36 citations · 240 across the 46 of their papers we have counts for
44 papers · 1 filter
Self-Supervised Learning of Semantically Consistent Visual Features via a Simple 3D Prior
Sebastian Day, Oisin Mac Aodha
Visual features extracted from vision models like DINOv2 or Stable Diffusion enable high quality semantic correspondence between images. However, such features exhibit documented l…
BAT3R: Bootstrapping Articulated 3D Reconstruction from 2D Image Collections
Jakub Zadrozny, Oisin Mac Aodha, Hakan Bilen
3D reconstruction of articulated objects from a single image is challenging because large training datasets with paired image and 3D supervision are difficult to obtain. Recent poi…
BiMotion: B-spline Motion for Text-guided Dynamic 3D Character Generation
Miaowei Wang, Qingxuan Yan, Zhi Cao +4
Text-guided dynamic 3D character generation has advanced rapidly, yet producing high-quality motion that faithfully reflects rich textual descriptions remains challenging. Existing…
View-Consistent Diffusion Representations for 3D-Consistent Video Generation
Duolikun Danier, Ge Gao, Steven McDonagh +3
Video generation models have made significant progress in generating realistic content, enabling applications in simulation, gaming, and film making. However, current generated vid…
Jamais Vu: Exposing the Generalization Gap in Supervised Semantic Correspondence
Octave Mariotti, Zhipeng Du, Yash Bhalgat +2
Semantic correspondence (SC) aims to establish semantically meaningful matches across different instances of an object category. We illustrate how recent supervised SC methods rema…
Representational Difference Explanations
Neehar Kondapaneni, Oisin Mac Aodha, Pietro Perona
We propose a method for discovering and visualizing the differences between two learned representations, enabling more direct and interpretable model comparisons. We validate our m…
Representational Similarity via Interpretable Visual Concepts
Neehar Kondapaneni, Oisin Mac Aodha, Pietro Perona
How do two deep neural networks differ in how they arrive at a decision? Measuring the similarity of deep networks has been a long-standing open question. Most existing methods pro…
CrossSDF: 3D Reconstruction of Thin Structures From Cross-Sections
Thomas Walker, Salvatore Esposito, Daniel Rebain +4
Reconstructing complex structures from planar cross-sections is a challenging problem, with wide-reaching applications in medical imaging, manufacturing, and topography. Out-of-the…
DepthCues: Evaluating Monocular Depth Perception in Large Vision Models
Duolikun Danier, Mehmet Aygün, Changjian Li +2
Large-scale pre-trained vision models are becoming increasingly prevalent, offering expressive and generalizable visual representations that benefit various downstream tasks. Recen…
Labeled Data Selection for Category Discovery
Bingchen Zhao, Nico Lang, Serge Belongie +1
Category discovery methods aim to find novel categories in unlabeled visual data. At training time, a set of labeled and unlabeled images are provided, where the labels correspond…
GeoGen: Geometry-Aware Generative Modeling via Signed Distance Functions
Salvatore Esposito, Qingshan Xu, Kacper Kania +6
We introduce a new generative approach for synthesizing 3D geometry and images from single-view collections. Most existing approaches predict volumetric density to render multi-vie…
AirPlanes: Accurate Plane Estimation via 3D-Consistent Embeddings
Jamie Watson, Filippo Aleotti, Mohamed Sayed +5
Extracting planes from a 3D scene is useful for downstream tasks in robotics and augmented reality. In this paper we tackle the problem of estimating the planar surfaces in a scene…
Enhancing 2D Representation Learning with a 3D Prior
Mehmet Aygün, Prithviraj Dhar, Zhicheng Yan +2
Learning robust and effective representations of visual data is a fundamental task in computer vision. Traditionally, this is achieved by training models with labeled data which ca…
Improving Semantic Correspondence with Viewpoint-Guided Spherical Maps
Octave Mariotti, Oisin Mac Aodha, Hakan Bilen
Recent progress in self-supervised representation learning has resulted in models that are capable of extracting image features that are not only effective at encoding image level,…
VL-Fields: Towards Language-Grounded Neural Implicit Spatial Representations
Nikolaos Tsagkas, Oisin Mac Aodha, Chris Xiaoxuan Lu
We present Visual-Language Fields (VL-Fields), a neural implicit spatial representation that enables open-vocabulary semantic queries. Our model encodes and fuses the geometry of a…
Virtual Occlusions Through Implicit Depth
Jamie Watson, Mohamed Sayed, Zawar Qureshi +4
For augmented reality (AR), it is important that virtual assets appear to `sit among' real world objects. The virtual element should variously occlude and be occluded by real matte…
Incremental Generalized Category Discovery
Bingchen Zhao, Oisin Mac Aodha
We explore the problem of Incremental Generalized Category Discovery (IGCD). This is a challenging category incremental learning setting where the goal is to develop models that ca…
SAOR: Single-View Articulated Object Reconstruction
Mehmet Aygün, Oisin Mac Aodha
We introduce SAOR, a novel approach for estimating the 3D shape, texture, and viewpoint of an articulated object from a single image captured in the wild. Unlike prior approaches t…
ViewNeRF: Unsupervised Viewpoint Estimation Using Category-Level Neural Radiance Fields
Octave Mariotti, Oisin Mac Aodha, Hakan Bilen
We introduce ViewNeRF, a Neural Radiance Field-based viewpoint estimation method that learns to predict category-level viewpoints directly from images during training. While NeRF i…
ViewNet: Unsupervised Viewpoint Estimation from Conditional Generation
Octave Mariotti, Oisin Mac Aodha, Hakan Bilen
Understanding the 3D world without supervision is currently a major challenge in computer vision as the annotations required to supervise deep networks for tasks in this domain are…
An Action Is Worth Multiple Words: Handling Ambiguity in Action Recognition
Kiyoon Kim, Davide Moltisanti, Oisin Mac Aodha +1
Precisely naming the action depicted in a video can be a challenging and oftentimes ambiguous task. In contrast to object instances represented as nouns (e.g. dog, cat, chair, etc.…
SVL-Adapter: Self-Supervised Adapter for Vision-Language Pretrained Models
Omiros Pantazis, Gabriel Brostow, Kate Jones +1
Vision-language models such as CLIP are pretrained on large volumes of internet sourced image and text pairs, and have been shown to sometimes exhibit impressive zero- and low-shot…
Visual Knowledge Tracing
Neehar Kondapaneni, Pietro Perona, Oisin Mac Aodha
Each year, thousands of people learn new visual categorization tasks -- radiologists learn to recognize tumors, birdwatchers learn to distinguish similar species, and crowd workers…
Exploring Fine-Grained Audiovisual Categorization with the SSW60 Dataset
Grant Van Horn, Rui Qian, Kimberly Wilber +3
We present a new benchmark dataset, Sapsucker Woods 60 (SSW60), for advancing research on audiovisual fine-grained categorization. While our community has made great strides in fin…