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Found 14 papers, 7 papers with code
Self-Attention Graph Residual Convolutional Networks for Event Detection with dependency relations
While existing GCN-based methods explore latent node-to-node dependency relations according to a stationary adjacency tensor, an attention-based dynamic tensor, which can pay much attention to the key node like event trigger or its neighboring nodes, has not been developed.
CAdam: Confidence-Based Optimization for Online Learning
Modern recommendation systems frequently employ online learning to dynamically update their models with freshly collected data.
BooW-VTON: Boosting In-the-Wild Virtual Try-On via Mask-Free Pseudo Data Training
Recent methods model virtual try-on as image mask-inpaint task, which requires masking the person image and results in significant loss of spatial information.
Revealing Vulnerabilities in Stable Diffusion via Targeted Attacks
In this study, we formulate the problem of targeted adversarial attack on Stable Diffusion and propose a framework to generate adversarial prompts.
MV-CLIP: Multi-View CLIP for Zero-shot 3D Shape Recognition
Consequently, this paper aims to improve the confidence with view selection and hierarchical prompts.
CAT-DM: Controllable Accelerated Virtual Try-on with Diffusion Model
Generative Adversarial Networks (GANs) dominate the research field in image-based virtual try-on, but have not resolved problems such as unnatural deformation of garments and the blurry generation quality.
Improving Rumor Detection by Class-based Adversarial Domain Adaptation
This is not in line with the original intention of the rumor detection task.
Dynamic Causal Disentanglement Model for Dialogue Emotion Detection
Specifically, we propose a dynamic temporal disentanglement model to infer the propagation of utterances and hidden variables, enabling the accumulation of emotion-related information throughout the conversation.
Reinforcement Learning Based Multi-modal Feature Fusion Network for Novel Class Discovery
We employed a clustering method with varying constraint conditions, ranging from strict to loose, allowing for the generation of dependable labels for a subset of unlabeled data during the training phase.
Deep Reinforcement Learning Framework for Thoracic Diseases Classification via Prior Knowledge Guidance
The chest X-ray is often utilized for diagnosing common thoracic diseases.
Chest X-ray Image Classification: A Causal Perspective
The chest X-ray (CXR) is one of the most common and easy-to-get medical tests used to diagnose common diseases of the chest.
Instrumental Variable Learning for Chest X-ray Classification
The chest X-ray (CXR) is commonly employed to diagnose thoracic illnesses, but the challenge of achieving accurate automatic diagnosis through this method persists due to the complex relationship between pathology.
Disentangled Multimodal Representation Learning for Recommendation
However, existing methods ignore the fact that different modalities contribute differently towards a user's preference on various factors of an item.
TBNet:Two-Stream Boundary-aware Network for Generic Image Manipulation Localization
In this work, a novel end-to-end two-stream boundary-aware network (abbreviated as TBNet) is proposed for generic image manipulation localization in which the RGB stream, the frequency stream, and the boundary artifact location are explored in a unified framework.
