Search Results for author:
Found 37 papers, 15 papers with code
Conditional Image Repainting via Semantic Bridge and Piecewise Value Function
We study conditional image repainting where a model is trained to generate visual content conditioned on user inputs, and composite the generated content seamlessly onto a user provided image while preserving the semantics of users' inputs.
Segment Anything Is Not Always Perfect: An Investigation of SAM on Different Real-world Applications
Recently, Meta AI Research approaches a general, promptable Segment Anything Model (SAM) pre-trained on an unprecedentedly large segmentation dataset (SA-1B).
A High-Performance Accelerator for Super-Resolution Processing on Embedded GPU
Recent years have witnessed impressive progress in super-resolution (SR) processing.
Evaluation of ChatGPT as a Question Answering System for Answering Complex Questions
As ChatGPT covers resources such as Wikipedia and supports natural language question answering, it has garnered attention as a potential replacement for traditional knowledge based question answering (KBQA) models.
Ranked #1 on
Question Answering
on GraphQuestions
NeRFLiX: High-Quality Neural View Synthesis by Learning a Degradation-Driven Inter-viewpoint MiXer
Neural radiance fields (NeRF) show great success in novel view synthesis.
Video-P2P: Video Editing with Cross-attention Control
This paper presents Video-P2P, a novel framework for real-world video editing with cross-attention control.
What Makes for Good Tokenizers in Vision Transformer?
The architecture of transformers, which recently witness booming applications in vision tasks, has pivoted against the widespread convolutional paradigm.
Image Inpainting via Iteratively Decoupled Probabilistic Modeling
To achieve high-quality results with low computational cost, we present a novel pixel spread model (PSM) that iteratively employs decoupled probabilistic modeling, combining the optimization efficiency of GANs with the prediction tractability of probabilistic models.
Mutual Guidance and Residual Integration for Image Enhancement
To address those issues, we propose a novel mutual guidance network (MGN) to perform effective bidirectional global-local information exchange while keeping a compact architecture.
Towards Efficient and Scale-Robust Ultra-High-Definition Image Demoireing
With the rapid development of mobile devices, modern widely-used mobile phones typically allow users to capture 4K resolution (i. e., ultra-high-definition) images.
Ranked #1 on
Image Restoration
on UHDM
Video Demoireing with Relation-Based Temporal Consistency
Moire patterns, appearing as color distortions, severely degrade image and video qualities when filming a screen with digital cameras.
MAT: Mask-Aware Transformer for Large Hole Image Inpainting
Recent studies have shown the importance of modeling long-range interactions in the inpainting problem.
Ranked #1 on
Image Inpainting
on CelebA-HQ
Exploring Motion Ambiguity and Alignment for High-Quality Video Frame Interpolation
Without the bells and whistles, our plug-and-play TCL is capable of improving the performance of existing VFI frameworks.
Ranked #1 on
Video Frame Interpolation
on Vimeo90K
SceneSqueezer: Learning To Compress Scene for Camera Relocalization
Standard visual localization methods build a priori 3D model of a scene which is used to establish correspondences against the 2D keypoints in a query image.
On Efficient Transformer-Based Image Pre-training for Low-Level Vision
Pre-training has marked numerous state of the arts in high-level computer vision, while few attempts have ever been made to investigate how pre-training acts in image processing systems.
Revisiting Temporal Alignment for Video Restoration
Long-range temporal alignment is critical yet challenging for video restoration tasks.
Ranked #1 on
Video Super-Resolution
on Vimeo-90K
Reviewing continual learning from the perspective of human-level intelligence
Different from previous reviews that mainly focus on the catastrophic forgetting phenomenon in CL, this paper surveys CL from a more macroscopic perspective based on the Stability Versus Plasticity mechanism.
Learning by Active Forgetting for Neural Networks
This paper presents a learning model by active forgetting mechanism with artificial neural networks.
MASA-SR: Matching Acceleration and Spatial Adaptation for Reference-Based Image Super-Resolution
Therefore, high-quality correspondence matching is critical.
LAPAR: Linearly-Assembled Pixel-Adaptive Regression Network for Single Image Super-Resolution and Beyond
Single image super-resolution (SISR) deals with a fundamental problem of upsampling a low-resolution (LR) image to its high-resolution (HR) version.
Unsupervised data augmentation for object detection
Data augmentation has always been an effective way to overcome overfitting issue when the dataset is small.
Best-Buddy GANs for Highly Detailed Image Super-Resolution
We consider the single image super-resolution (SISR) problem, where a high-resolution (HR) image is generated based on a low-resolution (LR) input.
MagGAN: High-Resolution Face Attribute Editing with Mask-Guided Generative Adversarial Network
We present Mask-guided Generative Adversarial Network (MagGAN) for high-resolution face attribute editing, in which semantic facial masks from a pre-trained face parser are used to guide the fine-grained image editing process.
MuCAN: Multi-Correspondence Aggregation Network for Video Super-Resolution
Motivated by these findings, we propose a temporal multi-correspondence aggregation strategy to leverage similar patches across frames, and a cross-scale nonlocal-correspondence aggregation scheme to explore self-similarity of images across scales.
Reinforcement Learning-Enabled Decision-Making Strategies for a Vehicle-Cyber-Physical-System in Connected Environment
As a typical vehicle-cyber-physical-system (V-CPS), connected automated vehicles attracted more and more attention in recent years.
Novel Human-Object Interaction Detection via Adversarial Domain Generalization
We study in this paper the problem of novel human-object interaction (HOI) detection, aiming at improving the generalization ability of the model to unseen scenarios.
A Spontaneous Driver Emotion Facial Expression (DEFE) Dataset for Intelligent Vehicles
In this paper, we introduce a new dataset, the driver emotion facial expression (DEFE) dataset, for driver spontaneous emotions analysis.
Convolution Neural Network Architecture Learning for Remote Sensing Scene Classification
The feature-learning procedure of CNN largely depends on the architecture of CNN.
Object-driven Text-to-Image Synthesis via Adversarial Training
In this paper, we propose Object-driven Attentive Generative Adversarial Newtorks (Obj-GANs) that allow object-centered text-to-image synthesis for complex scenes.
Rethinking on Multi-Stage Networks for Human Pose Estimation
Existing pose estimation approaches fall into two categories: single-stage and multi-stage methods.
Ranked #1 on
Pose Estimation
on COCO minival
Evolvement Constrained Adversarial Learning for Video Style Transfer
Video style transfer is a useful component for applications such as augmented reality, non-photorealistic rendering, and interactive games.
Who did What at Where and When: Simultaneous Multi-Person Tracking and Activity Recognition
We present a bootstrapping framework to simultaneously improve multi-person tracking and activity recognition at individual, interaction and social group activity levels.
STS Classification with Dual-stream CNN
Inspired by the dual-stream hypothesis in neural science, we propose a novel dual-stream framework for modeling the interweaved spatiotemporal dependency, and develop a convolutional neural network within this framework that aims to achieve high adaptability and flexibility in STS configurations from various diagonals, i. e., sequential order, dependency range and features.
Adaptive RNN Tree for Large-Scale Human Action Recognition
The RNNs in RNN-T are co-trained with the action category hierarchy, which determines the structure of RNN-T.
Ranked #86 on
Skeleton Based Action Recognition
on NTU RGB+D
POI: Multiple Object Tracking with High Performance Detection and Appearance Feature
In this paper, we explore the high-performance detection and deep learning based appearance feature, and show that they lead to significantly better MOT results in both online and offline setting.
Category-Blind Human Action Recognition: A Practical Recognition System
In this paper, we propose the category-blind human recognition method (CHARM) which can recognize a human action without making assumptions of the action category.
Multiple Target Tracking Based on Undirected Hierarchical Relation Hypergraph
Multi-target tracking is an interesting but challenging task in computer vision field.
