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Found 7 papers, 2 papers with code
Spectrum-guided Feature Enhancement Network for Event Person Re-Identification
This network consists of two innovative components: the Multi-grain Spectrum Attention Mechanism (MSAM) and the Consecutive Patch Dropout Module (CPDM).
Fine-grained Text and Image Guided Point Cloud Completion with CLIP Model
In this work, we propose a novel multimodal fusion network for point cloud completion, which can simultaneously fuse visual and textual information to predict the semantic and geometric characteristics of incomplete shapes effectively.
ALR-GAN: Adaptive Layout Refinement for Text-to-Image Synthesis
The ALR-GAN includes an Adaptive Layout Refinement (ALR) module and a Layout Visual Refinement (LVR) loss.
DR-GAN: Distribution Regularization for Text-to-Image Generation
This paper presents a new Text-to-Image generation model, named Distribution Regularization Generative Adversarial Network (DR-GAN), to generate images from text descriptions from improved distribution learning.
Incomplete Descriptor Mining with Elastic Loss for Person Re-Identification
The CBDB-Net contains two novel designs: the Consecutive Batch DropBlock Module (CBDBM) and the Elastic Loss (EL).
MHSA-Net: Multi-Head Self-Attention Network for Occluded Person Re-Identification
This paper presents a novel person re-identification model, named Multi-Head Self-Attention Network (MHSA-Net), to prune unimportant information and capture key local information from person images.
Semantics-Enhanced Adversarial Nets for Text-to-Image Synthesis
This paper presents a new model, Semantics-enhanced Generative Adversarial Network (SEGAN), for fine-grained text-to-image generation.
