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Found 8 papers, 4 papers with code
Dynamic Cross-Modal Feature Interaction Network for Hyperspectral and LiDAR Data Classification
To address these limitations, we propose a novel Dynamic Cross-Modal Feature Interaction Network (DCMNet), the first framework leveraging a dynamic routing mechanism for HSI and LiDAR classification.
Multi-Layer Visual Feature Fusion in Multimodal LLMs: Methods, Analysis, and Best Practices
Multimodal Large Language Models (MLLMs) have made significant advancements in recent years, with visual features playing an increasingly critical role in enhancing model performance.
Semantics Disentanglement and Composition for Versatile Codec toward both Human-eye Perception and Machine Vision Task
While learned image compression methods have achieved impressive results in either human visual perception or machine vision tasks, they are often specialized only for one domain.
To Preserve or To Compress: An In-Depth Study of Connector Selection in Multimodal Large Language Models
However, there is still considerable debate on constructing MLLM architectures, particularly regarding the selection of appropriate connectors for perception tasks of varying granularities.
Tell Codec What Worth Compressing: Semantically Disentangled Image Coding for Machine with LMMs
We present a new image compression paradigm to achieve ``intelligently coding for machine'' by cleverly leveraging the common sense of Large Multimodal Models (LMMs).
Sparse Focus Network for Multi-Source Remote Sensing Data Classification
Multi-source remote sensing data classification has emerged as a prominent research topic with the advancement of various sensors.
Boosting Spatial-Spectral Masked Auto-Encoder Through Mining Redundant Spectra for HSI-SAR/LiDAR Classification
Although recent masked image modeling (MIM)-based HSI-LiDAR/SAR classification methods have gradually recognized the importance of the spectral information, they have not adequately addressed the redundancy among different spectra, resulting in information leakage during the pretraining stage.
SS-MAE: Spatial-Spectral Masked Auto-Encoder for Multi-Source Remote Sensing Image Classification
Existing MIM-based methods mostly focus on spatial feature modeling, neglecting spectral feature modeling.
