Different from prior studies, we shift our focus from evaluating standard performance to introducing a comprehensive safety evaluation suite, covering both out-of-distribution (OOD) generalization and adversarial robustness.
Our work highlights the potential of LLMs in 3D modeling, offering a basic framework for future advancements in scene generation and animation.
Audio-visual zero-shot learning aims to classify samples consisting of a pair of corresponding audio and video sequences from classes that are not present during training.
Ranked #2 on GZSL Video Classification on ActivityNet-GZSL(main)
Specifically, we use a NeRF model to generate numerous image-angle pairs to train an adjustor, which can adjust the StyleGAN latent code to generate high-fidelity stylized images for any given angle.
This paper studies the problem of measuring and predicting how memorable an image is to pattern recognition machines, as a path to explore machine intelligence.
Learning a latent embedding to understand the underlying nature of data distribution is often formulated in Euclidean spaces with zero curvature.
We present a simple method, CropMix, for the purpose of producing a rich input distribution from the original dataset distribution.
In this paper, we present and study a new image segmentation task, called Generalized Open-set Semantic Segmentation (GOSS).
We present You Only Cut Once (YOCO) for performing data augmentations.
We propose and study a novel task named Blind Image Decomposition (BID), which requires separating a superimposed image into constituent underlying images in a blind setting, that is, both the source components involved in mixing as well as the mixing mechanism are unknown.
There are 2000 reference restored images and 6003 original underwater images in the unpaired training set.
Unsupervised image-to-image translation tasks aim to find a mapping between a source domain X and a target domain Y from unpaired training data.
Underwater image restoration attracts significant attention due to its importance in unveiling the underwater world.