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Found 25 papers, 8 papers with code
DreamTime: An Improved Optimization Strategy for Text-to-3D Content Creation
Text-to-image diffusion models pre-trained on billions of image-text pairs have recently enabled text-to-3D content creation by optimizing a randomly initialized Neural Radiance Fields (NeRF) with score distillation.
NegVSR: Augmenting Negatives for Generalized Noise Modeling in Real-World Video Super-Resolution
On the contrary, simple combinations of classical degradation are used for real-world noise modeling, which led to the VSR model often being violated by out-of-distribution noise.
DreamWaltz: Make a Scene with Complex 3D Animatable Avatars
We present DreamWaltz, a novel framework for generating and animating complex 3D avatars given text guidance and parametric human body prior.
ReGeneration Learning of Diffusion Models with Rich Prompts for Zero-Shot Image Translation
To ensure consistent preservation of the shape during image editing, we propose cross-attention guidance based on regeneration learning.
LipsFormer: Introducing Lipschitz Continuity to Vision Transformers
In contrast to previous practical tricks that address training instability by learning rate warmup, layer normalization, attention formulation, and weight initialization, we show that Lipschitz continuity is a more essential property to ensure training stability.
Open-World Pose Transfer via Sequential Test-Time Adaption
In open-world conditions, the pose transfer task raises various independent signals: OOD appearance and skeleton, which need to be extracted and distributed in speciality.
OccluMix: Towards De-Occlusion Virtual Try-on by Semantically-Guided Mixup
Image Virtual try-on aims at replacing the cloth on a personal image with a garment image (in-shop clothes), which has attracted increasing attention from the multimedia and computer vision communities.
DnSwin: Toward Real-World Denoising via Continuous Wavelet Sliding-Transformer
Real-world image denoising is a practical image restoration problem that aims to obtain clean images from in-the-wild noisy inputs.
Criteria Comparative Learning for Real-scene Image Super-Resolution
Inspired by the observation that the contrastive relationship could also exist between the criteria, in this work, we propose a novel training paradigm for RealSR, named Criteria Comparative Learning (Cria-CL), by developing contrastive losses defined on criteria instead of image patches.
Real-World Image Super-Resolution by Exclusionary Dual-Learning
Real-world image super-resolution is a practical image restoration problem that aims to obtain high-quality images from in-the-wild input, has recently received considerable attention with regard to its tremendous application potentials.
Dual-Perspective Semantic-Aware Representation Blending for Multi-Label Image Recognition with Partial Labels
Specifically, an instance-perspective representation blending (IPRB) module is designed to blend the representations of the known labels in an image with the representations of the corresponding unknown labels in another image to complement these unknown labels.
Image Classification
Multi-label Image Recognition with Partial Labels
Heterogeneous Semantic Transfer for Multi-label Recognition with Partial Labels
Multi-label image recognition with partial labels (MLR-PL), in which some labels are known while others are unknown for each image, may greatly reduce the cost of annotation and thus facilitate large-scale MLR.
Exploring Negatives in Contrastive Learning for Unpaired Image-to-Image Translation
Recently, contrastive learning (CL) has been used to further investigate the image correspondence in unpaired image translation by using patch-based positive/negative learning.
Content-adaptive Representation Learning for Fast Image Super-resolution
In contrast to existing studies that ignore difficulty diversity, we adopt different stage of a neural network to perform image restoration.
GTAE: Graph-Transformer based Auto-Encoders for Linguistic-Constrained Text Style Transfer
Non-parallel text style transfer has attracted increasing research interests in recent years.
AIM 2020 Challenge on Real Image Super-Resolution: Methods and Results
This paper introduces the real image Super-Resolution (SR) challenge that was part of the Advances in Image Manipulation (AIM) workshop, held in conjunction with ECCV 2020.
Unsupervised Multi-view Clustering by Squeezing Hybrid Knowledge from Cross View and Each View
To ensure that we achieve effective sparse representation and clustering performance on the original data matrix, adaptive graph regularization and unsupervised clustering constraints are also incorporated in the proposed model to preserve the internal structural features of the data.
NTIRE 2020 Challenge on Perceptual Extreme Super-Resolution: Methods and Results
This paper reviews the NTIRE 2020 challenge on perceptual extreme super-resolution with focus on proposed solutions and results.
DDet: Dual-path Dynamic Enhancement Network for Real-World Image Super-Resolution
Previous image SR methods fail to exhibit similar performance on Real-SR as the image data is not aligned inherently.
Face Hallucination by Attentive Sequence Optimization with Reinforcement Learning
Face hallucination is a domain-specific super-resolution problem that aims to generate a high-resolution (HR) face image from a low-resolution~(LR) input.
Difficulty-aware Image Super Resolution via Deep Adaptive Dual-Network
To identify whether a region is easy or hard, we propose a novel image difficulty recognition network based on PSNR prior.
PIRM Challenge on Perceptual Image Enhancement on Smartphones: Report
This paper reviews the first challenge on efficient perceptual image enhancement with the focus on deploying deep learning models on smartphones.
Attention-Aware Face Hallucination via Deep Reinforcement Learning
Face hallucination is a domain-specific super-resolution problem with the goal to generate high-resolution (HR) faces from low-resolution (LR) input images.
Structure-Preserving Image Super-resolution via Contextualized Multi-task Learning
Single image super resolution (SR), which refers to reconstruct a higher-resolution (HR) image from the observed low-resolution (LR) image, has received substantial attention due to its tremendous application potentials.
Local- and Holistic- Structure Preserving Image Super Resolution via Deep Joint Component Learning
Recently, machine learning based single image super resolution (SR) approaches focus on jointly learning representations for high-resolution (HR) and low-resolution (LR) image patch pairs to improve the quality of the super-resolved images.
