269 citations · 550 across the 34 of their papers we have counts for
7 papers · 1 filter
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…