You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: A Review of Facial Landmark Extraction in 2D Images and Videos Using Deep Learning 
Chapter Title:  Conclusions 
1. Abstract of Cited Paper (BIB001):  Sparse signal models have been the focus of much recent research, leading to (or improving upon) state-of-the-art results in signal, image, and video restoration. This article extends this line of research into a novel framework for local image discrimination tasks, proposing an energy formulation with both sparse reconstruction and class discrimination components, jointly optimized during dictionary learning. This approach improves over the state of the art in texture segmentation experiments using the Brodatz database, and it paves the way for a novel scene analysis and recognition framework based on simultaneously learning discriminative and reconstructive dictionaries. Preliminary results in this direction using examples from the Pascal VOC06 and Graz02 datasets are presented as well. 
2. Abstract of Cited Paper (BIB002):  Local space-time features have recently become a popular video representation for action recognition. Several methods for feature localization and description have been proposed in the literature and promising recognition results were demonstrated for a number of action classes. The comparison of existing methods, however, is often limited given the different experimental settings used. The purpose of this paper is to evaluate and compare previously proposed space-time features in a common experimental setup. In particular, we consider four different feature detectors and six local feature descriptors and use a standard bag-of-features SVM approach for action recognition. We investigate the performance of these methods on a total of 25 action classes distributed over three datasets with varying difficulty. Among interesting conclusions, we demonstrate that regular sampling of space-time features consistently outperforms all tested space-time interest point detectors for human actions in realistic settings. We also demonstrate a consistent ranking for the majority of methods over different datasets and discuss their advantages and limitations. 
3. Abstract of Cited Paper (BIB003):  We present a hierarchical classification model that allows rare objects to borrow statistical strength from related objects that have many training examples. Unlike many of the existing object detection and recognition systems that treat different classes as unrelated entities, our model learns both a hierarchy for sharing visual appearance across 200 object categories and hierarchical parameters. Our experimental results on the challenging object localization and detection task demonstrate that the proposed model substantially improves the accuracy of the standard single object detectors that ignore hierarchical structure altogether. 
4. Abstract of Cited Paper (BIB004):  In this paper, we present supervision-by-registration, an unsupervised approach to improve the precision of facial landmark detectors on both images and video. Our key observation is that the detections of the same landmark in adjacent frames should be coherent with registration, i.e., optical flow. Interestingly, the coherency of optical flow is a source of supervision that does not require manual labeling, and can be leveraged during detector training. For example, we can enforce in the training loss function that a detected landmark at frame$_{t-1}$ followed by optical flow tracking from frame$_{t-1}$ to frame$_t$ should coincide with the location of the detection at frame$_t$. Essentially, supervision-by-registration augments the training loss function with a registration loss, thus training the detector to have output that is not only close to the annotations in labeled images, but also consistent with registration on large amounts of unlabeled videos. End-to-end training with the registration loss is made possible by a differentiable Lucas-Kanade operation, which computes optical flow registration in the forward pass, and back-propagates gradients that encourage temporal coherency in the detector. The output of our method is a more precise image-based facial landmark detector, which can be applied to single images or video. With supervision-by-registration, we demonstrate (1) improvements in facial landmark detection on both images (300W, ALFW) and video (300VW, Youtube-Celebrities), and (2) significant reduction of jittering in video detections.

Literature Review:
In this survey, recent deep learning-based 2D facial landmark extraction methods were reviewed. After analyzing face landmarking techniques, comparing performances, and seeing the main challenges, we can draw the conclusion that deep-learning-based algorithms outperform others in terms of precision. However, computation efficiency remains a major constraint, especially for video facial landmark extraction. Despite the fact that deep-learning methods achieve excellent performance in many datasets, facial landmark extraction on a limited resource platform has not been solved. One future research direction is to investigate compression methods, such as Shuffle-Net  . Another direction is to focus on precision BIB005 , where specific applications like animation demand high precision to result in perfect rendering. Other promising research paths in landmarking techniques are listed as the following: Sparse dictionaries, that is, the paradigm of recognition under sparsity constraint and building of discriminatory dictionaries seems to be one viable method. The discriminative sparse dictionary can be constructed per landmark BIB002  or collectively, as in BIB004 . Adaboost selected features for multiview landmarking: Gabor or Haar wavelet features selected via the modified Adaboost scheme, where commonality and a geometric configuration of landmark appearances is exploited BIB001 . Finally, multiframe landmarking: the determination of landmark positions exploits the information in subsequent frames of a video, using, for example, spatio-temporal representations BIB003 . Funding: This research received no external funding.