Search Results for author:
Found 7 papers, 5 papers with code
InstructLayout: Instruction-Driven 2D and 3D Layout Synthesis with Semantic Graph Prior
The proposed semantic graph prior learns layout appearances and object distributions simultaneously, demonstrating versatility across various downstream tasks in a zero-shot manner.
HumanSplat: Generalizable Single-Image Human Gaussian Splatting with Structure Priors
Despite recent advancements in high-fidelity human reconstruction techniques, the requirements for densely captured images or time-consuming per-instance optimization significantly hinder their applications in broader scenarios.
InstructScene: Instruction-Driven 3D Indoor Scene Synthesis with Semantic Graph Prior
We introduce InstructScene, a novel generative framework that integrates a semantic graph prior and a layout decoder to improve controllability and fidelity for 3D scene synthesis.
NuTime: Numerically Multi-Scaled Embedding for Large-Scale Time-Series Pretraining
In this work, we make key technical contributions that are tailored to the numerical properties of time-series data and allow the model to scale to large datasets, e. g., millions of temporal sequences.
Watermark Faker: Towards Forgery of Digital Image Watermarking
Digital watermarking has been widely used to protect the copyright and integrity of multimedia data.
A Brief Survey on Deep Learning Based Data Hiding
Data hiding is the art of concealing messages with limited perceptual changes.
A Survey On Universal Adversarial Attack
The intriguing phenomenon of adversarial examples has attracted significant attention in machine learning and what might be more surprising to the community is the existence of universal adversarial perturbations (UAPs), i. e. a single perturbation to fool the target DNN for most images.
