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Found 20 papers, 7 papers with code
On the Content Bias in Fréchet Video Distance
We show that FVD with features extracted from the recent large-scale self-supervised video models is less biased toward image quality.
Grounded Text-to-Image Synthesis with Attention Refocusing
Driven by the scalable diffusion models trained on large-scale datasets, text-to-image synthesis methods have shown compelling results.
Preserve Your Own Correlation: A Noise Prior for Video Diffusion Models
Despite tremendous progress in generating high-quality images using diffusion models, synthesizing a sequence of animated frames that are both photorealistic and temporally coherent is still in its infancy.
Ranked #8 on
Text-to-Video Generation
on UCF-101
Expressive Text-to-Image Generation with Rich Text
For each region, we enforce its text attributes by creating region-specific detailed prompts and applying region-specific guidance, and maintain its fidelity against plain-text generation through region-based injections.
Text-driven Visual Synthesis with Latent Diffusion Prior
There has been tremendous progress in large-scale text-to-image synthesis driven by diffusion models enabling versatile downstream applications such as 3D object synthesis from texts, image editing, and customized generation.
Hyperbolic Contrastive Learning for Visual Representations beyond Objects
To exploit such a structure, we propose a contrastive learning framework where a Euclidean loss is used to learn object representations and a hyperbolic loss is used to encourage representations of scenes to lie close to representations of their constituent objects in a hyperbolic space.
Long Video Generation with Time-Agnostic VQGAN and Time-Sensitive Transformer
Videos are created to express emotion, exchange information, and share experiences.
Ranked #15 on
Video Generation
on UCF-101
Robust Contrastive Learning Using Negative Samples with Diminished Semantics
We also show that model bias favors texture and shape features differently under different test settings.
Visual Conceptual Blending with Large-scale Language and Vision Models
We ask the question: to what extent can recent large-scale language and image generation models blend visual concepts?
Shift Invariance Can Reduce Adversarial Robustness
Using this, we prove that shift invariance in neural networks produces adversarial examples for the simple case of two classes, each consisting of a single image with a black or white dot on a gray background.
Creative Sketch Generation
Sketching or doodling is a popular creative activity that people engage in.
Smooth Kernels Improve Adversarial Robustness and Perceptually-Aligned Gradients
Recent research has shown that CNNs are often overly sensitive to high-frequency textural patterns.
Learned Interpolation for 3D Generation
The typical approach for incorporating this creative process is to interpolate in a learned latent space so as to avoid the problem of generating unrealistic instances by exploiting the model's learned structure.
Getting Topology and Point Cloud Generation to Mesh
In this work, we explore the idea that effective generative models for point clouds under the autoencoding framework must acknowledge the relationship between a continuous surface, a discretized mesh, and a set of points sampled from the surface.
Personalizing Search Results Using Hierarchical RNN with Query-aware Attention
Our analysis reveals that the attention model is able to attribute higher weights to more related past sessions after fine training.
From Text to Sound: A Preliminary Study on Retrieving Sound Effects to Radio Stories
In this paper, we address the problem of automatically adding sound effects to radio stories with a retrieval-based model.
Developing Creative AI to Generate Sculptural Objects
We explore the intersection of human and machine creativity by generating sculptural objects through machine learning.
Learning Robust Global Representations by Penalizing Local Predictive Power
Despite their renowned predictive power on i. i. d.
Ranked #104 on
Domain Generalization
on PACS
Hallucinating Point Cloud into 3D Sculptural Object
Our approach extends DeepDream from images to 3D point clouds.
