Below are the abstract, introduction, and conclusion of a computer science research paper. Please summarize the main contribution of the work in a single sentence. Your response should include the summary and no additional text.

Paper text:

Hierarchical label structures widely exist in many machine learning tasks, ranging from those with explicit label hierarchies such as image classification to the ones that have latent label hierarchies such as semantic segmentation.
Unfortunately, state-of-the-art methods often utilize cross-entropy loss which in-explicitly assumes the independence among class labels.
Motivated by the fact that class members from the same hierarchy need to be similar to each others, we design a new training diagram called Hierarchical Complement Objective Training (HCOT).
In HCOT, in addition to maximizing the probability of the ground truth class, we also neutralize the probabilities of rest of the classes in a hierarchical fashion, making the model take advantage of the label hierarchy explicitly.
We conduct our method on both image classification and semantic segmentation.
Results show that HCOT outperforms state-of-the-art models in CIFAR100, Imagenet, and PASCAL-context.
Our experiments also demonstrate that HCOT can be applied on tasks with latent label hierarchies, which is a common characteristic in many machine learning tasks.
Many machine learning tasks involve making predictions on classes that have an inherent hierarchical structure.
One example would be image classification with hierarchical categories, where a category shares the same parental category with other ones.
For example, the categories with label "dog" and "cat" might share a common parental category "pet", which forms a explicit label hierarchy.
Another example would be in the task of semantic segmentation, where "beach", and "sea" are under the same theme "scenery" which forms a latent label hierarchy, while "people", and "pets" forms another one of "portrait."
In this work, we call a parental category a coarse(-level) category, while a category under a coarse category is called a fine(-level) category.
Many successful deep learning models are built and trained with cross-entropy loss that assumes prediction classes to be mutually independent.
This assumption works well for many tasks such as traditional image classifications where no hierarchical information is present.
In the explicitly hierarchical setting, however, one problem is that learning with objectives that pose such a strong assumption makes the model difficult to utilize the hierarchical structure in the label space.
Another challenge in modeling hierarchical labels is that many tasks sometime exhibit latent label hierarchy.
Take semantic segmentation for example, an inherent hierarchical structure has been explored by (Zhang et al., 2018a) as "'global context".
However, the dataset itself does not contain hierarchical information.
In this paper, we develop techniques that are capable of leveraging the information in a label hierarchy, through proposing new training objectives.
Our proposed technique is different from previous methods (Yan et al., 2015; Murdock et al., 2016; Guo et al., 2018; Zhang et al., 2018a) which exploit the label hierarchy by changing model architectures but not the objectives.
The general idea we propose is to penalize incorrect classes at different granularity levels: the classes that are "obviously wrong"-different from not only the ground truth but also the parental category of ground truth-should receive larger penalty than the ones that share the same parental categories of ground truth.
Such a mechanism allows us to take advantage of the information in the label hierarchy during training.
To achieve this goal of training with hierarchy information, we introduce the concept of Complement Objective Training (COT) (Chen et al., 2019b; a) into label hierarchy.
In COT, the probability of Figure 1: Sorted predicted probabilities (denoted asŷ) from three different training paradigms evaluated on CIFAR-100 dataset using PreAct ResNet-18.
The red bar indicates the probability of the ground-truth (denoted asŷ g ), the green bars are the probabilities of classes in the same parental category as the ground-truth (denoted asŷ G\{g} ), and blue bars are the probabilities of the rest classes (denoted asŷ K\G , see Sec. 3 for detailed notation definition).
Notice the "staircase shape" in (c) showing the significant difference betweenŷ g andŷ G\{g} , and then betweenŷ G\{g} andŷ K\G , which confirms HCOT well captures the label hierarchy.
the correct class is maximized by a primary objective (i.e., cross-entropy), while the probability of incorrect classes are neutralized by a complement objective (Chen et al., 2019b) .
This training paradigm aims at widening the gaps between the predicted probability value of the ground truth and those of the incorrect classes.
In this paper, we propose Hierarchical Complement Objective Training (HCOT) with a novel complement objective called "Hierarchical Complement Entropy" (defined in Sec. 3), by applying the idea of the complement objective on both the fine-level class and its corresponding coarse-level classes.
HCOT learns the class probabilities by three folds:
(a) maximizing the predicted probability of ground truth,
(b) neutralizing the predicted probabilities of incorrect classes sharing the same coarselevel category as the ground truth, and
(c) further penalizing others that are on different branches (in the label hierarchy) to the ground-truth class.
Figure 1 illustrates the general idea of HCOT compared to cross-entropy and COT, which shows HCOT leads to both confident prediction for the ground-truth class and the predicted distribution that better reflects the label hierarchy (and therefore closer to the true data distribution).
Particularly, the probability mass of the classes belonging to the parental category of the ground truth (in green) to be significantly higher than the rest of the classes (in blue).
In other words, the model is trained to strongly penalize the obviously wrong classes that are completely irrelevant to both the ground-truth class and other classes belonging to the same parental category.
We conduct HCOT on two important problems: image classification and semantic segmentation.
Experimental results show that models trained with the Hierarchical complement entropy achieve significantly better performance over both cross-entropy and COT, across a wide range of stateof-the-art methods.
We also show that HCOT improves model performance when predicting the coarse-level classes.
And finally, we show that HCOT can deal with not only tasks with explicit label hierarchy but also those with latent label hierarchy.
To the best of our knowledge, HCOT is the first paradigm that trains deep neural models using an objective to leverage information from a label hierarchy, and leads to significant performance improvement.
In this paper, we propose Hierarchical Complement Objective Training (HCOT) to answer the motivational question.
HCOT is a new training paradigm that deploys Hierarchical Complement Entropy as the training objective to leverage information from label hierarchy.
HCOT neutralizes the probabilities of incorrect classes at different granularity: under the same parental category as the ground-truth class or not belong to the same branch.
HCOT has been extensively evaluated on image classification and semantic segmentation tasks, and experimental results confirm that models trained with HCOT significantly outperform the state-of-the-arts.
A straight-line future work is to extend HCOT into Natural Language Processing tasks which involve rich hierarchical information.
We propose Hierarchical Complement Objective Training, a novel training paradigm to effectively leverage category hierarchy in the labeling space on both image classification and semantic segmentation.