Search Results for author:
Found 20 papers, 12 papers with code
InstructPipe: Building Visual Programming Pipelines with Human Instructions
Our user study (N=16) showed that InstructPipe empowers novice users to streamline their workflow in creating desired ML pipelines, reduce their learning curve, and spark innovative ideas with open-ended commands.
Category Feature Transformer for Semantic Segmentation
Aggregation of multi-stage features has been revealed to play a significant role in semantic segmentation.
Single Image Deraining via Feature-based Deep Convolutional Neural Network
A single image deraining algorithm based on the combination of data-driven and model-based approaches is proposed.
Recovering Sign Bits of DCT Coefficients in Digital Images as an Optimization Problem
Recovering unknown, missing, damaged, distorted, or lost information in DCT coefficients is a common task in multiple applications of digital image processing, including image compression, selective image encryption, and image communication.
Reversed Image Signal Processing and RAW Reconstruction. AIM 2022 Challenge Report
Cameras capture sensor RAW images and transform them into pleasant RGB images, suitable for the human eyes, using their integrated Image Signal Processor (ISP).
Overexposure Mask Fusion: Generalizable Reverse ISP Multi-Step Refinement
With the advent of deep learning methods replacing the ISP in transforming sensor RAW readings into RGB images, numerous methodologies solidified into real-life applications.
MIPI 2022 Challenge on RGBW Sensor Fusion: Dataset and Report
A detailed description of all models developed in this challenge is provided in this paper.
MIPI 2022 Challenge on RGBW Sensor Re-mosaic: Dataset and Report
A detailed description of all models developed in this challenge is provided in this paper.
MIPI 2022 Challenge on Quad-Bayer Re-mosaic: Dataset and Report
A detailed description of all models developed in this challenge is provided in this paper.
MIPI 2022 Challenge on RGB+ToF Depth Completion: Dataset and Report
A detailed description of all models developed in this challenge is provided in this paper.
MIPI 2022 Challenge on Under-Display Camera Image Restoration: Methods and Results
In this paper, we summarize and review the Under-Display Camera (UDC) Image Restoration track on MIPI 2022.
Approximate Nearest Neighbor Search under Neural Similarity Metric for Large-Scale Recommendation
In this paper, we propose a novel method to extend ANN search to arbitrary matching functions, e. g., a deep neural network.
Low-Light Image and Video Enhancement Using Deep Learning: A Survey
Low-light image enhancement (LLIE) aims at improving the perception or interpretability of an image captured in an environment with poor illumination.
EPRNet: Efficient Pyramid Representation Network for Real-Time Street Scene Segmentation
Current scene segmentation methods suffer from cumbersome model structures and high computational complexity, impeding their applications to real-world scenarios that require real-time processing.
HDR Video Reconstruction with Tri-Exposure Quad-Bayer Sensors
We propose a novel high dynamic range (HDR) video reconstruction method with new tri-exposure quad-bayer sensors.
STAR: A Structure-Aware Lightweight Transformer for Real-Time Image Enhancement
STAR is a general architecture that can be easily adapted to different image enhancement tasks.
AIM 2020 Challenge on Learned Image Signal Processing Pipeline
This paper reviews the second AIM learned ISP challenge and provides the description of the proposed solutions and results.
Defending Against Adversarial Attacks in Transmission- and Distribution-level PMU Data
Phasor measurement units (PMUs) provide high-fidelity data that improve situation awareness of electric power grid operations.
Attention-guided Chained Context Aggregation for Semantic Segmentation
The way features propagate in Fully Convolutional Networks is of momentous importance to capture multi-scale contexts for obtaining precise segmentation masks.
Ranked #23 on
Semantic Segmentation
on SUN-RGBD
(using extra training data)
Cross-lingual Data Transformation and Combination for Text Classification
In order to train a generalizable model, a large volume of text must be collected.
