Search Results for author:
Found 14 papers, 4 papers with code
CAT-DM: Controllable Accelerated Virtual Try-on with Diffusion Model
Image-based virtual try-on enables users to virtually try on different garments by altering original clothes in their photographs.
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.
Image-Based Virtual Try-On: A Survey
In this survey, we provide a comprehensive analysis of the state-of-the-art techniques and methodologies in aspects of pipeline architecture, person representation and key modules such as try-on indication, clothing warping and try-on stage.
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.
Causal Disentanglement Hidden Markov Model for Fault Diagnosis
In modern industries, fault diagnosis has been widely applied with the goal of realizing predictive maintenance.
Temporal-spatial Correlation Attention Network for Clinical Data Analysis in Intensive Care Unit
Our method can also find key clinical indicators of important outcomes that can be used to improve treatment options.
Deep Reinforcement Learning Framework for Thoracic Diseases Classification via Prior Knowledge Guidance
The chest X-ray is often utilized for diagnosing common thoracic diseases.
Point Cloud Completion Guided by Prior Knowledge via Causal Inference
In this paper, we propose a novel approach to point cloud completion task called Point-PC, which uses a memory network to retrieve shape priors and designs a causal inference model to filter missing shape information as supplemental geometric information to aid point cloud completion.
T2TD: Text-3D Generation Model based on Prior Knowledge Guidance
Meanwhile, to effectively integrate multi-modal prior knowledge into textual information, we adopt a novel multi-layer transformer structure to progressively fuse related shape and textual information, which can effectively compensate for the lack of structural information in the text and enhance the final performance of the 3D generation model.
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.
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.
3D Shape Knowledge Graph for Cross-domain 3D Shape Retrieval
To establish the knowledge graph, we employ geometric words as nodes, connecting them via shape categories and geometry attributes.
Multigranular Visual-Semantic Embedding for Cloth-Changing Person Re-identification
To solve these issues, in this work, a novel multigranular visual-semantic embedding algorithm (MVSE) is proposed for cloth-changing person ReID, where visual semantic information and human attributes are embedded into the network, and the generalized features of human appearance can be well learned to effectively solve the problem of clothing changes.
