Search Results for author: Bjorn Ommer

Found 12 papers, 1 papers with code

Diffusion Models and Representation Learning: A Survey

1 code implementation30 Jun 2024 Michael Fuest, Pingchuan Ma, Ming Gui, Johannes S. Fischer, Vincent Tao Hu, Bjorn Ommer

Diffusion Models are popular generative modeling methods in various vision tasks, attracting significant attention.

Denoising Representation Learning +1

Motion Flow Matching for Human Motion Synthesis and Editing

no code implementations14 Dec 2023 Vincent Tao Hu, Wenzhe Yin, Pingchuan Ma, Yunlu Chen, Basura Fernando, Yuki M Asano, Efstratios Gavves, Pascal Mettes, Bjorn Ommer, Cees G. M. Snoek

In this paper, we propose \emph{Motion Flow Matching}, a novel generative model designed for human motion generation featuring efficient sampling and effectiveness in motion editing applications.

Motion Interpolation motion prediction +1

Guided Diffusion from Self-Supervised Diffusion Features

no code implementations14 Dec 2023 Vincent Tao Hu, Yunlu Chen, Mathilde Caron, Yuki M. Asano, Cees G. M. Snoek, Bjorn Ommer

However, recent studies have revealed that the feature representation derived from diffusion model itself is discriminative for numerous downstream tasks as well, which prompts us to propose a framework to extract guidance from, and specifically for, diffusion models.

Self-Supervised Learning

Shape or Texture: Understanding Discriminative Features in CNNs

no code implementations27 Jan 2021 Md Amirul Islam, Matthew Kowal, Patrick Esser, Sen Jia, Bjorn Ommer, Konstantinos G. Derpanis, Neil Bruce

Contrasting the previous evidence that neurons in the later layers of a Convolutional Neural Network (CNN) respond to complex object shapes, recent studies have shown that CNNs actually exhibit a `texture bias': given an image with both texture and shape cues (e. g., a stylized image), a CNN is biased towards predicting the category corresponding to the texture.

Deep Semantic Feature Matching

no code implementations CVPR 2017 Nikolai Ufer, Bjorn Ommer

We introduce a novel method for semantic matching with pre-trained CNN features which is based on convolutional feature pyramids and activation guided feature selection.

feature selection Graph Matching

Per-Sample Kernel Adaptation for Visual Recognition and Grouping

no code implementations ICCV 2015 Borislav Antic, Bjorn Ommer

Nevertheless, the common approach to this problem in feature selection and kernel methods is to down-weight or eliminate entire training samples or the same dimensions of all samples.

Action Recognition In Videos feature selection +4

Visual Recognition using Embedded Feature Selection for Curvature Self-Similarity

no code implementations NeurIPS 2012 Angela Eigenstetter, Bjorn Ommer

This demand has led to feature descriptors of ever increasing dimensionality like co-occurrence statistics and self-similarity.

feature selection Object +2

Cannot find the paper you are looking for? You can Submit a new open access paper.