Complex video object segmentation serves as a fundamental task for a wide
range of downstream applications such as video editing and automatic data
annotation. Here we present the 2nd place solution in the MOSE track of PVUW
2024. To mitigate problems caused by tiny objects, similar objects and fast
movements in MOSE. We use instance segmentation to generate extra pretraining
data from the valid and test set of MOSE. The segmented instances are combined
with objects extracted from COCO to augment the training data and enhance
semantic representation of the baseline model. Besides, motion blur is added
during training to increase robustness against image blur induced by motion.
Finally, we apply test time augmentation (TTA) and memory strategy to the
inference stage. Our method ranked 2nd in the MOSE track of PVUW 2024, with a
$\mathcal{J}$ of 0.8007, a $\mathcal{F}$ of 0.8683 and a
$\mathcal{J}$\&$\mathcal{F}$ of 0.8345.

我们在 MOSE 轨道的 PVUW 2024 比赛中排名第二，并采用实例分割、运动模糊、测试时间增强和记忆策略等方法来提高视频对象分割的准确性。

CVPR2024 PVUW 工作坊 MOSE 跟踪比赛第二名解决方案：复杂视频目标分割

2nd Place Solution for MOSE Track in CVPR 2024 PVUW workshop: Complex  Video Object Segmentation

This report introduce our work on Egocentric 3D Hand Pose Estimation
workshop. Using AssemblyHands, this challenge focuses on egocentric 3D hand
pose estimation from a single-view image. In the competition, we adopt ViT
based backbones and a simple regressor for 3D keypoints prediction, which
provides strong model baselines. We noticed that Hand-objects occlusions and
self-occlusions lead to performance degradation, thus proposed a non-model
method to merge multi-view results in the post-process stage. Moreover, We
utilized test time augmentation and model ensemble to make further improvement.
We also found that public dataset and rational preprocess are beneficial. Our
method achieved 12.21mm MPJPE on test dataset, achieve the first place in
Egocentric 3D Hand Pose Estimation challenge.

本研究报告介绍了我们在 Egocentric 3D Hand Pose Estimation 挑战赛上的工作。采用 AssemblyHands，我们使用基于 ViT 的骨干网络和简单回归器进行 3D 关键点预测，提供了强大的模型基线。我们提出了一种非模型方法，在后处理阶段合并多视图结果，以解决手 - 物体遮挡和自遮挡引起的性能下降问题。此外，我们利用测试时间增强和模型集成进一步改进了方法。我们还发现公共数据集和合理的预处理是有益的。我们的方法在测试数据集上实现了 12.21mm 的 MPJPE，并在 Egocentric 3D Hand Pose Estimation 挑战赛中获得第一名。

2023 以自我为中心的手部姿势估计挑战的第一名解决方案技术报告：一种简洁的以自我为中心的手部姿势重建流程

1st Place Solution of Egocentric 3D Hand Pose Estimation Challenge 2023  Technical Report:A Concise Pipeline for Egocentric Hand Pose Reconstruction

With the rise of Deep Neural Networks, machine learning systems are nowadays
ubiquitous in a number of real-world applications, which bears the need for
highly reliable models. This requires a thorough look not only at the accuracy
of such systems, but also to their predictive uncertainty. Hence, we propose a
novel technique (with two different variations, named M-ATTA and V-ATTA) based
on test time augmentation, to improve the uncertainty calibration of deep
models for image classification. Unlike other test time augmentation
approaches, M/V-ATTA improves uncertainty calibration without affecting the
model's accuracy, by leveraging an adaptive weighting system. We evaluate the
performance of the technique with respect to different metrics of uncertainty
calibration. Empirical results, obtained on CIFAR-10, CIFAR-100, as well as on
the benchmark Aerial Image Dataset, indicate that the proposed approach
outperforms state-of-the-art calibration techniques, while maintaining the
baseline classification performance. Code for M/V-ATTA available at:
this https URL

本文提出一种基于测试时数据扩充的新技术（分别名为 M-ATTA 和 V-ATTA），以提高深度模型的图像分类的不确定性校准，实证结果表明所提出的方法在保持基线分类性能的同时优于现有的校准技术。

深度神经网络中不确定性校准的测试时间增强方法

Approaching Test Time Augmentation in the Context of Uncertainty  Calibration for Deep Neural Networks

Face detection has received intensive attention in recent years. Many works
present lots of special methods for face detection from different perspectives
like model architecture, data augmentation, label assignment and etc., which
make the overall algorithm and system become more and more complex. In this
paper, we point out that \textbf{there is no gap between face detection and
generic object detection}. Then we provide a strong but simple baseline method
to deal with face detection named TinaFace. We use ResNet-50 \cite{he2016deep}
as backbone, and all modules and techniques in TinaFace are constructed on
existing modules, easily implemented and based on generic object detection. On
the hard test set of the most popular and challenging face detection benchmark
WIDER FACE \cite{yang2016wider}, with single-model and single-scale, our
TinaFace achieves 92.1\% average precision (AP), which exceeds most of the
recent face detectors with larger backbone. And after using test time
augmentation (TTA), our TinaFace outperforms the current state-of-the-art
method and achieves 92.4\% AP. The code will be available at
https://github.com/Media-Smart/vedadet.

该研究使用现有的模块和通用物体检测模型构建 TinaFace，实现人脸检测。TinaFace 在 WIDER FACE 挑战赛上实现了 92.1% 的精度。使用测试时增强后，TinaFace 的结果超过了当前最先进的方法，达到 92.4% 的平均精度（AP）。