Fast and Accurate Lane Detection via Graph Structure and Disentangled Representation Learning

He, Yulin; Chen, Wei; Li, Chen; Luo, Xin; Huang, Lihua

doi:10.3390/s21144657

Cited by 4 publications

(3 citation statements)

References 32 publications

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“…Most recent research tends to focus on optimizing both the speed at which the lanes are detected, as well as the accuracy of said detections, by various methods, which can be observed in [8][9][10][11][12]21]. In a similar fashion, our approach also focused on improving the results in [7], managing to increase both the computational efficiency, and the accuracy, by reducing the number of edge pixels that need to be computed.…”

Section: Discussionmentioning

confidence: 99%

Robust Lane Detection and Tracking Algorithm for Steering Assist Systems

et al. 2021

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Modern vehicles rely on a multitude of sensors and cameras to both understand the environment around them and assist the driver in different situations. Lane detection is an overall process as it can be used in safety systems such as the lane departure warning system (LDWS). Lane detection may be used in steering assist systems, especially useful at night in the absence of light sources. Although developing such a system can be done simply by using global positioning system (GPS) maps, it is dependent on an internet connection or GPS signal, elements that may be absent in some locations. Because of this, such systems should also rely on computer vision algorithms. In this paper, we improve upon an existing lane detection method, by changing two distinct features, which in turn leads to better optimization and false lane marker rejection. We propose using a probabilistic Hough transform, instead of a regular one, as well as using a parallelogram region of interest (ROI), instead of a trapezoidal one. By using these two methods we obtain an increase in overall runtime of approximately 30%, as well as an increase in accuracy of up to 3%, compared to the original method.

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Section: Discussionmentioning

confidence: 99%

Robust Lane Detection and Tracking Algorithm for Steering Assist Systems

et al. 2021

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“…As discussed previously, verbal content within the memes prominently constitutes associated semantic undertones. Towards capturing the semantics from the textual content embedded within memes, we empirically designate the output from the last hidden layer of DeBERTa (He et al 2021), as our preferred choice. Besides demonstrating its superiority for a wide range of tasks, namely MNLI, SQuAD, RACE, etc., DeBERTa has demonstrated superior performance in detecting semantic role labels for entities in memes (Kun, Bankoti, and Kiskovski 2022).…”

Section: Entity Semanticsmentioning

confidence: 99%

What Do You MEME? Generating Explanations for Visual Semantic Role Labelling in Memes

Sharma

Agarwal

Suresh

et al. 2023

AAAI

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Memes are powerful means for effective communication on social media. Their effortless amalgamation of viral visuals and compelling messages can have far-reaching implications with proper marketing. Previous research on memes has primarily focused on characterizing their affective spectrum and detecting whether the meme's message insinuates any intended harm, such as hate, offense, racism, etc. However, memes often use abstraction, which can be elusive. Here, we introduce a novel task - EXCLAIM, generating explanations for visual semantic role labeling in memes. To this end, we curate ExHVV, a novel dataset that offers natural language explanations of connotative roles for three types of entities - heroes, villains, and victims, encompassing 4,680 entities present in 3K memes. We also benchmark ExHVV with several strong unimodal and multimodal baselines. Moreover, we posit LUMEN, a novel multimodal, multi-task learning framework that endeavors to address EXCLAIM optimally by jointly learning to predict the correct semantic roles and correspondingly to generate suitable natural language explanations. LUMEN distinctly outperforms the best baseline across 18 standard natural language generation evaluation metrics. Our systematic evaluation and analyses demonstrate that characteristic multimodal cues required for adjudicating semantic roles are also helpful for generating suitable explanations.

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“…In supervised deep learning, a large amount of labeled data needs to be collected for training [99,100], especially in the scorching field of autonomous driving. In this field, the perception of the environment of unmanned vehicles is particularly important [101,102]. The perception of the model directly affects the quality of decision making and plays a vital role in the safety of unmanned vehicles [103,104].…”

Section: Deep Learning-based Autonomous Drivingmentioning

confidence: 99%

Deep Active Learning for Computer Vision Tasks: Methodologies, Applications, and Challenges

Yao

2022

Applied Sciences

Self Cite

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Active learning is a label-efficient machine learning method that actively selects the most valuable unlabeled samples to annotate. Active learning focuses on achieving the best possible performance while using as few, high-quality sample annotations as possible. Recently, active learning achieved promotion combined with deep learning-based methods, which are named deep active learning methods in this paper. Deep active learning plays a crucial role in computer vision tasks, especially in label-insensitive scenarios, such as hard-to-label tasks (medical images analysis) and time-consuming tasks (autonomous driving). However, deep active learning still has some challenges, such as unstable performance and dirty data, which are future research trends. Compared with other reviews on deep active learning, our work introduced the deep active learning from computer vision-related methodologies and corresponding applications. The expected audience of this vision-friendly survey are researchers who are working in computer vision but willing to utilize deep active learning methods to solve vision problems. Specifically, this review systematically focuses on the details of methods, applications, and challenges in vision tasks, and we also introduce the classic theories, strategies, and scenarios of active learning in brief.

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Fast and Accurate Lane Detection via Graph Structure and Disentangled Representation Learning

Cited by 4 publications

References 32 publications

Robust Lane Detection and Tracking Algorithm for Steering Assist Systems

Robust Lane Detection and Tracking Algorithm for Steering Assist Systems

What Do You MEME? Generating Explanations for Visual Semantic Role Labelling in Memes

Deep Active Learning for Computer Vision Tasks: Methodologies, Applications, and Challenges

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