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Found 4 papers, 2 papers with code
LEIA: Latent View-invariant Embeddings for Implicit 3D Articulation
Previous works have tackled this issue by focusing on part-level reconstruction and motion estimation for objects, but they often rely on heuristics regarding the number of moving parts or object categories, which can limit their practical use.
Do text-free diffusion models learn discriminative visual representations?
We find that the intermediate feature maps of the U-Net are diverse, discriminative feature representations.
Chop & Learn: Recognizing and Generating Object-State Compositions
Recognizing and generating object-state compositions has been a challenging task, especially when generalizing to unseen compositions.
Diffusion Models Beat GANs on Image Classification
We explore optimal methods for extracting and using these embeddings for classification tasks, demonstrating promising results on the ImageNet classification task.
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