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Found 6 papers, 2 papers with code
Leveraging Adaptive Implicit Representation Mapping for Ultra High-Resolution Image Segmentation
To address these limitations, we propose a novel approach that leverages the newly proposed Adaptive Implicit Representation Mapping (AIRM) for ultra-high-resolution Image Segmentation.
Bidirectional Autoregressive Diffusion Model for Dance Generation
To make the generated dance motion smoother, a local information decoder is built for local motion enhancement.
Bidirectional Autoregessive Diffusion Model for Dance Generation
To make the generated dance motion smoother a local information decoder is built for local motion enhancement.
SAIR: Learning Semantic-aware Implicit Representation
To this end, we propose a framework with two modules: (1) building a semantic implicit representation (SIR) for a corrupted image whose large regions miss.
SuperInpaint: Learning Detail-Enhanced Attentional Implicit Representation for Super-resolutional Image Inpainting
Given the coordinates of a pixel we want to reconstruct, we first collect its neighboring pixels in the input image and extract their detail-enhanced semantic embeddings, unmask-attentional semantic embeddings, importance values, and spatial distances to the desired pixel.
Few-Shot 3D Point Cloud Semantic Segmentation via Stratified Class-Specific Attention Based Transformer Network
While a few-shot learning method was proposed recently to address these two problems, it suffers from high computational complexity caused by graph construction and inability to learn fine-grained relationships among points due to the use of pooling operations.
Few-shot 3D Point Cloud Semantic Segmentation
Few-Shot Learning
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