Trust-SSL introduces additive-residual trust weights in SSL to selectively handle corruptions in aerial imagery, yielding higher linear-probe accuracy and larger gains under severe degradations than SimCLR or VICReg.
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Repurposing discarded supervised classification heads from vision models as semantic prototypes boosts post-hoc vision-language alignment methods on retrieval and zero/few-shot classification.
citing papers explorer
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Trust-SSL: Additive-Residual Selective Invariance for Robust Aerial Self-Supervised Learning
Trust-SSL introduces additive-residual trust weights in SSL to selectively handle corruptions in aerial imagery, yielding higher linear-probe accuracy and larger gains under severe degradations than SimCLR or VICReg.
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Supervised Classification Heads as Semantic Prototypes: Unlocking Vision-Language Alignment via Weight Recycling
Repurposing discarded supervised classification heads from vision models as semantic prototypes boosts post-hoc vision-language alignment methods on retrieval and zero/few-shot classification.
- Vision Transformers and Convolutional Neural Networks for Land Use Scene Classification