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Found 9 papers, 3 papers with code
Vision-Informed Flow Image Super-Resolution with Quaternion Spatial Modeling and Dynamic Flow Convolution
To tackle this dilemma, we comprehensively consider the flow visual properties, including the unique flow imaging principle and morphological information, and propose the first flow visual property-informed FISR algorithm.
Domain Prompt Learning with Quaternion Networks
Specifically, the proposed method involves using domain-specific vision features from domain-specific foundation models to guide the transformation of generalized contextual embeddings from the language branch into a specialized space within the quaternion networks.
CLeaRForecast: Contrastive Learning of High-Purity Representations for Time Series Forecasting
Time series forecasting (TSF) holds significant importance in modern society, spanning numerous domains.
MoE-AMC: Enhancing Automatic Modulation Classification Performance Using Mixture-of-Experts
Utilizing the MoE framework, MoE-AMC seamlessly combines the strengths of LSRM (a Transformer-based model) for handling low SNR signals and HSRM (a ResNet-based model) for high SNR signals.
Domain-Controlled Prompt Learning
Existing prompt learning methods often lack domain-awareness or domain-transfer mechanisms, leading to suboptimal performance due to the misinterpretation of specific images in natural image patterns.
Reflection Invariance Learning for Few-shot Semantic Segmentation
Few-shot semantic segmentation (FSS) aims to segment objects of unseen classes in query images with only a few annotated support images.
Few-Shot Rotation-Invariant Aerial Image Semantic Segmentation
Few-shot aerial image segmentation is a challenging task that involves precisely parsing objects in query aerial images with limited annotated support.
Progressively Dual Prior Guided Few-shot Semantic Segmentation
Few-shot semantic segmentation task aims at performing segmentation in query images with a few annotated support samples.
Learning Non-target Knowledge for Few-shot Semantic Segmentation
Then, a BG Eliminating Module and a DO Eliminating Module are proposed to successively filter out the BG and DO information from the query feature, based on which we can obtain a BG and DO-free target object segmentation result.
