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:

Identifying salient points in images is a crucial component for visual odometry, Structure-from-Motion or SLAM algorithms.
Recently, several learned keypoint methods have demonstrated compelling performance on challenging benchmarks.  
However, generating consistent and accurate training data for interest-point detection in natural images still remains challenging, especially for human annotators.
We introduce IO-Net (i.e. InlierOutlierNet), a novel proxy task for the self-supervision of keypoint detection, description and matching.
By making the sampling of inlier-outlier sets from point-pair correspondences fully differentiable within the keypoint learning framework, we show that are able to simultaneously self-supervise keypoint description and improve keypoint matching.
Second, we introduce KeyPointNet, a keypoint-network architecture that is especially amenable to robust keypoint detection and description.
We design the network to allow local keypoint aggregation to avoid artifacts due to spatial discretizations commonly used for this task, and we improve fine-grained keypoint descriptor performance by taking advantage of efficient sub-pixel convolutions to upsample the descriptor feature-maps to a higher operating resolution.
Through extensive experiments and ablative analysis, we show that the proposed self-supervised keypoint learning method greatly improves the quality of feature matching and homography estimation on challenging benchmarks over the state-of-the-art.
Detecting interest points in RGB images and matching them across views is a fundamental capability of many robotic systems.
Tasks such Simultaneous Localization and Mapping (SLAM) (Cadena et al., 2016) , Structure-from-Motion (SfM) (Agarwal et al., 2010) and object detection assume that salient keypoints can be detected and re-identified in a wide range of scenarios, which requires invariance properties to lighting effects, viewpoint changes, scale, time of day, etc.
However, these tasks still mostly rely on handcrafted image features such as SIFT (Lowe et al., 1999) or ORB (Rublee et al., 2011) , which have been shown to be limited in performance when compared to learned alternatives (Balntas et al., 2017) .
Deep learning methods have revolutionized many computer vision applications including 2D/3D object detection (Lang et al., 2019; Tian et al., 2019) , semantic segmentation (Li et al., 2018; Kirillov et al., 2019) , human pose estimation (Sun et al., 2019) , etc.
However, most learning algorithms need supervision and rely on labels which are often expensive to acquire.
Moreover, supervising interest point detection is unnatural, as a human annotator cannot readily identify salient regions in images as well as key signatures or descriptors, which would allow their re-identification.
Selfsupervised learning methods have gained in popularity recently, being used for tasks such as depth regression (Guizilini et al., 2019) , tracking (Vondrick et al., 2018) and representation learning Kolesnikov et al., 2019) .
Following DeTone et al. (2018b) and Christiansen et al. (2019) , we propose a self-supervised methodology for jointly training a keypoint detector as well as its associated descriptor.
Our main contributions are:
(i) We introduce IO-Net (i.e. InlierOutlierNet), a novel proxy task for the self-supervision of keypoint detection, description and matching.
By using a neurally-guided outlier-rejection scheme (Brachmann & Rother, 2019) as an auxiliary task, we show that we are able to simultaneously self-supervise keypoint description and generate optimal inlier sets from possible corresponding point-pairs.
While the keypoint network is fully self-supervised, the network is able to effectively learn distinguishable features for two-view matching, via the flow of gradients from consistently matched point-pairs.
(ii) We introduce KeyPointNet, and propose two modifications to the keypoint-network architecture described in Christiansen et al. (2019) .
First, we allow the keypoint location head to regress keypoint locations outside their corresponding cells, enabling keypoint matching near and across cell-boundaries.
Second, by taking advantage of sub-pixel convolutions to interpolate the descriptor feature-maps to a higher resolution, we show that we are able to improve the fine-grained keypoint descriptor fidelity and performance especially as they retain more fine-grained detail for pixel-level metric learning in the self-supervised regime.
Through extensive experiments and ablation studies, we show that the proposed architecture allows us to establish state-of-the-art performance for the task of self-supervised keypoint detection, description and matching.
In this paper, we proposed a new learning scheme for training a keypoint detector and associated descriptor in a self-supervised fashion.
Different with existing methods, we used a proxy network to generate an extra supervisory signal from a task tightly connected to keypoint extraction: outlier rejection.
We show that even without an explicit keypoint descriptor loss in the IO-Net, the supervisory signal from the auxiliary task can be effectively propagated back to the keypoint network to generate distinguishable descriptors.
Using the combination of the proposed method as well as the improved network structure, we achieve competitive results in the homography estimation benchmark.
Learning to extract distinguishable keypoints from a proxy task, outlier rejection.