What's There in the Dark

2019-09-24 · Sauradip Nag, Saptakatha Adak, Sukhendu Das

Scene Parsing is an important cog for modern autonomousdriving systems. Most of the works in semantic segmenta-tion pertains to day-time scenes with favourable weather andillumination conditions. In this paper, we propose a noveldeep architecture, NiSeNet, that performs semantic segmen-tation of night scenes using a domain mapping approach ofsynthetic to real data. It is a dual-channel network, wherewe designed a Real channel using DeepLabV3+ coupled withan MSE loss to preserve the spatial information. In addition,we used an Adaptive channel reducing the domain gap be-tween synthetic and real night images, which also comple-ments the failures of Real channel output. Apart from thedual channel, we introduced a novel fusion scheme to fuse theoutputs of two channels. In addition to that, we compiled anew dataset Urban Night Driving Dataset (UNDD); it consistsof7125unlabelled day and night images; additionally, it has75night images with pixel-level annotations having classesequivalent to Cityscapes dataset. We evaluated our approachon the Berkley Deep Drive dataset, the challenging Mapil-lary dataset and UNDD dataset to exhibit that the proposedmethod outperforms the state-of-the-art techniques in termsof accuracy and visual quality

📄 PDF Abstract BibTeX

Code (1)

sauradip/night_image_semantic_segmentation 공식 구현 pytorch

Tasks

Scene Parsing Semantic Segmentation

Methods 이 논문이 사용한 방법론

Spatial Pyramid Pooling Spatial Pyramid Pooling (SPP) is a pooling layer that removes the fixed-size constraint of the network, i.e. a CNN does not require a fixed-size input image. Specifically, we…

Batch Normalization 설명 없음

1x1 Convolution A 1 x 1 Convolution is a convolution with some special properties in that it can be used for dimensionality reduction,…

Dilated Convolution 설명 없음

ASPP Atrous Spatial Pyramid Pooling (ASPP) is a semantic segmentation module for resampling a given feature layer at multiple rates prior to…

DeepLabv3 설명 없음

DarkVesselNet: Multi-Modal Remote Sensing and Trajectory Reasoning for Dark Vessel Detection

2026-05-30 · Arun Sharma arxiv

Dark vessel detection requires fusing what vessels report through AIS with what satellites observe through radar and optical sensors. DarkVesselNet is a multi-modal remote sensing stack that combines Sentinel-1 SAR, Sent…

Vessel Detection

First-Person Activity Forecasting with Online Inverse Reinforcement Learning

2016-12-22 · ICCV 2017 10 · Nicholas Rhinehart, Kris M. Kitani

We address the problem of incrementally modeling and forecasting long-term goals of a first-person camera wearer: what the user will do, where they will go, and what goal they seek. In contrast to prior work in trajector…

reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)Trajectory Forecasting

Say What I Want: Towards the Dark Side of Neural Dialogue Models

2019-09-13 · Haochen Liu, Tyler Derr, Zitao Liu, Jiliang Tang

Neural dialogue models have been widely adopted in various chatbot applications because of their good performance in simulating and generalizing human conversations. However, there exists a dark side of these models -- d…

Chatbot Reinforcement Learning Reinforcement Learning (RL)

Interpreting Deep Classifier by Visual Distillation of Dark Knowledge

2018-03-11 · Kai Xu, Dae Hoon Park, Chang Yi, Charles Sutton

Interpreting black box classifiers, such as deep networks, allows an analyst to validate a classifier before it is deployed in a high-stakes setting. A natural idea is to visualize the deep network's representations, so …

Dimensionality Reduction Model Compression

Learning of Colors from Color Names: Distribution and Point Estimation

2017-09-27 · Lyndon White, Roberto Togneri, Wei Liu, Mohammed Bennamoun

Color names are often made up of multiple words. As a task in natural language understanding we investigate in depth the capacity of neural networks based on sums of word embeddings (SOWE), recurrence (LSTM and GRU based…

Natural Language Understanding Word Embeddings