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Found 12 papers, 8 papers with code
Latent Guard: a Safety Framework for Text-to-image Generation
Hence, we propose Latent Guard, a framework designed to improve safety measures in text-to-image generation.
On Pretraining Data Diversity for Self-Supervised Learning
We explore the impact of training with more diverse datasets, characterized by the number of unique samples, on the performance of self-supervised learning (SSL) under a fixed computational budget.
SynthCLIP: Are We Ready for a Fully Synthetic CLIP Training?
We present SynthCLIP, a novel framework for training CLIP models with entirely synthetic text-image pairs, significantly departing from previous methods relying on real data.
Material Palette: Extraction of Materials from a Single Image
In this paper, we propose a method to extract physically-based rendering (PBR) materials from a single real-world image.
ManiFest: Manifold Deformation for Few-shot Image Translation
To enforce feature consistency, our framework learns a style manifold between source and proxy anchor domains (assumed to be composed of large numbers of images).
Leveraging Local Domains for Image-to-Image Translation
Our comprehensive evaluation setting shows we are able to generate realistic translations, with minimal priors, and training only on a few images.
Physics-informed Guided Disentanglement in Generative Networks
Image-to-image translation (i2i) networks suffer from entanglement effects in presence of physics-related phenomena in target domain (such as occlusions, fog, etc), lowering altogether the translation quality, controllability and variability.
CoMoGAN: continuous model-guided image-to-image translation
CoMoGAN is a continuous GAN relying on the unsupervised reorganization of the target data on a functional manifold.
Model-based occlusion disentanglement for image-to-image translation
Image-to-image translation is affected by entanglement phenomena, which may occur in case of target data encompassing occlusions such as raindrops, dirt, etc.
Domain Bridge for Unpaired Image-to-Image Translation and Unsupervised Domain Adaptation
Image-to-image translation architectures may have limited effectiveness in some circumstances.
Lane Detection and Classification using Cascaded CNNs
Lane detection is extremely important for autonomous vehicles.
Ranked #33 on
Lane Detection
on TuSimple
Enhanced free space detection in multiple lanes based on single CNN with scene identification
Traditional algorithms usually estimate only the position of the lanes on the road, but an autonomous control system may also need to know if a lane marking can be crossed or not, and what portion of space inside the lane is free from obstacles, to make safer control decisions.
