Search Results for author:
Found 26 papers, 23 papers with code
Unsupervised Representation Learning by Predicting Image Rotations
However, in order to successfully learn those features, they usually require massive amounts of manually labeled data, which is both expensive and impractical to scale.
Ranked #126 on
Self-Supervised Image Classification
on ImageNet
PersonLab: Person Pose Estimation and Instance Segmentation with a Bottom-Up, Part-Based, Geometric Embedding Model
We present a box-free bottom-up approach for the tasks of pose estimation and instance segmentation of people in multi-person images using an efficient single-shot model.
Ranked #8 on
Multi-Person Pose Estimation
on COCO test-dev
Dynamic Few-Shot Visual Learning without Forgetting
In this context, the goal of our work is to devise a few-shot visual learning system that during test time it will be able to efficiently learn novel categories from only a few training data while at the same time it will not forget the initial categories on which it was trained (here called base categories).
Ranked #2 on
Few-Shot Image Classification
on ImageNet (1-shot)
Localizing Objects with Self-Supervised Transformers and no Labels
We also show that training a class-agnostic detector on the discovered objects boosts results by another 7 points.
Ranked #4 on
Weakly-Supervised Object Localization
on CUB-200-2011
(Top-1 Localization Accuracy metric)
LocNet: Improving Localization Accuracy for Object Detection
We propose a novel object localization methodology with the purpose of boosting the localization accuracy of state-of-the-art object detection systems.
Image-to-Lidar Self-Supervised Distillation for Autonomous Driving Data
In this context, we propose a self-supervised pre-training method for 3D perception models that is tailored to autonomous driving data.
Generating Classification Weights with GNN Denoising Autoencoders for Few-Shot Learning
The meta-model, given as input some novel classes with few training examples per class, must properly adapt the existing recognition model into a new model that can correctly classify in a unified way both the novel and the base classes.
Boosting Few-Shot Visual Learning with Self-Supervision
Few-shot learning and self-supervised learning address different facets of the same problem: how to train a model with little or no labeled data.
Object detection via a multi-region & semantic segmentation-aware CNN model
We propose an object detection system that relies on a multi-region deep convolutional neural network (CNN) that also encodes semantic segmentation-aware features.
Object Detection via a Multi-Region and Semantic Segmentation-Aware CNN Model
We propose an object detection system that relies on a multi-region deep convolutional neural network (CNN) that also encodes semantic segmentation-aware features.
OBoW: Online Bag-of-Visual-Words Generation for Self-Supervised Learning
With this in mind, we propose a teacher-student scheme to learn representations by training a convolutional net to reconstruct a bag-of-visual-words (BoW) representation of an image, given as input a perturbed version of that same image.
Ranked #18 on
Semi-Supervised Image Classification
on ImageNet - 1% labeled data
(Top 5 Accuracy metric)
RangeViT: Towards Vision Transformers for 3D Semantic Segmentation in Autonomous Driving
(c) We refine pixel-wise predictions with a convolutional decoder and a skip connection from the convolutional stem to combine low-level but fine-grained features of the the convolutional stem with the high-level but coarse predictions of the ViT encoder.
Attend Refine Repeat: Active Box Proposal Generation via In-Out Localization
We extensively evaluate our AttractioNet approach on several image datasets (i. e. COCO, PASCAL, ImageNet detection and NYU-Depth V2 datasets) reporting on all of them state-of-the-art results that surpass the previous work in the field by a significant margin and also providing strong empirical evidence that our approach is capable to generalize to unseen categories.
Drive&Segment: Unsupervised Semantic Segmentation of Urban Scenes via Cross-modal Distillation
This work investigates learning pixel-wise semantic image segmentation in urban scenes without any manual annotation, just from the raw non-curated data collected by cars which, equipped with cameras and LiDAR sensors, drive around a city.
What to Hide from Your Students: Attention-Guided Masked Image Modeling
In this work, we argue that image token masking differs from token masking in text, due to the amount and correlation of tokens in an image.
Unsupervised Object Localization in the Era of Self-Supervised ViTs: A Survey
We propose here a survey of unsupervised object localization methods that discover objects in images without requiring any manual annotation in the era of self-supervised ViTs.
Detect, Replace, Refine: Deep Structured Prediction For Pixel Wise Labeling
Instead, we propose a generic architecture that decomposes the label improvement task to three steps: 1) detecting the initial label estimates that are incorrect, 2) replacing the incorrect labels with new ones, and finally 3) refining the renewed labels by predicting residual corrections w. r. t.
Active Learning Strategies for Weakly-supervised Object Detection
On COCO, using on average 10 fully-annotated images per class, or equivalently 1% of the training set, BiB also reduces the performance gap (in AP) between the weakly-supervised detector and the fully-supervised Fast RCNN by over 70%, showing a good trade-off between performance and data efficiency.
SPOT: Self-Training with Patch-Order Permutation for Object-Centric Learning with Autoregressive Transformers
Unsupervised object-centric learning aims to decompose scenes into interpretable object entities, termed slots.
Three Pillars improving Vision Foundation Model Distillation for Lidar
In particular, thanks to our scalable distillation method named ScaLR, we show that scaling the 2D and 3D backbones and pretraining on diverse datasets leads to a substantial improvement of the feature quality.
Learning Representations by Predicting Bags of Visual Words
Inspired by the success of NLP methods in this area, in this work we propose a self-supervised approach based on spatially dense image descriptions that encode discrete visual concepts, here called visual words.
QUEST: Quantized embedding space for transferring knowledge
Knowledge distillation refers to the process of training a compact student network to achieve better accuracy by learning from a high capacity teacher network.
MOCA: Self-supervised Representation Learning by Predicting Masked Online Codebook Assignments
Self-supervised learning can be used for mitigating the greedy needs of Vision Transformer networks for very large fully-annotated datasets.
POP-3D: Open-Vocabulary 3D Occupancy Prediction from Images
We describe an approach to predict open-vocabulary 3D semantic voxel occupancy map from input 2D images with the objective of enabling 3D grounding, segmentation and retrieval of free-form language queries.
3D Semantic Occupancy Prediction
3D Semantic Segmentation
+3
OccFeat: Self-supervised Occupancy Feature Prediction for Pretraining BEV Segmentation Networks
Models pretrained with our method exhibit improved BEV semantic segmentation performance, particularly in low-data scenarios.
