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Found 13 papers, 7 papers with code
Continuous, Subject-Specific Attribute Control in T2I Models by Identifying Semantic Directions
We demonstrate that these directions can be used to augment the prompt text input with fine-grained control over attributes of specific subjects in a compositional manner (control over multiple attributes of a single subject) without having to adapt the diffusion model.
ZigMa: A DiT-style Zigzag Mamba Diffusion Model
The diffusion model has long been plagued by scalability and quadratic complexity issues, especially within transformer-based structures.
DepthFM: Fast Monocular Depth Estimation with Flow Matching
Due to the generative nature of our approach, our model reliably predicts the confidence of its depth estimates.
Training Class-Imbalanced Diffusion Model Via Overlap Optimization
To address the observed appearance overlap between synthesized images of rare classes and tail classes, we propose a method based on contrastive learning to minimize the overlap between distributions of synthetic images for different classes.
Latent Space Editing in Transformer-Based Flow Matching
Flow Matching is an emerging generative modeling technique that offers the advantage of simple and efficient training.
Motion Flow Matching for Human Motion Synthesis and Editing
In this paper, we propose \emph{Motion Flow Matching}, a novel generative model designed for human motion generation featuring efficient sampling and effectiveness in motion editing applications.
Guided Diffusion from Self-Supervised Diffusion Features
However, recent studies have revealed that the feature representation derived from diffusion model itself is discriminative for numerous downstream tasks as well, which prompts us to propose a framework to extract guidance from, and specifically for, diffusion models.
ScribbleGen: Generative Data Augmentation Improves Scribble-supervised Semantic Segmentation
We propose ScribbleGen, a generative data augmentation method that leverages a ControlNet diffusion model conditioned on semantic scribbles to produce high-quality training data.
ToddlerDiffusion: Flash Interpretable Controllable Diffusion Model
Diffusion-based generative models excel in perceptually impressive synthesis but face challenges in interpretability.
Self-Guided Diffusion Models
Diffusion models have demonstrated remarkable progress in image generation quality, especially when guidance is used to control the generative process.
Self-supervised Video Representation Learning with Cross-Stream Prototypical Contrasting
Instance-level contrastive learning techniques, which rely on data augmentation and a contrastive loss function, have found great success in the domain of visual representation learning.
Ranked #3 on
Self-supervised Video Retrieval
on HMDB51
PointMixup: Augmentation for Point Clouds
In this paper, we define data augmentation between point clouds as a shortest path linear interpolation.
Ranked #3 on
3D Point Cloud Data Augmentation
on ModelNet40
3D Point Cloud Classification
3D Point Cloud Data Augmentation
+2
Localizing the Common Action Among a Few Videos
The start and end of an action in a long untrimmed video is determined based on just a hand-full of trimmed video examples containing the same action, without knowing their common class label.
