269 citations · 550 across the 34 of their papers we have counts for
40 papers
Whareformer: Learning to Track What is Where in Long Egocentric Videos
Jacob Chalk, Saptarshi Sinha, Dima Damen +2
The recently established 'Out of Sight, Not out of Mind' (OSNOM) task for egocentric videos focuses on tracking objects that are moved by the camera wearer, online, maintaining kno…
Syn4D: A Multiview Synthetic 4D Dataset
Zeren Jiang, Yushi Lan, Yihang Luo +8
Dense 3D reconstruction and tracking of dynamic scenes from monocular video remains an important open challenge in computer vision. Progress in this area has been constrained by th…
Mesh4D: 4D Mesh Reconstruction and Tracking from Monocular Video
Zeren Jiang, Chuanxia Zheng, Iro Laina +2
We propose Mesh4D, a feed-forward model for monocular 4D mesh reconstruction. Given a monocular video of a dynamic object, our model reconstructs the object's complete 3D shape and…
Layered Motion Fusion: Lifting Motion Segmentation to 3D in Egocentric Videos
Vadim Tschernezki, Diane Larlus, Iro Laina +1
Computer vision is largely based on 2D techniques, with 3D vision still relegated to a relatively narrow subset of applications. However, by building on recent advances in 3D model…
Geo4D: Leveraging Video Generators for Geometric 4D Scene Reconstruction
Zeren Jiang, Chuanxia Zheng, Iro Laina +2
We introduce Geo4D, a method to repurpose video diffusion models for monocular 3D reconstruction of dynamic scenes. By leveraging the strong dynamic priors captured by large-scale…
What could go wrong? Discovering and describing failure modes in computer vision
Gabriela Csurka, Tyler L. Hayes, Diane Larlus +1
Deep learning models are effective, yet brittle. Even carefully trained, their behavior tends to be hard to predict when confronted with out-of-distribution samples. In this work,…
Weatherproofing Retrieval for Localization with Generative AI and Geometric Consistency
Yannis Kalantidis, Mert Bülent Sarıyıldız, Rafael S. Rezende +3
State-of-the-art visual localization approaches generally rely on a first image retrieval step whose role is crucial. Yet, retrieval often struggles when facing varying conditions,…
PANDAS: Prototype-based Novel Class Discovery and Detection
Tyler L. Hayes, César R. de Souza, Namil Kim +3
Object detectors are typically trained once and for all on a fixed set of classes. However, this closed-world assumption is unrealistic in practice, as new classes will inevitably…
SLACK: Stable Learning of Augmentations with Cold-start and KL regularization
Juliette Marrie, Michael Arbel, Diane Larlus +1
Data augmentation is known to improve the generalization capabilities of neural networks, provided that the set of transformations is chosen with care, a selection often performed…
EPIC Fields: Marrying 3D Geometry and Video Understanding
Vadim Tschernezki, Ahmad Darkhalil, Zhifan Zhu +5
Neural rendering is fuelling a unification of learning, 3D geometry and video understanding that has been waiting for more than two decades. Progress, however, is still hampered by…
RaSP: Relation-aware Semantic Prior for Weakly Supervised Incremental Segmentation
Subhankar Roy, Riccardo Volpi, Gabriela Csurka +1
Class-incremental semantic image segmentation assumes multiple model updates, each enriching the model to segment new categories. This is typically carried out by providing expensi…
Fake it till you make it: Learning transferable representations from synthetic ImageNet clones
Mert Bulent Sariyildiz, Karteek Alahari, Diane Larlus +1
Recent image generation models such as Stable Diffusion have exhibited an impressive ability to generate fairly realistic images starting from a simple text prompt. Could such mode…
Granularity-aware Adaptation for Image Retrieval over Multiple Tasks
Jon Almazán, Byungsoo Ko, Geonmo Gu +2
Strong image search models can be learned for a specific domain, ie. set of labels, provided that some labeled images of that domain are available. A practical visual search model,…
Neural Feature Fusion Fields: 3D Distillation of Self-Supervised 2D Image Representations
Vadim Tschernezki, Iro Laina, Diane Larlus +1
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 reconstructib…
No Reason for No Supervision: Improved Generalization in Supervised Models
Mert Bulent Sariyildiz, Yannis Kalantidis, Karteek Alahari +1
We consider the problem of training a deep neural network on a given classification task, e.g., ImageNet-1K (IN1K), so that it excels at both the training task as well as at other…
On the Road to Online Adaptation for Semantic Image Segmentation
Riccardo Volpi, Pau de Jorge, Diane Larlus +1
We propose a new problem formulation and a corresponding evaluation framework to advance research on unsupervised domain adaptation for semantic image segmentation. The overall goa…
ARTEMIS: Attention-based Retrieval with Text-Explicit Matching and Implicit Similarity
Ginger Delmas, Rafael Sampaio de Rezende, Gabriela Csurka +1
An intuitive way to search for images is to use queries composed of an example image and a complementary text. While the first provides rich and implicit context for the search, th…
Learning Super-Features for Image Retrieval
Philippe Weinzaepfel, Thomas Lucas, Diane Larlus +1
Methods that combine local and global features have recently shown excellent performance on multiple challenging deep image retrieval benchmarks, but their use of local features ra…
Domain Adaptation in Multi-View Embedding for Cross-Modal Video Retrieval
Jonathan Munro, Michael Wray, Diane Larlus +2
Given a gallery of uncaptioned video sequences, this paper considers the task of retrieving videos based on their relevance to an unseen text query. To compensate for the lack of a…
NeuralDiff: Segmenting 3D objects that move in egocentric videos
Vadim Tschernezki, Diane Larlus, Andrea Vedaldi
Given a raw video sequence taken from a freely-moving camera, we study the problem of decomposing the observed 3D scene into a static background and a dynamic foreground containing…
TLDR: Twin Learning for Dimensionality Reduction
Yannis Kalantidis, Carlos Lassance, Jon Almazan +1
Dimensionality reduction methods are unsupervised approaches which learn low-dimensional spaces where some properties of the initial space, typically the notion of "neighborhood",…
Probabilistic Embeddings for Cross-Modal Retrieval
Sanghyuk Chun, Seong Joon Oh, Rafael Sampaio de Rezende +2
Cross-modal retrieval methods build a common representation space for samples from multiple modalities, typically from the vision and the language domains. For images and their cap…
StacMR: Scene-Text Aware Cross-Modal Retrieval
Andrés Mafla, Rafael Sampaio de Rezende, Lluís Gómez +2
Recent models for cross-modal retrieval have benefited from an increasingly rich understanding of visual scenes, afforded by scene graphs and object interactions to mention a few.…
Concept Generalization in Visual Representation Learning
Mert Bulent Sariyildiz, Yannis Kalantidis, Diane Larlus +1
Measuring concept generalization, i.e., the extent to which models trained on a set of (seen) visual concepts can be leveraged to recognize a new set of (unseen) concepts, is a pop…