Search Results for author:
Found 58 papers, 22 papers with code
CoTracker: It is Better to Track Together
In this paper, we thus propose CoTracker, an architecture that jointly tracks multiple points throughout an entire video.
PoseDiffusion: Solving Pose Estimation via Diffusion-aided Bundle Adjustment
Camera pose estimation is a long-standing computer vision problem that to date often relies on classical methods, such as handcrafted keypoint matching, RANSAC and bundle adjustment.
Diffusion Models for Zero-Shot Open-Vocabulary Segmentation
This provides a distribution of appearances for a given text circumventing the ambiguity problem.
Viewset Diffusion: (0-)Image-Conditioned 3D Generative Models from 2D Data
We fit a diffusion model to a large number of viewsets for a given category of objects.
DynamicStereo: Consistent Dynamic Depth from Stereo Videos
The network learns to pool information from neighboring frames to improve the temporal consistency of its predictions.
Farm3D: Learning Articulated 3D Animals by Distilling 2D Diffusion
We propose a framework using an image generator like Stable Diffusion to generate virtual training data for learning such a reconstruction network from scratch.
What does CLIP know about a red circle? Visual prompt engineering for VLMs
Large-scale Vision-Language Models, such as CLIP, learn powerful image-text representations that have found numerous applications, from zero-shot classification to text-to-image generation.
Continual Detection Transformer for Incremental Object Detection
Incremental object detection (IOD) aims to train an object detector in phases, each with annotations for new object categories.
Temperature Schedules for Self-Supervised Contrastive Methods on Long-Tail Data
Such a schedule results in a constant `task switching' between an emphasis on instance discrimination and group-wise discrimination and thereby ensures that the model learns both group-wise features, as well as instance-specific details.
$PC^2$: Projection-Conditioned Point Cloud Diffusion for Single-Image 3D Reconstruction
Reconstructing the 3D shape of an object from a single RGB image is a long-standing and highly challenging problem in computer vision.
RealFusion: 360° Reconstruction of Any Object from a Single Image
We consider the problem of reconstructing a full 360{\deg} photographic model of an object from a single image of it.
Behind the Scenes: Density Fields for Single View Reconstruction
By directly sampling color from the available views instead of storing color in the density field, our scene representation becomes significantly less complex compared to NeRFs, and a neural network can predict it in a single forward pass.
PC2: Projection-Conditioned Point Cloud Diffusion for Single-Image 3D Reconstruction
Reconstructing the 3D shape of an object from a single RGB image is a long-standing problem in computer vision.
RealFusion: 360deg Reconstruction of Any Object From a Single Image
We consider the problem of reconstructing a full 360deg photographic model of an object from a single image of it.
MagicPony: Learning Articulated 3D Animals in the Wild
We consider the problem of predicting the 3D shape, articulation, viewpoint, texture, and lighting of an articulated animal like a horse given a single test image as input.
Nondestructive thermographic detection of internal defects using pixel-pattern based laser excitation and photothermal super resolution reconstruction
In this work, we present a novel approach to photothermal super resolution based thermographic resolution of internal defects using two-dimensional pixel pattern-based active photothermal laser heating in conjunction with subsequent numerical reconstruction to achieve a high-resolution reconstruction of internal defect structures.
Unsupervised Multi-object Segmentation by Predicting Probable Motion Patterns
We propose a new approach to learn to segment multiple image objects without manual supervision.
VTC: Improving Video-Text Retrieval with User Comments
In this paper, we a) introduce a new dataset of videos, titles and comments; b) present an attention-based mechanism that allows the model to learn from sometimes irrelevant data such as comments; c) show that by using comments, our method is able to learn better, more contextualised, representations for image, video and audio representations.
Guess What Moves: Unsupervised Video and Image Segmentation by Anticipating Motion
Motion, measured via optical flow, provides a powerful cue to discover and learn objects in images and videos.
Ranked #4 on
Unsupervised Object Segmentation
on SegTrack-v2
Deep Spectral Methods: A Surprisingly Strong Baseline for Unsupervised Semantic Segmentation and Localization
We find that these eigenvectors already decompose an image into meaningful segments, and can be readily used to localize objects in a scene.
Thermographic detection of internal defects using 2D photothermal super resolution reconstruction with sequential laser heating
Based on a combination of the application of special sampling strategies and a subsequent numerical optimization step in post-processing, thermographic super resolution has already proven to be superior to standard thermographic methods in the detection of one-dimensional defect/inhomogeneity structures.
De-rendering 3D Objects in the Wild
With increasing focus on augmented and virtual reality applications (XR) comes the demand for algorithms that can lift objects from images and videos into representations that are suitable for a wide variety of related 3D tasks.
ClevrTex: A Texture-Rich Benchmark for Unsupervised Multi-Object Segmentation
We benchmark a large set of recent unsupervised multi-object segmentation models on ClevrTex and find all state-of-the-art approaches fail to learn good representations in the textured setting, despite impressive performance on simpler data.
Ranked #3 on
Unsupervised Object Segmentation
on ClevrTex
Unsupervised Part Discovery from Contrastive Reconstruction
First, we construct a proxy task through a set of objectives that encourages the model to learn a meaningful decomposition of the image into its parts.
Ranked #1 on
Unsupervised Keypoint Estimation
on CUB
The Curious Layperson: Fine-Grained Image Recognition without Expert Labels
We then train a fine-grained textual similarity model that matches image descriptions with documents on a sentence-level basis.
Learning Context-Adapted Video-Text Retrieval by Attending to User Comments
Learning strong representations for multi-modal retrieval is an important problem for many applications, such as recommendation and search.
PASS: An ImageNet replacement for self-supervised pretraining without humans
On the other hand, state-of-the-art pretraining is nowadays obtained with unsupervised methods, meaning that labelled datasets such as ImageNet may not be necessary, or perhaps not even optimal, for model pretraining.
DOVE: Learning Deformable 3D Objects by Watching Videos
In this paper, we present DOVE, a method that learns textured 3D models of deformable object categories from monocular videos available online, without keypoint, viewpoint or template shape supervision.
Finding an Unsupervised Image Segmenter in Each of Your Deep Generative Models
Recent research has shown that numerous human-interpretable directions exist in the latent space of GANs.
Neural Response Interpretation through the Lens of Critical Pathways
Is critical input information encoded in specific sparse pathways within the neural network?
On Sparse Critical Paths of Neural Response
Is critical input information encoded in specific sparse paths within the network?
Labelling unlabelled videos from scratch with multi-modal self-supervision
A large part of the current success of deep learning lies in the effectiveness of data -- more precisely: labelled data.
Semantic Image Manipulation Using Scene Graphs
In our work, we address the novel problem of image manipulation from scene graphs, in which a user can edit images by merely applying changes in the nodes or edges of a semantic graph that is generated from the image.
Unsupervised Learning of Probably Symmetric Deformable 3D Objects from Images in the Wild
We propose a method to learn 3D deformable object categories from raw single-view images, without external supervision.
Improving Feature Attribution through Input-specific Network Pruning
Attributing the output of a neural network to the contribution of given input elements is a way of shedding light on the black-box nature of neural networks.
Self-labelling via simultaneous clustering and representation learning
Combining clustering and representation learning is one of the most promising approaches for unsupervised learning of deep neural networks.
Ranked #4 on
Image Clustering
on ImageNet
Traffic4cast-Traffic Map Movie Forecasting -- Team MIE-Lab
The goal of the IARAI competition traffic4cast was to predict the city-wide traffic status within a 15-minute time window, based on information from the previous hour.
Towards Unsupervised Image Captioning with Shared Multimodal Embeddings
The core component of our approach is a shared latent space that is structured by visual concepts.
Photo-Geometric Autoencoding to Learn 3D Objects from Unlabelled Images
Specifically, given a single image of the object seen from an arbitrary viewpoint, our model predicts a symmetric canonical view, the corresponding 3D shape and a viewpoint transformation, and trains with the goal of reconstructing the input view, resembling an auto-encoder.
A critical analysis of self-supervision, or what we can learn from a single image
We look critically at popular self-supervision techniques for learning deep convolutional neural networks without manual labels.
Finding and Visualizing Weaknesses of Deep Reinforcement Learning Agents
Further, critical states in which a very high or a very low reward can be achieved are often interesting to understand the situational awareness of the system as they can correspond to risky states.
Explaining the Ambiguity of Object Detection and 6D Pose From Visual Data
For each object instance we predict multiple pose and class outcomes to estimate the specific pose distribution generated by symmetries and repetitive textures.
Dealing with Ambiguity in Robotic Grasping via Multiple Predictions
Further, we reformulate the problem of robotic grasping by replacing conventional grasp rectangles with grasp belief maps, which hold more precise location information than a rectangle and account for the uncertainty inherent to the task.
Understanding Regularization to Visualize Convolutional Neural Networks
Variational methods for revealing visual concepts learned by convolutional neural networks have gained significant attention during the last years.
Guide Me: Interacting with Deep Networks
Interaction and collaboration between humans and intelligent machines has become increasingly important as machine learning methods move into real-world applications that involve end users.
Predicting Multiple Actions for Stochastic Continuous Control
We introduce a new approach to estimate continuous actions using actor-critic algorithms for reinforcement learning problems.
Learning to Imagine Manipulation Goals for Robot Task Planning
Ideally, we would combine the ability of machine learning to leverage big data for learning the semantics of a task, while using techniques from task planning to reliably generalize to new environment.
Learning without Prejudice: Avoiding Bias in Webly-Supervised Action Recognition
One of the main problems in webly-supervised learning is cleaning the noisy labeled data from the web.
Concurrent Segmentation and Localization for Tracking of Surgical Instruments
Real-time instrument tracking is a crucial requirement for various computer-assisted interventions.
Analyzing and Exploiting NARX Recurrent Neural Networks for Long-Term Dependencies
Recurrent neural networks (RNNs) have achieved state-of-the-art performance on many diverse tasks, from machine translation to surgical activity recognition, yet training RNNs to capture long-term dependencies remains difficult.
Learning in an Uncertain World: Representing Ambiguity Through Multiple Hypotheses
In future prediction, for example, many distinct outcomes are equally valid.
Hands-Free Segmentation of Medical Volumes via Binary Inputs
We propose a novel hands-free method to interactively segment 3D medical volumes.
Deep Active Contours
We propose a method for interactive boundary extraction which combines a deep, patch-based representation with an active contour framework.
A Taxonomy and Library for Visualizing Learned Features in Convolutional Neural Networks
Over the last decade, Convolutional Neural Networks (CNN) saw a tremendous surge in performance.
Deeper Depth Prediction with Fully Convolutional Residual Networks
This paper addresses the problem of estimating the depth map of a scene given a single RGB image.
Semi-Automatic Segmentation of Autosomal Dominant Polycystic Kidneys using Random Forests
This paper presents a method for 3D segmentation of kidneys from patients with autosomal dominant polycystic kidney disease (ADPKD) and severe renal insufficiency, using computed tomography (CT) data.
Image Segmentation in Twenty Questions
Consider the following scenario between a human user and the computer.
Robust Optimization for Deep Regression
Convolutional Neural Networks (ConvNets) have successfully contributed to improve the accuracy of regression-based methods for computer vision tasks such as human pose estimation, landmark localization, and object detection.
