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Found 32 papers, 9 papers with code
Towards Understanding Dual BN In Hybrid Adversarial Training
There is a growing concern about applying batch normalization (BN) in adversarial training (AT), especially when the model is trained on both adversarial samples and clean samples (termed Hybrid-AT).
BreakGPT: A Large Language Model with Multi-stage Structure for Financial Breakout Detection
To address these issues, we introduce BreakGPT, the first large language model for financial breakout detection.
Human Aesthetic Preference-Based Large Text-to-Image Model Personalization: Kandinsky Generation as an Example
This paper introduces a prompting-free generative approach that empowers users to automatically generate personalized painterly content that incorporates their aesthetic preferences in a customized artistic style.
Multi-modal vision-language model for generalizable annotation-free pathological lesions localization and clinical diagnosis
Defining pathologies automatically from medical images aids the understanding of the emergence and progression of diseases, and such an ability is crucial in clinical diagnostics.
The Contemporary Art of Image Search: Iterative User Intent Expansion via Vision-Language Model
Moreover, the intent expansion framework enables users to perform flexible contextualized interactions with the search results to further specify or adjust their detailed search intents iteratively.
DifAugGAN: A Practical Diffusion-style Data Augmentation for GAN-based Single Image Super-resolution
It is well known the adversarial optimization of GAN-based image super-resolution (SR) methods makes the preceding SR model generate unpleasant and undesirable artifacts, leading to large distortion.
Archiving Body Movements: Collective Generation of Chinese Calligraphy
As a communication channel, body movements have been widely explored in behavioral studies and kinesics.
ACDMSR: Accelerated Conditional Diffusion Models for Single Image Super-Resolution
To speed up inference and further enhance the performance, our research revisits diffusion models in image super-resolution and proposes a straightforward yet significant diffusion model-based super-resolution method called ACDMSR (accelerated conditional diffusion model for image super-resolution).
A Transformer-based representation-learning model with unified processing of multimodal input for clinical diagnostics
During the diagnostic process, clinicians leverage multimodal information, such as chief complaints, medical images, and laboratory-test results.
Learning from Multi-Perception Features for Real-Word Image Super-resolution
Currently, there are two popular approaches for addressing real-world image super-resolution problems: degradation-estimation-based and blind-based methods.
MIPI 2023 Challenge on RGB+ToF Depth Completion: Methods and Results
To evaluate the performance of different depth completion methods, we organized an RGB+sparse ToF depth completion competition.
Everyone Can Be Picasso? A Computational Framework into the Myth of Human versus AI Painting
The recent advances of AI technology, particularly in AI-Generated Content (AIGC), have enabled everyone to easily generate beautiful paintings with simple text description.
CDPMSR: Conditional Diffusion Probabilistic Models for Single Image Super-Resolution
Diffusion probabilistic models (DPM) have been widely adopted in image-to-image translation to generate high-quality images.
Semi-Supervised Video Inpainting with Cycle Consistency Constraints
Specifically, in this work, we propose an end-to-end trainable framework consisting of completion network and mask prediction network, which are designed to generate corrupted contents of the current frame using the known mask and decide the regions to be filled of the next frame, respectively.
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 self-supervised learning (SSL) approaches, such as MoCo, for enhancing performance.
A Survey on Masked Autoencoder for Self-supervised Learning in Vision and Beyond
Masked autoencoders are scalable vision learners, as the title of MAE \cite{he2022masked}, which suggests that self-supervised learning (SSL) in vision might undertake a similar trajectory as in NLP.
Decoupled Adversarial Contrastive Learning for Self-supervised Adversarial Robustness
Adversarial training (AT) for robust representation learning and self-supervised learning (SSL) for unsupervised representation learning are two active research fields.
Understanding and Improving Group Normalization
Various normalization layers have been proposed to help the training of neural networks.
Dual Temperature Helps Contrastive Learning Without Many Negative Samples: Towards Understanding and Simplifying MoCo
Contrastive learning (CL) is widely known to require many negative samples, 65536 in MoCo for instance, for which the performance of a dictionary-free framework is often inferior because the negative sample size (NSS) is limited by its mini-batch size (MBS).
Investigating Top-$k$ White-Box and Transferable Black-box Attack
It is widely reported that stronger I-FGSM transfers worse than simple FGSM, leading to a popular belief that transferability is at odds with the white-box attack strength.
How Does SimSiam Avoid Collapse Without Negative Samples? A Unified Understanding with Self-supervised Contrastive Learning
This yields a unified perspective on how negative samples and SimSiam alleviate collapse.
Fast Adversarial Training with Noise Augmentation: A Unified Perspective on RandStart and GradAlign
The former works only for a relatively small perturbation 8/255 with the l_\infty constraint, and GradAlign improves it by extending the perturbation size to 16/255 (with the l_\infty constraint) but at the cost of being 3 to 4 times slower.
Investigating Top-k White-Box and Transferable Black-Box Attack
It is widely reported that stronger I-FGSM transfers worse than simple FGSM, leading to a popular belief that transferability is at odds with the white-box attack strength.
How Does SimSiam Avoid Collapse Without Negative Samples? Towards a Unified Understanding of Progress in SSL
Towards avoiding collapse in self-supervised learning (SSL), contrastive loss is widely used but often requires a large number of negative samples.
Early Stop And Adversarial Training Yield Better surrogate Model: Very Non-Robust Features Harm Adversarial Transferability
The transferability of adversarial examples (AE); known as adversarial transferability, has attracted significant attention because it can be exploited for TransferableBlack-box Attacks (TBA).
Query Rewriting via Cycle-Consistent Translation for E-Commerce Search
Online A/B experiments show that it improves core e-commerce business metrics significantly.
Towards Personalized and Semantic Retrieval: An End-to-End Solution for E-commerce Search via Embedding Learning
Two critical challenges stay in today's e-commerce search: how to retrieve items that are semantically relevant but not exact matching to query terms, and how to retrieve items that are more personalized to different users for the same search query.
A Computational Model of Afterimages based on Simultaneous and Successive Contrasts
The colors of negative afterimages differ from the old stimulating colors in the original image when the color in the new area is either neutral or chromatic.
3D Fragment Reassembly Using Integrated Template Guidance and Fracture-Region Matching
This paper studies matching of fragmented objects to recompose their original geometry.
Bayesian regression and Bitcoin
In this paper, we discuss the method of Bayesian regression and its efficacy for predicting price variation of Bitcoin, a recently popularized virtual, cryptographic currency.
Cross-Scale Cost Aggregation for Stereo Matching
We firstly reformulate cost aggregation from a unified optimization perspective and show that different cost aggregation methods essentially differ in the choices of similarity kernels.
Binary Stereo Matching
In this paper, we propose a novel binary-based cost computation and aggregation approach for stereo matching problem.
