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DehazeNet: An End-to-End System for Single Image Haze Removal
TLDR
This paper proposes a trainable end-to-end system called DehazeNet, for medium transmission estimation, which takes a hazy image as input, and outputs its medium transmission map that is subsequently used to recover a haze-free image via atmospheric scattering model.
Deep Ordinal Regression Network for Monocular Depth Estimation
TLDR
The proposed deep ordinal regression network (DORN) achieves state-of-the-art results on three challenging benchmarks, i.e., KITTI, Make3D, and NYU Depth v2, and outperforms existing methods by a large margin.
Benchmarking Single-Image Dehazing and Beyond
TLDR
This work presents a new large-scale benchmark consisting of both synthetic and real-world hazy images, called REalistic Single-Image DEhazing (RESIDE), which highlights diverse data sources and image contents, and is divided into five subsets, each serving different training or evaluation purposes.
MUlti-Store Tracker (MUSTer): A cognitive psychology inspired approach to object tracking
TLDR
Inspired by the well-known Atkinson-Shiffrin Memory Model, this work proposes MUlti-Store Tracker (MUSTer), a dual-component approach consisting of short- and long-term memory stores to process target appearance memories.
GoDec: Randomized Lowrank & Sparse Matrix Decomposition in Noisy Case
TLDR
This paper develops "Go Decomposition" (GoDec) to efficiently and robustly estimate the low-rank part L and the sparse part S of a matrix X = L + S + G with noise G to discover the robustness of GoDec.
NTIRE 2017 Challenge on Single Image Super-Resolution: Methods and Results
TLDR
This paper reviews the first challenge on single image super-resolution (restoration of rich details in an low resolution image) with focus on proposed solutions and results and gauges the state-of-the-art in single imagesuper-resolution.
General Tensor Discriminant Analysis and Gabor Features for Gait Recognition
TLDR
A general tensor discriminant analysis (GTDA) is developed as a preprocessing step for LDA for face recognition and achieves good performance for gait recognition based on image sequences from the University of South Florida (USF) HumanID Database.
Deep Modular Co-Attention Networks for Visual Question Answering
TLDR
A deep Modular Co-Attention Network (MCAN) that consists of Modular co-attention layers cascaded in depth that significantly outperforms the previous state-of-the-art models and is quantitatively and qualitatively evaluated on the benchmark VQA-v2 dataset.
Deep Domain Generalization via Conditional Invariant Adversarial Networks
TLDR
This work proposes an end-to-end conditional invariant deep domain generalization approach by leveraging deep neural networks for domain-invariant representation learning and proves the effectiveness of the proposed method.
Multi-modal Factorized Bilinear Pooling with Co-attention Learning for Visual Question Answering
TLDR
A Multi-modal Factorized Bilinear (MFB) pooling approach to efficiently and effectively combine multi- modal features, which results in superior performance for VQA compared with other bilinear pooling approaches.
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