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Found 10 papers, 7 papers with code
OmniBal: Towards Fast Instruct-tuning for Vision-Language Models via Omniverse Computation Balance
We rebalanced the computational loads from data, model, and memory perspectives to address this issue, achieving more balanced computation across devices.
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 #18 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.
