We observe that uniform sampling from diffusion models predominantly samples from high-density regions of the data manifold.
no code implementations • 8 Feb 2022 • Zoë Papakipos, Giorgos Tolias, Tomas Jenicek, Ed Pizzi, Shuhei Yokoo, Wenhao Wang, Yifan Sun, Weipu Zhang, Yi Yang, Sanjay Addicam, Sergio Manuel Papadakis, Cristian Canton Ferrer, Ondrej Chum, Matthijs Douze
The 2021 Image Similarity Challenge introduced a dataset to serve as a new benchmark to evaluate recent image copy detection methods.
Under this threat model, we create adversarial examples by perturbing only regions in the inputs where a classifier is uncertain.
1 code implementation • 17 Jun 2021 • Matthijs Douze, Giorgos Tolias, Ed Pizzi, Zoë Papakipos, Lowik Chanussot, Filip Radenovic, Tomas Jenicek, Maxim Maximov, Laura Leal-Taixé, Ismail Elezi, Ondřej Chum, Cristian Canton Ferrer
This benchmark is used for the Image Similarity Challenge at NeurIPS'21 (ISC2021).
Ranked #1 on Image Similarity Detection on DISC21 dev
The videos were recorded in multiple U. S. states with a diverse set of adults in various age, gender and apparent skin tone groups.
This work examines the vulnerability of multimodal (image + text) models to adversarial threats similar to those discussed in previous literature on unimodal (image- or text-only) models.
We perform our evaluations on the winning entries of the DeepFake Detection Challenge (DFDC) and demonstrate that they can be easily bypassed in a practical attack scenario by designing transferable and accessible adversarial attacks.
Online social networks provide a platform for sharing information and free expression.
In addition to Deepfakes, a variety of GAN-based face swapping methods have also been published with accompanying code.
Due to respectively limited training data, different entities addressing the same vision task based on certain sensitive images may not train a robust deep network.
In this paper, we introduce a preview of the Deepfakes Detection Challenge (DFDC) dataset consisting of 5K videos featuring two facial modification algorithms.
This work addresses the challenge of hate speech detection in Internet memes, and attempts using visual information to automatically detect hate speech, unlike any previous work of our knowledge.
This paper introduces a novel approach to in-painting where the identity of the object to remove or change is preserved and accounted for at inference time: Exemplar GANs (ExGANs).
This paper proposes a system for automatic social pattern characterization using a wearable photo-camera.
Following the increasingly popular trend of social interaction analysis in egocentric vision, this manuscript presents a comprehensive study for automatic social pattern characterization of a wearable photo-camera user, by relying on the visual analysis of egocentric photo-streams.
This paper introduces an unsupervised framework to extract semantically rich features for video representation.
We introduce SalGAN, a deep convolutional neural network for visual saliency prediction trained with adversarial examples.
Crowd sourcing has become a widely adopted scheme to collect ground truth labels.
This paper introduces a novel approach to the task of data association within the context of pedestrian tracking, by introducing a two-stage learning scheme to match pairs of detections.