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Found 23 papers, 11 papers with code
Global-and-Local Relative Position Embedding for Unsupervised Video Summarization
In order to summarize a content video properly, it is important to grasp the sequential structure of video as well as the long-term dependency between frames.
Deterministic Guidance Diffusion Model for Probabilistic Weather Forecasting
We also introduce and evaluate the Pacific Northwest Windstorm (PNW)-Typhoon weather satellite dataset to verify the effectiveness of DGDM in high-resolution regional forecasting.
Soundini: Sound-Guided Diffusion for Natural Video Editing
We propose a method for adding sound-guided visual effects to specific regions of videos with a zero-shot setting.
DIFu: Depth-Guided Implicit Function for Clothed Human Reconstruction
Beyond the SMPL, which provides skinned parametric human 3D information, in this paper, we propose a new IF-based method, DIFu, that utilizes a projected depth prior containing textured and non-parametric human 3D information.
IFQA: Interpretable Face Quality Assessment
Existing face restoration models have relied on general assessment metrics that do not consider the characteristics of facial regions.
Lightweight Alpha Matting Network Using Distillation-Based Channel Pruning
Therefore, there has been a demand for a lightweight alpha matting model due to the limited computational resources of commercial portable devices.
Weakly-Supervised Stitching Network for Real-World Panoramic Image Generation
Recently, there has been growing attention on an end-to-end deep learning-based stitching model.
Domain Adaptation without Source Data
Our key idea is to leverage a pre-trained model from the source domain and progressively update the target model in a self-learning manner.
Restore from Restored: Video Restoration with Pseudo Clean Video
We analyze the restoration performance of the fine-tuned video denoising networks with the proposed self-supervision-based learning algorithm, and demonstrate that the FCN can utilize recurring patches without requiring accurate registration among adjacent frames.
Restore from Restored: Single Image Denoising with Pseudo Clean Image
However, these methods have limitations in using internal information available in a given test image.
Fast Adaptation to Super-Resolution Networks via Meta-Learning
In the training stage, we train the network via meta-learning; thus, the network can quickly adapt to any input image at test time.
Self-Supervised Fast Adaptation for Denoising via Meta-Learning
Under certain statistical assumptions of noise, recent self-supervised approaches for denoising have been introduced to learn network parameters without true clean images, and these methods can restore an image by exploiting information available from the given input (i. e., internal statistics) at test time.
VisDrone-DET2019: The Vision Meets Drone Object Detection in Image Challenge Results
Results of 33 object detection algorithms are presented.
Preserving Semantic and Temporal Consistency for Unpaired Video-to-Video Translation
In this paper, we investigate the problem of unpaired video-to-video translation.
Key Instance Selection for Unsupervised Video Object Segmentation
After M-th frame, we select K IDs based on video saliency and frequency of appearance; then only these key IDs are tracked through the remaining frames.
Self-Supervised Video Representation Learning with Space-Time Cubic Puzzles
Self-supervised tasks such as colorization, inpainting and zigsaw puzzle have been utilized for visual representation learning for still images, when the number of labeled images is limited or absent at all.
Ranked #42 on
Self-Supervised Action Recognition
on HMDB51
Discriminative Feature Learning for Unsupervised Video Summarization
The proposed variance loss allows a network to predict output scores for each frame with high discrepancy which enables effective feature learning and significantly improves model performance.
Ranked #3 on
Unsupervised Video Summarization
on SumMe
Supervised Video Summarization
Unsupervised Video Summarization
LinkNet: Relational Embedding for Scene Graph
In this paper, we present a method that improves scene graph generation by explicitly modeling inter-dependency among the entire object instances.
Double JPEG Detection in Mixed JPEG Quality Factors using Deep Convolutional Neural Network
Double JPEG detection is essential for detecting various image manipulations.
Learning Image Representations by Completing Damaged Jigsaw Puzzles
The recovery of the aforementioned damage pushes the network to obtain robust and general-purpose representations.
Weakly- and Self-Supervised Learning for Content-Aware Deep Image Retargeting
Our method implicitly learns an attention map, which leads to a content-aware shift map for image retargeting.
Two-Phase Learning for Weakly Supervised Object Localization
Weakly supervised semantic segmentation and localiza- tion have a problem of focusing only on the most important parts of an image since they use only image-level annota- tions.
A Unified Approach of Multi-scale Deep and Hand-crafted Features for Defocus Estimation
In this paper, we introduce robust and synergetic hand-crafted features and a simple but efficient deep feature from a convolutional neural network (CNN) architecture for defocus estimation.
Ranked #2 on
Defocus Estimation
on CUHK - Blur Detection Dataset
