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Found 9 papers, 6 papers with code
DiffusionFace: Towards a Comprehensive Dataset for Diffusion-Based Face Forgery Analysis
The rapid progress in deep learning has given rise to hyper-realistic facial forgery methods, leading to concerns related to misinformation and security risks.
HODN: Disentangling Human-Object Feature for HOI Detection
However, these methods ignore the relationship among humans, objects, and interactions: 1) human features are more contributive than object ones to interaction prediction; 2) interactive information disturbs the detection of objects but helps human detection.
Improving Human-Object Interaction Detection via Virtual Image Learning
Human-Object Interaction (HOI) detection aims to understand the interactions between humans and objects, which plays a curtail role in high-level semantic understanding tasks.
CamoDiffusion: Camouflaged Object Detection via Conditional Diffusion Models
Due to the stochastic sampling process of diffusion, our model is capable of sampling multiple possible predictions from the mask distribution, avoiding the problem of overconfident point estimation.
Latent Feature Relation Consistency for Adversarial Robustness
Deep neural networks have been applied in many computer vision tasks and achieved state-of-the-art performance.
CAT:Collaborative Adversarial Training
By revisiting the previous methods, we find different adversarial training methods have distinct robustness for sample instances.
Exploring Invariant Representation for Visible-Infrared Person Re-Identification
Cross-spectral person re-identification, which aims to associate identities to pedestrians across different spectra, faces a main challenge of the modality discrepancy.
Exploring Target Representations for Masked Autoencoders
Masked autoencoders have become popular training paradigms for self-supervised visual representation learning.
Learning to Learn Transferable Attack
By treating the attack of both specific data and a modified model as a task, we expect the adversarial perturbations to adopt enough tasks for generalization.
