Autonomous vehicles is the task of making a vehicle that can guide itself without human conduction.
|TREND||DATASET||BEST METHOD||PAPER TITLE||PAPER||CODE||COMPARE|
Deep Learning (DL) has shown great success in many human-related tasks, which has led to its adoption in many computer vision based applications, such as security surveillance system, autonomous vehicles and healthcare.
Behavior of each of the surrounding vehicles is governed by the motion of its neighbor vehicles.
This technology enables drivers to use voice commands to control the vehicle and will be soon available in Advanced Driver Assistance Systems (ADAS).
In this paper, we propose a method for improving accuracy of edge models without any extra compute cost by means of automatic model specialization.
We also identify dominating modality problem when training a multimodal descriptor.
Apart from the dataset, we present a novel gradient-based algorithm for raindrop presence detection in a video sequence.
Finally, we design an ensemble model to combine the strengths of the different learning strategies.
Specifically, we design the Spatial Information Enhancement (SIE) module to predict the spatial shapes of the foreground points within proposals, and extract the structure information to learn the representative features for further box refinement.
In this paper, we create a novel dataset, TrafficQA (Traffic Question Answering), which takes the form of video QA based on the collected 10, 080 in-the-wild videos and annotated 62, 535 QA pairs, for benchmarking the cognitive capability of causal inference and event understanding models in complex traffic scenarios.
Ranked #1 on Video Question Answering on SUTD-TrafficQA
Self-supervised learning of depth map prediction and motion estimation from monocular video sequences is of vital importance -- since it realizes a broad range of tasks in robotics and autonomous vehicles.