We address the challenging task of identifying, segmenting, and tracking
hand-held objects, which is crucial for applications such as human action
segmentation and performance evaluation. This task is particularly challenging
due to heavy occlusion, rapid motion, and the transitory nature of objects
being hand-held, where an object may be held, released, and subsequently picked
up again. To tackle these challenges, we have developed a novel
transformer-based architecture called HOIST-Former. HOIST-Former is adept at
spatially and temporally segmenting hands and objects by iteratively pooling
features from each other, ensuring that the processes of identification,
segmentation, and tracking of hand-held objects depend on the hands' positions
and their contextual appearance. We further refine HOIST-Former with a contact
loss that focuses on areas where hands are in contact with objects. Moreover,
we also contribute an in-the-wild video dataset called HOIST, which comprises
4,125 videos complete with bounding boxes, segmentation masks, and tracking IDs
for hand-held objects. Through experiments on the HOIST dataset and two
additional public datasets, we demonstrate the efficacy of HOIST-Former in
segmenting and tracking hand-held objects.

我们开发了一种名为 HOIST-Former 的新型基于 Transformer 的架构，通过迭代地从彼此汇集特征来空间和时间地分割手和物体，确保手持物体的识别、分割和跟踪过程依赖于手的位置和它们的上下文外观。我们通过在 HOIST 数据集和其他两个公开数据集上进行实验，证明了 HOIST-Former 在分割和跟踪手持物体方面的有效性。

野外手持物品识别、分割和追踪: HOIST-Former

HOIST-Former: Hand-held Objects Identification, Segmentation, and  Tracking in the Wild

In this work, we address the task of weakly-supervised human action
segmentation in long, untrimmed videos. Recent methods have relied on expensive
learning models, such as Recurrent Neural Networks (RNN) and Hidden Markov
Models (HMM). However, these methods suffer from expensive computational cost,
thus are unable to be deployed in large scale. To overcome the limitations, the
keys to our design are efficiency and scalability. We propose a novel action
modeling framework, which consists of a new temporal convolutional network,
named Temporal Convolutional Feature Pyramid Network (TCFPN), for predicting
frame-wise action labels, and a novel training strategy for weakly-supervised
sequence modeling, named Iterative Soft Boundary Assignment (ISBA), to align
action sequences and update the network in an iterative fashion. The proposed
framework is evaluated on two benchmark datasets, Breakfast and Hollywood
Extended, with four different evaluation metrics. Extensive experimental
results show that our methods achieve competitive or superior performance to
state-of-the-art methods.

本文提出了一种新的行为建模框架，包括一种名为 Temporal Convolutional Feature Pyramid Network（TCFPN）的新型时态卷积网络和一种名为 Iterative Soft Boundary Assignment（ISBA）的新型弱监督序列建模训练策略，用于进行长且未修剪的视频中的弱监督人类行为分割。 并以 Breakfast 和 Hollywood Extended 这两个基准数据集进行评估，实验结果表明，其方法达到了与最先进方法相当或更好的性能。