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Found 8 papers, 4 papers with code
Physical Backdoor: Towards Temperature-based Backdoor Attacks in the Physical World
In the digital realm, we evaluate our approach using benchmark datasets for TIOD, achieving an Attack Success Rate (ASR) of up to 98. 21%.
Distributed Graph Embedding with Information-Oriented Random Walks
Graph embedding maps graph nodes to low-dimensional vectors, and is widely adopted in machine learning tasks.
MALICE: Manipulation Attacks on Learned Image ComprEssion
To characterize the robustness of state-of-the-art learned image compression, we mount white-box and black-box attacks.
Convolutional Neural Network for Universal Sentence Embeddings
This paper proposes a simple CNN model for creating general-purpose sentence embeddings that can transfer easily across domains and can also act as effective initialization for downstream tasks.
Benchmarking Single Image Dehazing and Beyond
We present a comprehensive study and evaluation of existing single image dehazing algorithms, using a new large-scale benchmark consisting of both synthetic and real-world hazy images, called REalistic Single Image DEhazing (RESIDE).
AOD-Net: All-In-One Dehazing Network
This paper proposes an image dehazing model built with a convolutional neural network (CNN), called All-in-One Dehazing Network (AOD-Net).
Ranked #20 on
Image Dehazing
on SOTS Outdoor
End-to-End United Video Dehazing and Detection
Furthermore, we build an End-to-End United Video Dehazing and Detection Network(EVDD-Net), which concatenates and jointly trains EVD-Net with a video object detection model.
An All-in-One Network for Dehazing and Beyond
This paper proposes an image dehazing model built with a convolutional neural network (CNN), called All-in-One Dehazing Network (AOD-Net).
