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Found 6 papers, 2 papers with code
Brain Mapping with Dense Features: Grounding Cortical Semantic Selectivity in Natural Images With Vision Transformers
To overcome the challenge presented by the co-occurrence of multiple categories in natural images, we introduce BrainSAIL (Semantic Attribution and Image Localization), a method for isolating specific neurally-activating visual concepts in images.
MaterialFusion: Enhancing Inverse Rendering with Material Diffusion Priors
This model is trained on albedo, material, and relit image data derived from a curated dataset of approximately ~12K artist-designed synthetic Blender objects called BlenderVault.
StableSemantics: A Synthetic Language-Vision Dataset of Semantic Representations in Naturalistic Images
To the best of our knowledge, we are the first to release a diffusion dataset with semantic attributions.
DiffusionPID: Interpreting Diffusion via Partial Information Decomposition
Text-to-image diffusion models have made significant progress in generating naturalistic images from textual inputs, and demonstrate the capacity to learn and represent complex visual-semantic relationships.
Detecting Anomalies using Generative Adversarial Networks on Images
Most of the available data in the anomaly detection task is imbalanced as the number of positive/anomalous instances is sparse.
Tuned Compositional Feature Replays for Efficient Stream Learning
To address the limited number of existing online stream learning datasets, we introduce 2 new benchmarks by adapting existing datasets for stream learning.
