EAPFusion uses self-evolving intrinsic priors to produce dynamic, scene-adaptive convolution kernels and channel-mixing fusion for infrared-visible images, reporting state-of-the-art results and downstream gains.
Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
3 Pith papers cite this work. Polarity classification is still indexing.
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A multilevel perceptual CRF model using Swin Transformer, HPF fusion, HA adapters, and dynamic scaling attention achieves state-of-the-art monocular depth estimation on NYU Depth v2, KITTI, and MatterPort3D with reduced error and fast inference.
Invariant and equivariant semi-supervised learning improves multi-task detection and segmentation performance on partially labeled vision datasets compared to supervised baselines.
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EAPFusion: Intrinsic Evolving Auxiliary Prior Guidance for Infrared and Visible Image Fusion
EAPFusion uses self-evolving intrinsic priors to produce dynamic, scene-adaptive convolution kernels and channel-mixing fusion for infrared-visible images, reporting state-of-the-art results and downstream gains.