Search Results for author:
Found 10 papers, 4 papers with code
Spatio-Temporal Pixel-Level Contrastive Learning-based Source-Free Domain Adaptation for Video Semantic Segmentation
Unsupervised Domain Adaptation (UDA) of semantic segmentation transfers labeled source knowledge to an unlabeled target domain by relying on accessing both the source and target data.
Adaptive Distillation: Aggregating Knowledge from Multiple Paths for Efficient Distillation
Despite this advancement in different techniques for distilling the knowledge, the aggregation of different paths for distillation has not been studied comprehensively.
Ranked #33 on
Knowledge Distillation
on ImageNet
Learning Panoptic Segmentation from Instance Contours
Instance contours along with semantic segmentation yield a boundary aware semantic segmentation of things.
Adaptive Hierarchical Decomposition of Large Deep Networks
As datasets get larger, a natural question to ask is if existing deep learning architectures can be extended to handle the 50+K classes thought to be perceptible by a typical human.
FisheyeMultiNet: Real-time Multi-task Learning Architecture for Surround-view Automated Parking System
We provide a holistic overview of an industrial system covering the embedded system, use cases and the deep learning architecture.
WoodScape: A multi-task, multi-camera fisheye dataset for autonomous driving
Fisheye cameras are commonly employed for obtaining a large field of view in surveillance, augmented reality and in particular automotive applications.
MultiNet++: Multi-Stream Feature Aggregation and Geometric Loss Strategy for Multi-Task Learning
In this work, we propose a multi-stream multi-task network to take advantage of using feature representations from preceding frames in a video sequence for joint learning of segmentation, depth, and motion.
NeurAll: Towards a Unified Visual Perception Model for Automated Driving
In this paper, we propose a joint multi-task network design for learning several tasks simultaneously.
AuxNet: Auxiliary tasks enhanced Semantic Segmentation for Automated Driving
Decision making in automated driving is highly specific to the environment and thus semantic segmentation plays a key role in recognizing the objects in the environment around the car.
Multi-stream CNN based Video Semantic Segmentation for Automated Driving
We propose two simple high-level architectures based on Recurrent FCN (RFCN) and Multi-Stream FCN (MSFCN) networks.
