LESSViT introduces a low-rank efficient spatial-spectral attention mechanism and a hyperspectral masked autoencoder to improve generalization across spectral configuration shifts in hyperspectral imagery.
What do vision transformers learn? a visual exploration
4 Pith papers cite this work. Polarity classification is still indexing.
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Suppressing one refusal neuron or amplifying one concept neuron bypasses safety alignment in LLMs from 1.7B to 70B parameters without training or prompt engineering.
Realiz3D decouples visual domain from 3D controls in diffusion models via domain-aware residual adapters to enable photorealistic controllable generation.
Textual supervision enhances geospatial representations in vision-language models relative to vision-only models, shown via evaluations on image clusters of varying localizability.
citing papers explorer
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LESSViT: Robust Hyperspectral Representation Learning under Spectral Configuration Shift
LESSViT introduces a low-rank efficient spatial-spectral attention mechanism and a hyperspectral masked autoencoder to improve generalization across spectral configuration shifts in hyperspectral imagery.
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A Single Neuron Is Sufficient to Bypass Safety Alignment in Large Language Models
Suppressing one refusal neuron or amplifying one concept neuron bypasses safety alignment in LLMs from 1.7B to 70B parameters without training or prompt engineering.
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Realiz3D: 3D Generation Made Photorealistic via Domain-Aware Learning
Realiz3D decouples visual domain from 3D controls in diffusion models via domain-aware residual adapters to enable photorealistic controllable generation.
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Textual Supervision Enhances Geospatial Representations in Vision-Language Models
Textual supervision enhances geospatial representations in vision-language models relative to vision-only models, shown via evaluations on image clusters of varying localizability.