Search Results for author:
Found 23 papers, 8 papers with code
Infinite-ID: Identity-preserved Personalization via ID-semantics Decoupling Paradigm
Drawing on recent advancements in diffusion models for text-to-image generation, identity-preserved personalization has made significant progress in accurately capturing specific identities with just a single reference image.
Closed-Loop Unsupervised Representation Disentanglement with $β$-VAE Distillation and Diffusion Probabilistic Feedback
Representation disentanglement may help AI fundamentally understand the real world and thus benefit both discrimination and generation tasks.
Diffusion model for relational inference
Dynamical behaviors of complex interacting systems, including brain activities, financial price movements, and physical collective phenomena, are associated with underlying interactions between the system's components.
Two-stream joint matching method based on contrastive learning for few-shot action recognition
Although few-shot action recognition based on metric learning paradigm has achieved significant success, it fails to address the following issues: (1) inadequate action relation modeling and underutilization of multi-modal information; (2) challenges in handling video matching problems with different lengths and speeds, and video matching problems with misalignment of video sub-actions.
Efficient Trigger Word Insertion
With the boom in the natural language processing (NLP) field these years, backdoor attacks pose immense threats against deep neural network models.
Peer is Your Pillar: A Data-unbalanced Conditional GANs for Few-shot Image Generation
Few-shot image generation aims to train generative models using a small number of training images.
Explore the Effect of Data Selection on Poison Efficiency in Backdoor Attacks
In this study, we focus on improving the poisoning efficiency of backdoor attacks from the sample selection perspective.
Dynamical Graph Echo State Networks with Snapshot Merging for Dissemination Process Classification
The Dissemination Process Classification (DPC) is a popular application of temporal graph classification.
Efficient Backdoor Attacks for Deep Neural Networks in Real-world Scenarios
However, existing backdoor attack methods make unrealistic assumptions, assuming that all training data comes from a single source and that attackers have full access to the training data.
A Proxy-Free Strategy for Practically Improving the Poisoning Efficiency in Backdoor Attacks
Poisoning efficiency is a crucial factor in poisoning-based backdoor attacks.
Multimodal Feature Extraction and Fusion for Emotional Reaction Intensity Estimation and Expression Classification in Videos with Transformers
In this paper, we present our advanced solutions to the two sub-challenges of Affective Behavior Analysis in the wild (ABAW) 2023: the Emotional Reaction Intensity (ERI) Estimation Challenge and Expression (Expr) Classification Challenge.
FakeCLR: Exploring Contrastive Learning for Solving Latent Discontinuity in Data-Efficient GANs
Since data augmentation strategies have largely alleviated the training instability, how to further improve the generative performance of DE-GANs becomes a hotspot.
Forcing the Whole Video as Background: An Adversarial Learning Strategy for Weakly Temporal Action Localization
With video-level labels, weakly supervised temporal action localization (WTAL) applies a localization-by-classification paradigm to detect and classify the action in untrimmed videos.
Classification
Weakly-supervised Temporal Action Localization
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Data-Efficient Backdoor Attacks
Recent studies have proven that deep neural networks are vulnerable to backdoor attacks.
A Comprehensive Survey on Data-Efficient GANs in Image Generation
Generative Adversarial Networks (GANs) have achieved remarkable achievements in image synthesis.
Tightening the Approximation Error of Adversarial Risk with Auto Loss Function Search
The most common solution for this is to compute an approximate risk by replacing the 0-1 loss with a surrogate one.
Enhancing Backdoor Attacks with Multi-Level MMD Regularization
Then, ML-MMDR, a difference reduction method that adds multi-level MMD regularization into the loss, is proposed, and its effectiveness is testified on three typical difference-based defense methods.
Exploring The Effect of High-frequency Components in GANs Training
Generative Adversarial Networks (GANs) have the ability to generate images that are visually indistinguishable from real images.
A New Perspective on Stabilizing GANs training: Direct Adversarial Training
Quite a number of methods have been proposed to stabilize the training of GANs, the focuses of which were respectively put on the loss functions, regularization and normalization technologies, training algorithms, and model architectures.
A Systematic Survey of Regularization and Normalization in GANs
Although a handful number of regularization and normalization methods have been proposed for GANs, to the best of our knowledge, there exists no comprehensive survey that primarily focuses on objectives and development of these methods, apart from some in-comprehensive and limited scope studies.
PGD-UNet: A Position-Guided Deformable Network for Simultaneous Segmentation of Organs and Tumors
Position information is explicitly encoded into the network to enhance the capabilities of deformation.
Interpreting the Latent Space of GANs via Correlation Analysis for Controllable Concept Manipulation
Generative adversarial nets (GANs) have been successfully applied in many fields like image generation, inpainting, super-resolution and drug discovery, etc., by now, the inner process of GANs is far from been understood.
DA-RefineNet:A Dual Input Whole Slide Image Segmentation Algorithm Based on Attention
Automatic medical image segmentation has wide applications for disease diagnosing.
