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Found 20 papers, 14 papers with code
DiffusionAct: Controllable Diffusion Autoencoder for One-shot Face Reenactment
To this end, in this paper we present DiffusionAct, a novel method that leverages the photo-realistic image generation of diffusion models to perform neural face reenactment.
Multilinear Mixture of Experts: Scalable Expert Specialization through Factorization
The Mixture of Experts (MoE) paradigm provides a powerful way to decompose inscrutable dense layers into smaller, modular computations often more amenable to human interpretation, debugging, and editability.
One-shot Neural Face Reenactment via Finding Directions in GAN's Latent Space
Moreover, we show that by embedding real images in the GAN latent space, our method can be successfully used for the reenactment of real-world faces.
DISYRE: Diffusion-Inspired SYnthetic REstoration for Unsupervised Anomaly Detection
Such a score function is potentially relevant for UAD, since $\nabla_x \log p(x)$ is itself a pixel-wise anomaly score.
Improving Fairness using Vision-Language Driven Image Augmentation
These paths are then applied to augment images to improve the fairness of a given dataset.
HyperReenact: One-Shot Reenactment via Jointly Learning to Refine and Retarget Faces
In this paper, we present our method for neural face reenactment, called HyperReenact, that aims to generate realistic talking head images of a source identity, driven by a target facial pose.
Parts of Speech-Grounded Subspaces in Vision-Language Models
Latent image representations arising from vision-language models have proved immensely useful for a variety of downstream tasks.
Self-Supervised Video Similarity Learning
We introduce S$^2$VS, a video similarity learning approach with self-supervision.
Ranked #1 on
Video Retrieval
on FIVR-200K
Attribute-preserving Face Dataset Anonymization via Latent Code Optimization
By optimizing the latent codes directly, we ensure both that the identity is of a desired distance away from the original (with an identity obfuscation loss), whilst preserving the facial attributes (using a novel feature-matching loss in FaRL's deep feature space).
StyleMask: Disentangling the Style Space of StyleGAN2 for Neural Face Reenactment
In this paper we address the problem of neural face reenactment, where, given a pair of a source and a target facial image, we need to transfer the target's pose (defined as the head pose and its facial expressions) to the source image, by preserving at the same time the source's identity characteristics (e. g., facial shape, hair style, etc), even in the challenging case where the source and the target faces belong to different identities.
ContraCLIP: Interpretable GAN generation driven by pairs of contrasting sentences
This work addresses the problem of discovering non-linear interpretable paths in the latent space of pre-trained GANs in a model-agnostic manner.
PandA: Unsupervised Learning of Parts and Appearances in the Feature Maps of GANs
Recent advances in the understanding of Generative Adversarial Networks (GANs) have led to remarkable progress in visual editing and synthesis tasks, capitalizing on the rich semantics that are embedded in the latent spaces of pre-trained GANs.
WarpedGANSpace: Finding non-linear RBF paths in GAN latent space
This work addresses the problem of discovering, in an unsupervised manner, interpretable paths in the latent space of pretrained GANs, so as to provide an intuitive and easy way of controlling the underlying generative factors.
DnS: Distill-and-Select for Efficient and Accurate Video Indexing and Retrieval
In this work, we propose a Knowledge Distillation framework, called Distill-and-Select (DnS), that starting from a well-performing fine-grained Teacher Network learns: a) Student Networks at different retrieval performance and computational efficiency trade-offs and b) a Selector Network that at test time rapidly directs samples to the appropriate student to maintain both high retrieval performance and high computational efficiency.
Ranked #2 on
Video Retrieval
on FIVR-200K
Few-Shot Action Localization without Knowing Boundaries
Learning to localize actions in long, cluttered, and untrimmed videos is a hard task, that in the literature has typically been addressed assuming the availability of large amounts of annotated training samples for each class -- either in a fully-supervised setting, where action boundaries are known, or in a weakly-supervised setting, where only class labels are known for each video.
Uncertainty Propagation in Convolutional Neural Networks: Technical Report
In this technical report we study the problem of propagation of uncertainty (in terms of variances of given uni-variate normal random variables) through typical building blocks of a Convolutional Neural Network (CNN).
Temporal Action Localization with Variance-Aware Networks
Results in the action localization problem show that the incorporation of second order statistics improves over the baseline network, and that VANp surpasses the accuracy of virtually all other two-stage networks without involving any additional parameters.
Boundary Uncertainty in a Single-Stage Temporal Action Localization Network
We use two uncertainty-aware boundary regression losses: first, the Kullback-Leibler divergence between the ground truth location of the boundary and the Gaussian modeling the prediction of the boundary and second, the expectation of the $\ell_1$ loss under the same Gaussian.
Learning to detect video events from zero or very few video examples
In this work we deal with the problem of high-level event detection in video.
Linear Maximum Margin Classifier for Learning from Uncertain Data
In this paper, we propose a maximum margin classifier that deals with uncertainty in data input.
