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20152023
most citedAutoNovel: Automatically Discovering and Learning Novel Visual Categories

151 citations · 561 across the 38 of their papers we have counts for

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cs.CV2022

Self-Supervised Correspondence Estimation via Multiview Registration

Mohamed El Banani, Ignacio Rocco, David Novotny +4

Video provides us with the spatio-temporal consistency needed for visual learning. Recent approaches have utilized this signal to learn correspondence estimation from close-by fram…

cs.CV2022

Common Pets in 3D: Dynamic New-View Synthesis of Real-Life Deformable Categories

Samarth Sinha, Roman Shapovalov, Jeremy Reizenstein +4

Obtaining photorealistic reconstructions of objects from sparse views is inherently ambiguous and can only be achieved by learning suitable reconstruction priors. Earlier works on…

cs.CV20227 cited

Unsupervised Multi-object Segmentation by Predicting Probable Motion Patterns

Laurynas Karazija, Subhabrata Choudhury, Iro Laina +2

We propose a new approach to learn to segment multiple image objects without manual supervision. The method can extract objects form still images, but uses videos for supervision.…

cs.CV20225 cited

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…

cs.CV20221 cited

Measuring the Interpretability of Unsupervised Representations via Quantized Reverse Probing

Iro Laina, Yuki M. Asano, Andrea Vedaldi

Self-supervised visual representation learning has recently attracted significant research interest. While a common way to evaluate self-supervised representations is through trans…

cs.CV20226 cited

Deep Spectral Methods: A Surprisingly Strong Baseline for Unsupervised Semantic Segmentation and Localization

Luke Melas-Kyriazi, Christian Rupprecht, Iro Laina +1

Unsupervised localization and segmentation are long-standing computer vision challenges that involve decomposing an image into semantically-meaningful segments without any labeled…