user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Feature Intertwiner for Object Detection

Paper content:
A well-trained model should classify objects with unanimous score for every category.
This requires the high-level semantic features should be alike among samples, despite a wide span in resolution, texture, deformation, etc.
Previous works focus on re-designing the loss function or proposing new regularization constraints on the loss.
In this paper, we address this problem via a new perspective.
For each category, it is assumed that there are two sets in the feature space: one with more reliable information and the other with less reliable source.
We argue that the reliable set could guide the feature learning of the less reliable set during training - in spirit of student mimicking teacher’s behavior and thus pushing towards a more compact class centroid in the high-dimensional space.
Such a scheme also benefits the reliable set since samples become more closer within the same category - implying that it is easilier for the classifier to identify.
We refer to this mutual learning process as feature intertwiner and embed the spirit into object detection.
It is well-known that objects of low resolution are more difficult to detect due to the loss of detailed information during network forward pass.
We thus regard objects of high resolution as the reliable set and objects of low resolution as the less reliable set.
Specifically, an intertwiner is achieved by minimizing the distribution divergence between two sets.
We design a historical buffer to represent all previous samples in the reliable set and utilize them to guide the feature learning of the less reliable set.
The design of obtaining an effective feature representation for the reliable set is further investigated, where we introduce the optimal transport (OT) algorithm into the framework.
Samples in the less reliable set are better aligned with the reliable set with aid of OT metric.
Incorporated with such a plug-and-play intertwiner, we achieve an evident improvement over previous state-of-the-arts on the COCO object detection benchmark.
Classifying complex data in the high-dimensional feature space is the core of most machine learning problems, especially with the emergence of deep learning for better feature embedding (Krizhevsky et al., 2012; BID3 BID10 .
Previous methods address the feature representation problem by the conventional cross-entropy loss, l 1 / l 2 loss, or a regularization constraint on the loss term to ensure small intra-class variation and large inter-class distance (Janocha & Czarneck, 2017; BID16 BID29 BID15 .
The goal of these works is to learn more compact representation for each class in the feature space.
In this paper, we also aim for such a goal and propose a new perspective to address the problem.Our observation is that samples can be grouped into two sets in the feature space.
One set is more reliable, while the other is less reliable.
For example, visual samples may be less reliable due to low resolution, occlusion, adverse lighting, noise, blur, etc.
The learned features for samples from the reliable set are easier to classify than those from the less reliable one.
Our hypothesis is that the reliable set can guide the feature learning of the less reliable set, in the spirit of a teacher supervising the student.
We refer to this mutual learning process as a feature intertwiner.In this paper, a plug-and-play module, namely, feature intertwiner, is applied for object detection, which is the task of classifying and localizing objects in the wild.
An object of lower resolution will inevitably lose detailed information during the forward pass in the network.
Therefore, it is well-known that the detection accuracy drops significantly as resolutions of objects decrease.
We can treat samples with high resolution (often corresponds to large objects or region proposals) as the reliable set and samples with low resolution (small instances) as the less reliable set 1 .
Equipped with these two 'prototypical' sets, we can apply the feature intertwiner where the reliable set is leveraged to help the feature learning of the less reliable set.
Without intertwiner in
(a), samples are more scattered and separated from each other.
Note there are several samples that are far from its own class and close to the samples in other categories (e.g., class person in blue), indicating a potential mistake in classification.
With the aid of feature intertwiner in
(b), there is barely outlier sample outside each cluster.
the features in the lower resolution set approach closer to the features in the higher resolution set -achieving the goal of compact centroids in the feature space.
Empirically, these two settings correspond to the baseline and intertwiner experiments (marked in gray) in TAB3 .
The overall mAP metric increases from 32.8% to 35.2%, with an evident improvement of 2.6% for small instances and a satisfying increase of 0.8% for large counterparts.
This suggests the proposed feature intertwiner could benefit both sets.Two important modifications are incorporated based on the preliminary intertwiner framework.
The first is the use of class-dependent historical representative stored in a buffer.
Since there might be no large sample for the same category in one mini-batch during training, the record of all previous features of a given category for large instances is recorded by a representative, of which value gets updated dynamically as training evolves.
The second is an inclusion of the optimal transport (OT) divergence as a deluxe regularization in the feature intertwiner.
OT metric maps the comparison of two distributions on high-dimensional feature space onto a lower dimension space so that it is more sensible to measure the similarity between two distributions.
For the feature intertwiner, OT is capable of enforcing the less reliable set to be better aligned with the reliable set.We name the detection system equipped with the feature intertwiner as InterNet.
Full code suite is available at https://github.com/hli2020/feature intertwiner.
For brevity, we put the descriptions of dividing two sets in the detection task, related work (partial), background knowledge on OT theory and additional experiments in the appendix.
In this paper, we propose a feature intertwiner module to leverage the features from a more reliable set to help guide the feature learning of another less reliable set.
This is a better solution for generating a more compact centroid representation in the high-dimensional space.
It is assumed that the high-level semantic features within the same category should resemble as much as possible among samples with different visual variations.
The mutual learning process helps two sets to have closer distance within the cluster in each class.
The intertwiner is applied on the object detection task, where a historical buffer is proposed to address the sample missing problem during one mini-batch and the optimal transport (OT) theory is introduced to enforce the similarity among the two sets.
Since the features in the reliable set serve as teacher in the feature learning, careful preparation of such features is required so that they would match the information in the small-object set.
This is why we design different options for the large set and finally choose OT as a solution.
With aid of the feature intertwiner, we improve the detection performance by a large margin compared to previous state-of-the-arts, especially for small instances.Feature intertwiner is positioned as a general alternative to feature learning.
As long as there exists proper division of one reliable set and the other less reliable set, one can apply the idea of utilizing the reliable set guide the feature learning of another, based on the hypothesis that these two sets share similar distribution in some feature space.
One direction in the future work would be applying feature intertwiner into other domains, e.g., data classification, if proper set division are available.

assistant: text: (Camera-ready version) A feature intertwiner module to leverage features from one accurate set to help the learning of another less reliable set.