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Found 7 papers, 4 papers with code
Customizing Motion in Text-to-Video Diffusion Models
To avoid overfitting to the new custom motion, we introduce an approach for regularization over videos.
Concept Sliders: LoRA Adaptors for Precise Control in Diffusion Models
We present a method to create interpretable concept sliders that enable precise control over attributes in image generations from diffusion models.
FIND: A Function Description Benchmark for Evaluating Interpretability Methods
FIND contains functions that resemble components of trained neural networks, and accompanying descriptions of the kind we seek to generate.
Erasing Concepts from Diffusion Models
We propose a fine-tuning method that can erase a visual concept from a pre-trained diffusion model, given only the name of the style and using negative guidance as a teacher.
Disentangling visual and written concepts in CLIP
The CLIP network measures the similarity between natural text and images; in this work, we investigate the entanglement of the representation of word images and natural images in its image encoder.
Something-Else: Compositional Action Recognition with Spatial-Temporal Interaction Networks
Human action is naturally compositional: humans can easily recognize and perform actions with objects that are different from those used in training demonstrations.
The SYNTHIA Dataset: A Large Collection of Synthetic Images for Semantic Segmentation of Urban Scenes
In order to answer this question we have generated a synthetic collection of diverse urban images, named SYNTHIA, with automatically generated class annotations.
