7,055 research outputs found
Importance-Aware Image Segmentation-based Semantic Communication for Autonomous Driving
This article studies the problem of image segmentation-based semantic
communication in autonomous driving. In real traffic scenes, detecting the key
objects (e.g., vehicles, pedestrians and obstacles) is more crucial than that
of other objects to guarantee driving safety. Therefore, we propose a vehicular
image segmentation-oriented semantic communication system, termed VIS-SemCom,
where image segmentation features of important objects are transmitted to
reduce transmission redundancy. First, to accurately extract image semantics,
we develop a semantic codec based on Swin Transformer architecture, which
expands the perceptual field thus improving the segmentation accuracy. Next, we
propose a multi-scale semantic extraction scheme via assigning the number of
Swin Transformer blocks for diverse resolution features, thus highlighting the
important objects' accuracy. Furthermore, the importance-aware loss is invoked
to emphasize the important objects, and an online hard sample mining (OHEM)
strategy is proposed to handle small sample issues in the dataset. Experimental
results demonstrate that the proposed VIS-SemCom can achieve a coding gain of
nearly 6 dB with a 60% mean intersection over union (mIoU), reduce the
transmitted data amount by up to 70% with a 60% mIoU, and improve the
segmentation intersection over union (IoU) of important objects by 4%, compared
to traditional transmission scheme.Comment: 10 pages, 8 figure
Cascaded Scene Flow Prediction using Semantic Segmentation
Given two consecutive frames from a pair of stereo cameras, 3D scene flow
methods simultaneously estimate the 3D geometry and motion of the observed
scene. Many existing approaches use superpixels for regularization, but may
predict inconsistent shapes and motions inside rigidly moving objects. We
instead assume that scenes consist of foreground objects rigidly moving in
front of a static background, and use semantic cues to produce pixel-accurate
scene flow estimates. Our cascaded classification framework accurately models
3D scenes by iteratively refining semantic segmentation masks, stereo
correspondences, 3D rigid motion estimates, and optical flow fields. We
evaluate our method on the challenging KITTI autonomous driving benchmark, and
show that accounting for the motion of segmented vehicles leads to
state-of-the-art performance.Comment: International Conference on 3D Vision (3DV), 2017 (oral presentation
Model Adaptation with Synthetic and Real Data for Semantic Dense Foggy Scene Understanding
This work addresses the problem of semantic scene understanding under dense
fog. Although considerable progress has been made in semantic scene
understanding, it is mainly related to clear-weather scenes. Extending
recognition methods to adverse weather conditions such as fog is crucial for
outdoor applications. In this paper, we propose a novel method, named
Curriculum Model Adaptation (CMAda), which gradually adapts a semantic
segmentation model from light synthetic fog to dense real fog in multiple
steps, using both synthetic and real foggy data. In addition, we present three
other main stand-alone contributions: 1) a novel method to add synthetic fog to
real, clear-weather scenes using semantic input; 2) a new fog density
estimator; 3) the Foggy Zurich dataset comprising real foggy images,
with pixel-level semantic annotations for images with dense fog. Our
experiments show that 1) our fog simulation slightly outperforms a
state-of-the-art competing simulation with respect to the task of semantic
foggy scene understanding (SFSU); 2) CMAda improves the performance of
state-of-the-art models for SFSU significantly by leveraging unlabeled real
foggy data. The datasets and code are publicly available.Comment: final version, ECCV 201
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