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Found 22 papers, 3 papers with code
BlazeBVD: Make Scale-Time Equalization Great Again for Blind Video Deflickering
Developing blind video deflickering (BVD) algorithms to enhance video temporal consistency, is gaining importance amid the flourish of image processing and video generation.
Learning Dynamic Tetrahedra for High-Quality Talking Head Synthesis
Recent works in implicit representations, such as Neural Radiance Fields (NeRF), have advanced the generation of realistic and animatable head avatars from video sequences.
Understanding Oversmoothing in Diffusion-Based GNNs From the Perspective of Operator Semigroup Theory
This paper presents a novel study of the oversmoothing issue in diffusion-based Graph Neural Networks (GNNs).
Towards Optimal Adversarial Robust Q-learning with Bellman Infinity-error
Establishing robust policies is essential to counter attacks or disturbances affecting deep reinforcement learning (DRL) agents.
General Method for Solving Four Types of SAT Problems
We experimentally verify that this paradigm can prune the unnecessary search space to find the optimal Boolean assignments for the problem.
CARSS: Cooperative Attention-guided Reinforcement Subpath Synthesis for Solving Traveling Salesman Problem
The algorithm's primary objective is to enhance efficiency in terms of training memory consumption, testing time, and scalability, through the adoption of a multi-agent divide and conquer paradigm.
Multi-agent Reinforcement Learning
Traveling Salesman Problem
DiffBFR: Bootstrapping Diffusion Model Towards Blind Face Restoration
This design is implemented with two key components: 1) Identity Restoration Module (IRM) for preserving the face details in results.
Transforming Radiance Field with Lipschitz Network for Photorealistic 3D Scene Stylization
Simply coupling NeRF with photorealistic style transfer (PST) will result in cross-view inconsistency and degradation of stylized view syntheses.
StyO: Stylize Your Face in Only One-Shot
And it introduces a novel triple reconstruction loss to fine-tune the pre-trained LDM for encoding style and content into corresponding identifiers; 2) Fine-grained Content Controller (FCC) for the recombination phase.
DropKey for Vision Transformer
Given exploration on the above three questions, we present the novel DropKey method that regards Key as the drop unit and exploits decreasing schedule for drop ratio, improving ViTs in a general way.
Masked Reconstruction Contrastive Learning with Information Bottleneck Principle
In order to alleviate the discriminative information overfitting problem effectively, we employ the reconstruction task to regularize the discriminative task.
Ranked #23 on
Self-Supervised Image Classification
on ImageNet
Contrastive Learning
Self-Supervised Image Classification
+2
Comprehensive Analysis of Over-smoothing in Graph Neural Networks from Markov Chains Perspective
Results show that our proposed sufficient condition can effectively improve over-smoothing problem in operator-inconsistent GNN and enhance the performance of the model.
NeuroPrim: An Attention-based Model for Solving NP-hard Spanning Tree Problems
We apply our framework to three difficult problems on Euclidean space: the Degree-constrained Minimum Spanning Tree (DCMST) problem, the Minimum Routing Cost Spanning Tree (MRCST) problem, and the Steiner Tree Problem in graphs (STP).
Generalized One-shot Domain Adaptation of Generative Adversarial Networks
While previous works mainly focus on style transfer, we propose a novel and concise framework to address the \textit{generalized one-shot adaptation} task for both style and entity transfer, in which a reference image and its binary entity mask are provided.
DropKey
Given exploration on the above three questions, we present the novel DropKey method that regards Key as the drop unit and exploits decreasing schedule for drop ratio, improving ViTs in a general way.
PetsGAN: Rethinking Priors for Single Image Generation
Moreover, we apply our method to other image manipulation tasks (e. g., style transfer, harmonization), and the results further prove the effectiveness and efficiency of our method.
A Game-Theoretic Approach for Improving Generalization Ability of TSP Solvers
In this paper, we introduce a two-player zero-sum framework between a trainable \emph{Solver} and a \emph{Data Generator} to improve the generalization ability of deep learning-based solvers for Traveling Salesman Problem (TSP).
ExSinGAN: Learning an Explainable Generative Model from a Single Image
Generating images from a single sample, as a newly developing branch of image synthesis, has attracted extensive attention.
DFS: A Diverse Feature Synthesis Model for Generalized Zero-Shot Learning
In this paper, we propose to enhance the generalizability of GZSL models via improving feature diversity of unseen classes.
Blocked and Hierarchical Disentangled Representation From Information Theory Perspective
It is well known that information theory has an excellent explanatory meaning for the network, so we start to solve the disentanglement problem from the perspective of information theory.
A Robust Point Sets Matching Method
Point sets matching method is very important in computer vision, feature extraction, fingerprint matching, motion estimation and so on.
Fingerprint Classification Based on Depth Neural Network
Fingerprint classification is an effective technique for reducing the candidate numbers of fingerprints in the stage of matching in automatic fingerprint identification system (AFIS).
