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09/21/2023
DimCL: Dimensional Contrastive Learning For Improving Self-Supervised Learning
Self-supervised learning (SSL) has gained remarkable success, for which ...
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09/11/2023
CNN or ViT? Revisiting Vision Transformers Through the Lens of Convolution
The success of Vision Transformer (ViT) has been widely reported on a wi...
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07/25/2023
FedMEKT: Distillation-based Embedding Knowledge Transfer for Multimodal Federated Learning
Federated learning (FL) enables a decentralized machine learning paradig...
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07/07/2023
Swin Transformer-Based Dynamic Semantic Communication for Multi-User with Different Computing Capacity
Semantic communication has gained significant attention from researchers...
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06/25/2023
Faster Segment Anything: Towards Lightweight SAM for Mobile Applications
Segment anything model (SAM) is a prompt-guided vision foundation model ...
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06/23/2023
Robustness of Segment Anything Model (SAM) for Autonomous Driving in Adverse Weather Conditions
Segment Anything Model (SAM) has gained considerable interest in recent ...
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06/13/2023
Robustness of SAM: Segment Anything Under Corruptions and Beyond
Segment anything model (SAM), as the name suggests, is claimed to be cap...
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06/03/2023
Segment Anything Meets Semantic Communication
In light of the diminishing returns of traditional methods for enhancing...
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05/10/2023
When ChatGPT for Computer Vision Will Come? From 2D to 3D
ChatGPT and its improved variant GPT4 have revolutionized the NLP field ...
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05/10/2023
Generative AI meets 3D: A Survey on Text-to-3D in AIGC Era
Generative AI (AIGC, a.k.a. AI generated content) has made remarkable pr...
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05/01/2023
Attack-SAM: Towards Evaluating Adversarial Robustness of Segment Anything Model
Segment Anything Model (SAM) has attracted significant attention recentl...
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04/29/2023
Segment Anything Model (SAM) Meets Glass: Mirror and Transparent Objects Cannot Be Easily Detected
Meta AI Research has recently released SAM (Segment Anything Model) whic...
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04/15/2023
What if we have Meta GPT? From Content Singularity to Human-Metaverse Interaction in AIGC Era
The global metaverse development is facing a "cooldown moment", while th...
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04/04/2023
A Survey on Graph Diffusion Models: Generative AI in Science for Molecule, Protein and Material
Diffusion models have become a new SOTA generative modeling method in va...
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04/04/2023
One Small Step for Generative AI, One Giant Leap for AGI: A Complete Survey on ChatGPT in AIGC Era
OpenAI has recently released GPT-4 (a.k.a. ChatGPT plus), which is demon...
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04/01/2023
MP-FedCL: Multi-Prototype Federated Contrastive Learning for Edge Intelligence
Federated learning-assisted edge intelligence enables privacy protection...
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03/23/2023
A Survey on Audio Diffusion Models: Text To Speech Synthesis and Enhancement in Generative AI
Generative AI has demonstrated impressive performance in various fields,...
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03/21/2023
A Complete Survey on Generative AI (AIGC): Is ChatGPT from GPT-4 to GPT-5 All You Need?
As ChatGPT goes viral, generative AI (AIGC, a.k.a AI-generated content) ...
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03/14/2023
Text-to-image Diffusion Model in Generative AI: A Survey
This survey reviews text-to-image diffusion models in the context that d...
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08/11/2022
On the Pros and Cons of Momentum Encoder in Self-Supervised Visual Representation Learning
Exponential Moving Average (EMA or momentum) is widely used in modern se...
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07/30/2022
A Survey on Masked Autoencoder for Self-supervised Learning in Vision and Beyond
Masked autoencoders are scalable vision learners, as the title of MAE <c...
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07/22/2022
Decoupled Adversarial Contrastive Learning for Self-supervised Adversarial Robustness
Adversarial training (AT) for robust representation learning and self-su...
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03/30/2022
Investigating Top-k White-Box and Transferable Black-box Attack
Existing works have identified the limitation of top-1 attack success ra...
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03/30/2022
Dual Temperature Helps Contrastive Learning Without Many Negative Samples: Towards Understanding and Simplifying MoCo
Contrastive learning (CL) is widely known to require many negative sampl...
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03/30/2022
How Does SimSiam Avoid Collapse Without Negative Samples? A Unified Understanding with Self-supervised Contrastive Learning
To avoid collapse in self-supervised learning (SSL), a contrastive loss ...
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03/09/2022
Practical No-box Adversarial Attacks with Training-free Hybrid Image Transformation
In recent years, the adversarial vulnerability of deep neural networks (...
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02/11/2022
Noise Augmentation Is All You Need For FGSM Fast Adversarial Training: Catastrophic Overfitting And Robust Overfitting Require Different Augmentation
Adversarial training (AT) and its variants are the most effective approa...
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10/06/2021
Adversarial Robustness Comparison of Vision Transformer and MLP-Mixer to CNNs
Convolutional Neural Networks (CNNs) have become the de facto gold stand...
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04/19/2021
Restoration of Video Frames from a Single Blurred Image with Motion Understanding
We propose a novel framework to generate clean video frames from a singl...
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04/07/2021
Universal Adversarial Training with Class-Wise Perturbations
Despite their overwhelming success on a wide range of applications, conv...
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03/02/2021
A Brief Survey on Deep Learning Based Data Hiding, Steganography and Watermarking
Data hiding is the art of concealing messages with limited perceptual ch...
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03/02/2021
A Survey On Universal Adversarial Attack
Deep neural networks (DNNs) have demonstrated remarkable performance for...
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02/12/2021
Universal Adversarial Perturbations Through the Lens of Deep Steganography: Towards A Fourier Perspective
The booming interest in adversarial attacks stems from a misalignment be...
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12/30/2020
Towards Robust Data Hiding Against (JPEG) Compression: A Pseudo-Differentiable Deep Learning Approach
Data hiding is one widely used approach for protecting authentication an...
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10/26/2020
Robustness May Be at Odds with Fairness: An Empirical Study on Class-wise Accuracy
Recently, convolutional neural networks (CNNs) have made significant adv...
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10/23/2020
ResNet or DenseNet? Introducing Dense Shortcuts to ResNet
ResNet or DenseNet? Nowadays, most deep learning based approaches are im...
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10/07/2020
Revisiting Batch Normalization for Improving Corruption Robustness
Modern deep neural networks (DNN) have demonstrated remarkable success i...
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10/07/2020
Batch Normalization Increases Adversarial Vulnerability: Disentangling Usefulness and Robustness of Model Features
Batch normalization (BN) has been widely used in modern deep neural netw...
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10/07/2020
CD-UAP: Class Discriminative Universal Adversarial Perturbation
A single universal adversarial perturbation (UAP) can be added to all na...
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10/07/2020
Double Targeted Universal Adversarial Perturbations
Despite their impressive performance, deep neural networks (DNNs) are wi...
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07/13/2020
Data from Model: Extracting Data from Non-robust and Robust Models
The essence of deep learning is to exploit data to train a deep neural n...
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07/13/2020
