CODE and DATA

Data, code and models released by NAVER LABS Europe

SLACK

Stable Learning of Augmentations with Cold-start and KL regularization.

Learning augmentation policies without prior knowledge.

RELIS semantic segmentation

Reliability in semantic segmentation: are we on the right track?

A codebase to evaluate the robustness and uncertainty properties of semantic segmentation models as implemented in the CVPR 2024 paper.

T-REX

No reason for no supervision: improved generalization in supervised models.

Model for transfer learning.

Synthetic ImageNet clones

Fake it till you make it: learning transferable representations from synthetic ImageNet clones.

Two ResNet50 models pretrained on our synthetic ImageNet clones: ImageNet-100-SD or ImageNet-1K-SD.

ARTEMIS

Attention-based Retrieval with Text-Explicit Matching and Implicit Similarity.

An Explicit Matching module for compatibility and an Implicit Similarity module for relevance.

Learning super-features for image retrieval

A novel architecture for deep image retrieval

Code for running our FIRe model , based solely on mid-level features that we call super-features.

Neural feature fusion fields

3D distillation of self-supervised 2D image representations.

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.

Semantic segmentation (OASIS benchmark)

On the road to Online Adaptation for Semantic Image Segmentation (OASIS).

A Pytorch codebase for research to replicate the CVPR22 paper.

Single-step adversarial training (N-FGSM)

Make some noise: reliable and efficient single-step adversarial training.

Official repo for the NeurIPS 2022 paper.

StacMR

Scene-Text Aware Cross-Modal Retrieval

Dataset that allows exploration of cross-modal retrieval where images contain scene-text instances.

TLDR

Twin Learning for Dimensionality Reduction

A method that is simple, easy to implement and train and of broad applicability.

CoG benchmark

Concept generalization in visual representation learning.

Code repository for the ImageNet-CoG Benchmark introduced in the paper ICCV 2021 paper.

MOCHI

Mixing of Contrastive Hard negatives.

Data mixing strategies that can be computed on-the-fly with minimal computational overhead, highly transferable visual representations.

Deep image retrieval

End-to-end learning of deep visual representations for image retrieval.

Repository contains models and evaluation scripts of papers ‘End-to-end Learning of Deep Visual Representations for Image Retrieval’ & ‘Learning with Average Precision: Training Image Retrieval with a Listwise Loss’.

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