of an instance-classification task and a localization task) and an image-classification task in our framework, responsible for utilizing the two types of supervision.
The Equalization Losses: Gradient-Driven Training for Long-tailed Object Recognition
Long-tail distribution is widely spread in real-world applications.
Equalized Focal Loss for Dense Long-Tailed Object Detection
The conventional focal loss balances the training process with the same modulating factor for all categories, thus failing to handle the long-tailed problem.
RefineMask: Towards High-Quality Instance Segmentation with Fine-Grained Features
In this work, we propose a new method called RefineMask for high-quality instance segmentation of objects and scenes, which incorporates fine-grained features during the instance-wise segmenting process in a multi-stage manner.
Equalization Loss v2: A New Gradient Balance Approach for Long-tailed Object Detection
To address the problem of imbalanced gradients, we introduce a new version of equalization loss, called equalization loss v2 (EQL v2), a novel gradient guided reweighing mechanism that re-balances the training process for each category independently and equally.
Ranked #12 on
Instance Segmentation
on LVIS v1.0 val
1st Place Solution of LVIS Challenge 2020: A Good Box is not a Guarantee of a Good Mask
This article introduces the solutions of the team lvisTraveler for LVIS Challenge 2020.
Ranked #1 on
Instance Segmentation
on LVIS v1.0 test-dev
Equalization Loss for Long-Tailed Object Recognition
Based on it, we propose a simple but effective loss, named equalization loss, to tackle the problem of long-tailed rare categories by simply ignoring those gradients for rare categories.
Ranked #17 on
Long-tail Learning
on CIFAR-10-LT (ρ=10)
Equalization Loss for Large Vocabulary Instance Segmentation
Recent object detection and instance segmentation tasks mainly focus on datasets with a relatively small set of categories, e. g. Pascal VOC with 20 classes and COCO with 80 classes.
