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Found 21 papers, 14 papers with code
The revenge of BiSeNet: Efficient Multi-Task Image Segmentation
Recent advancements in image segmentation have focused on enhancing the efficiency of the models to meet the demands of real-time applications, especially on edge devices.
PEM: Prototype-based Efficient MaskFormer for Image Segmentation
To fill this gap, we propose Prototype-based Efficient MaskFormer (PEM), an efficient transformer-based architecture that can operate in multiple segmentation tasks.
Cross-Domain Transfer Learning with CoRTe: Consistent and Reliable Transfer from Black-Box to Lightweight Segmentation Model
Many practical applications require training of semantic segmentation models on unlabelled datasets and their execution on low-resource hardware.
The Robust Semantic Segmentation UNCV2023 Challenge Results
This paper outlines the winning solutions employed in addressing the MUAD uncertainty quantification challenge held at ICCV 2023.
Mask2Anomaly: Mask Transformer for Universal Open-set Segmentation
Segmenting unknown or anomalous object instances is a critical task in autonomous driving applications, and it is approached traditionally as a per-pixel classification problem.
Unmasking Anomalies in Road-Scene Segmentation
We propose a paradigm change by shifting from a per-pixel classification to a mask classification.
Ranked #1 on
Scene Segmentation
on StreetHazards
(using extra training data)
CoMFormer: Continual Learning in Semantic and Panoptic Segmentation
%a In this paper, we present the first continual learning model capable of operating on both semantic and panoptic segmentation.
Ranked #2 on
Continual Semantic Segmentation
on ADE20K
Detecting the unknown in Object Detection
Object detection methods have witnessed impressive improvements in the last years thanks to the design of novel neural network architectures and the availability of large scale datasets.
Modeling Missing Annotations for Incremental Learning in Object Detection
We propose to handle these missing annotations by revisiting the standard knowledge distillation framework.
FedDrive: Generalizing Federated Learning to Semantic Segmentation in Autonomous Driving
For similar reasons, Federated Learning has been recently introduced as a new machine learning paradigm aiming to learn a global model while preserving privacy and leveraging data on millions of remote devices.
Modeling the Background for Incremental and Weakly-Supervised Semantic Segmentation
To tackle these issues, we introduce a novel incremental class learning approach for semantic segmentation taking into account a peculiar aspect of this task: since each training step provides annotation only for a subset of all possible classes, pixels of the background class exhibit a semantic shift.
A Contrastive Distillation Approach for Incremental Semantic Segmentation in Aerial Images
Incremental learning represents a crucial task in aerial image processing, especially given the limited availability of large-scale annotated datasets.
Incremental Learning in Semantic Segmentation from Image Labels
As opposed to existing approaches, that need to generate pseudo-labels offline, we use an auxiliary classifier, trained with image-level labels and regularized by the segmentation model, to obtain pseudo-supervision online and update the model incrementally.
Pixel-by-Pixel Cross-Domain Alignment for Few-Shot Semantic Segmentation
The pixel-wise adversarial training is assisted by a novel sample selection procedure, that handles the imbalance between source and target data, and a knowledge distillation strategy, that avoids overfitting towards the few target images.
On the Challenges of Open World Recognitionunder Shifting Visual Domains
Robotic visual systems operating in the wild must act in unconstrained scenarios, under different environmental conditions while facing a variety of semantic concepts, including unknown ones.
Detecting Anomalies in Semantic Segmentation with Prototypes
Current state of the art of anomaly segmentation uses generative models, exploiting their incapability to reconstruct patterns unseen during training.
A Closer Look at Self-training for Zero-Label Semantic Segmentation
Being able to segment unseen classes not observed during training is an important technical challenge in deep learning, because of its potential to reduce the expensive annotation required for semantic segmentation.
Ranked #8 on
Zero-Shot Semantic Segmentation
on PASCAL VOC
Prototype-based Incremental Few-Shot Semantic Segmentation
Semantic segmentation models have two fundamental weaknesses: i) they require large training sets with costly pixel-level annotations, and ii) they have a static output space, constrained to the classes of the training set.
Boosting Deep Open World Recognition by Clustering
While convolutional neural networks have brought significant advances in robot vision, their ability is often limited to closed world scenarios, where the number of semantic concepts to be recognized is determined by the available training set.
Modeling the Background for Incremental Learning in Semantic Segmentation
Current strategies fail on this task because they do not consider a peculiar aspect of semantic segmentation: since each training step provides annotation only for a subset of all possible classes, pixels of the background class (i. e. pixels that do not belong to any other classes) exhibit a semantic distribution shift.
Ranked #3 on
Domain 11-5
on Cityscapes
The RGB-D Triathlon: Towards Agile Visual Toolboxes for Robots
Deep networks have brought significant advances in robot perception, enabling to improve the capabilities of robots in several visual tasks, ranging from object detection and recognition to pose estimation, semantic scene segmentation and many others.
