Search Results for author:
Found 14 papers, 3 papers with code
FlowVid: Taming Imperfect Optical Flows for Consistent Video-to-Video Synthesis
This enables our model for video synthesis by editing the first frame with any prevalent I2I models and then propagating edits to successive frames.
Discovery of Small Ultra-short-period Planets Orbiting KG Dwarfs in Kepler Survey Using GPU Phase Folding and Deep Learning Detection System
K01821. b is a sub-Earth with a radius of $0. 648R_\oplus$, orbiting a G dwarf over a 0. 91978-day period.
AVID: Any-Length Video Inpainting with Diffusion Model
Given a video, a masked region at its initial frame, and an editing prompt, it requires a model to do infilling at each frame following the editing guidance while keeping the out-of-mask region intact.
The GPU Phase Folding and Deep Learning Method for Detecting Exoplanet Transits
This paper presents GPFC, a novel Graphics Processing Unit (GPU) Phase Folding and Convolutional Neural Network (CNN) system to detect exoplanets using the transit method.
Open-Vocabulary Semantic Segmentation with Mask-adapted CLIP
To address this, we propose to finetune CLIP on a collection of masked image regions and their corresponding text descriptions.
Human Behavior Recognition Method Based on CEEMD-ES Radar Selection
First, this method decomposes and reconstructs the radar signal according to the difference in the reflected echo frequency between the limbs and the trunk of the human body.
Generalized Few-Shot Semantic Segmentation: All You Need is Fine-Tuning
Generalized few-shot semantic segmentation was introduced to move beyond only evaluating few-shot segmentation models on novel classes to include testing their ability to remember base classes.
Objectness-Aware Few-Shot Semantic Segmentation
We demonstrate how to increase overall model capacity to achieve improved performance, by introducing objectness, which is class-agnostic and so not prone to overfitting, for complementary use with class-specific features.
Assessing Image Quality Issues for Real-World Problems
We introduce a new large-scale dataset that links the assessment of image quality issues to two practical vision tasks: image captioning and visual question answering.
Captioning Images Taken by People Who Are Blind
While an important problem in the vision community is to design algorithms that can automatically caption images, few publicly-available datasets for algorithm development directly address the interests of real users.
Unconstrained Foreground Object Search
Many people search for foreground objects to use when editing images.
Predicting How to Distribute Work Between Algorithms and Humans to Segment an Image Batch
We propose a resource allocation framework for predicting how best to allocate a fixed budget of human annotation effort in order to collect higher quality segmentations for a given batch of images and automated methods.
Guided Image Inpainting: Replacing an Image Region by Pulling Content from Another Image
Deep generative models have shown success in automatically synthesizing missing image regions using surrounding context.
Identifying the Absorption Bump with Deep Learning
In this paper, we apply Deep Learning techniques to detect the broad absorption bump.
