Search Results for author:
Found 31 papers, 13 papers with code
N2F2: Hierarchical Scene Understanding with Nested Neural Feature Fields
To address this, we introduce Nested Neural Feature Fields (N2F2), a novel approach that employs hierarchical supervision to learn a single feature field, wherein different dimensions within the same high-dimensional feature encode scene properties at varying granularities.
IM-3D: Iterative Multiview Diffusion and Reconstruction for High-Quality 3D Generation
A mitigation is to fine-tune the 2D generator to be multi-view aware, which can help distillation or can be combined with reconstruction networks to output 3D objects directly.
SHAP-EDITOR: Instruction-guided Latent 3D Editing in Seconds
In particular, we hypothesise that editing can be greatly simplified by first encoding 3D objects in a suitable latent space.
Understanding Self-Supervised Features for Learning Unsupervised Instance Segmentation
Self-supervised learning (SSL) can be used to solve complex visual tasks without human labels.
Ranked #2 on
Unsupervised Instance Segmentation
on COCO val2017
Diffusion Models for Zero-Shot Open-Vocabulary Segmentation
This provides a distribution of appearances for a given text circumventing the ambiguity problem.
EPIC Fields: Marrying 3D Geometry and Video Understanding
Compared to other neural rendering datasets, EPIC Fields is better tailored to video understanding because it is paired with labelled action segments and the recent VISOR segment annotations.
Contrastive Lift: 3D Object Instance Segmentation by Slow-Fast Contrastive Fusion
Our approach outperforms the state-of-the-art on challenging scenes from the ScanNet, Hypersim, and Replica datasets, as well as on our newly created Messy Rooms dataset, demonstrating the effectiveness and scalability of our slow-fast clustering method.
Training-Free Layout Control with Cross-Attention Guidance
We thoroughly evaluate our approach on three benchmarks and provide several qualitative examples and a comparative analysis of the two strategies that demonstrate the superiority of backward guidance compared to forward guidance, as well as prior work.
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.
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.
Unsupervised Multi-object Segmentation by Predicting Probable Motion Patterns
We propose a new approach to learn to segment multiple image objects without manual supervision.
Measuring the Interpretability of Unsupervised Representations via Quantized Reverse Probing
Self-supervised visual representation learning has recently attracted significant research interest.
Neural Feature Fusion Fields: 3D Distillation of Self-Supervised 2D Image Representations
We present Neural Feature Fusion Fields (N3F), a method that improves dense 2D image feature extractors when the latter are applied to the analysis of multiple images reconstructible as a 3D scene.
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.
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.
Measuring the Interpretability of Unsupervised Representations via Quantized Reversed Probing
Self-supervised visual representation learning has attracted significant research interest.
Ranked #90 on
Image Classification
on ObjectNet
(using extra training data)
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.
Quantifying Learnability and Describability of Visual Concepts Emerging in Representation Learning
The increasing impact of black box models, and particularly of unsupervised ones, comes with an increasing interest in tools to understand and interpret them.
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.
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.
2017 Robotic Instrument Segmentation Challenge
In mainstream computer vision and machine learning, public datasets such as ImageNet, COCO and KITTI have helped drive enormous improvements by enabling researchers to understand the strengths and limitations of different algorithms via performance comparison.
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.
Peeking Behind Objects: Layered Depth Prediction from a Single Image
While conventional depth estimation can infer the geometry of a scene from a single RGB image, it fails to estimate scene regions that are occluded by foreground objects.
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.
CNN-SLAM: Real-time dense monocular SLAM with learned depth prediction
Given the recent advances in depth prediction from Convolutional Neural Networks (CNNs), this paper investigates how predicted depth maps from a deep neural network can be deployed for accurate and dense monocular reconstruction.
Concurrent Segmentation and Localization for Tracking of Surgical Instruments
Real-time instrument tracking is a crucial requirement for various computer-assisted interventions.
Learning in an Uncertain World: Representing Ambiguity Through Multiple Hypotheses
In future prediction, for example, many distinct outcomes are equally valid.
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.
