research
∙
09/06/2023
PDiscoNet: Semantically consistent part discovery for fine-grained recognition
Fine-grained classification often requires recognizing specific object p...
share
research
∙
09/05/2023
Iterative Superquadric Recomposition of 3D Objects from Multiple Views
Humans are good at recomposing novel objects, i.e. they can identify com...
research
∙
08/21/2023
Image-free Classifier Injection for Zero-Shot Classification
Zero-shot learning models achieve remarkable results on image classifica...
research
∙
08/18/2023
On the Effectiveness of LayerNorm Tuning for Continual Learning in Vision Transformers
State-of-the-art rehearsal-free continual learning methods exploit the p...
research
∙
07/01/2023
ProbVLM: Probabilistic Adapter for Frozen Vison-Language Models
Large-scale vision-language models (VLMs) like CLIP successfully find co...
research
∙
05/22/2023
If at First You Don't Succeed, Try, Try Again: Faithful Diffusion-based Text-to-Image Generation by Selection
Despite their impressive capabilities, diffusion-based text-to-image (T2...
research
∙
10/19/2022
Cross-Modal Fusion Distillation for Fine-Grained Sketch-Based Image Retrieval
Representation learning for sketch-based image retrieval has mostly been...
research
∙
10/05/2022
Relational Proxies: Emergent Relationships as Fine-Grained Discriminators
Fine-grained categories that largely share the same set of parts cannot ...
research
∙
08/24/2022
Semi-Supervised and Unsupervised Deep Visual Learning: A Survey
State-of-the-art deep learning models are often trained with a large amo...
research
∙
07/27/2022
Abstracting Sketches through Simple Primitives
Humans show high-level of abstraction capabilities in games that require...
research
∙
07/14/2022
BayesCap: Bayesian Identity Cap for Calibrated Uncertainty in Frozen Neural Networks
High-quality calibrated uncertainty estimates are crucial for numerous r...
research
∙
05/13/2022
KG-SP: Knowledge Guided Simple Primitives for Open World Compositional Zero-Shot Learning
The goal of open-world compositional zero-shot learning (OW-CZSL) is to ...
research
∙
04/27/2022
Attention Consistency on Visual Corruptions for Single-Source Domain Generalization
Generalizing visual recognition models trained on a single distribution ...
research
∙
01/31/2022
Modeling the Background for Incremental and Weakly-Supervised Semantic Segmentation
Deep neural networks have enabled major progresses in semantic segmentat...
research
∙
08/19/2021
Concurrent Discrimination and Alignment for Self-Supervised Feature Learning
Existing self-supervised learning methods learn representation by means ...
research
∙
07/09/2021
On the Challenges of Open World Recognitionunder Shifting Visual Domains
Robotic visual systems operating in the wild must act in unconstrained s...
research
∙
06/01/2021
Detecting Anomalies in Semantic Segmentation with Prototypes
Traditional semantic segmentation methods can recognize at test time onl...
research
∙
05/03/2021
Learning Graph Embeddings for Open World Compositional Zero-Shot Learning
Compositional Zero-Shot learning (CZSL) aims to recognize unseen composi...
research
∙
04/21/2021
A Closer Look at Self-training for Zero-Label Semantic Segmentation
Being able to segment unseen classes not observed during training is an ...
research
∙
03/25/2021
Boosting Binary Masks for Multi-Domain Learning through Affine Transformations
In this work, we present a new, algorithm for multi-domain learning. Giv...
research
∙
03/25/2021
Inferring Latent Domains for Unsupervised Deep Domain Adaptation
Unsupervised Domain Adaptation (UDA) refers to the problem of learning a...
research
∙
01/29/2021
Open World Compositional Zero-Shot Learning
Compositional Zero-Shot learning (CZSL) requires to recognize state-obje...
research
∙
12/16/2020
Towards Recognizing New Semantic Concepts in New Visual Domains
Deep learning models heavily rely on large scale annotated datasets for ...
research
∙
11/30/2020
A Few Guidelines for Incremental Few-Shot Segmentation
Reducing the amount of supervision required by neural networks is especi...
research
∙
08/04/2020
Shape Consistent 2D Keypoint Estimation under Domain Shift
Recent unsupervised domain adaptation methods based on deep architecture...
research
∙
07/23/2020
Towards Recognizing Unseen Categories in Unseen Domains
Current deep visual recognition systems suffer from severe performance d...
research
∙
04/20/2020
Boosting Deep Open World Recognition by Clustering
While convolutional neural networks have brought significant advances in...
research
∙
02/03/2020
Modeling the Background for Incremental Learning in Semantic Segmentation
Despite their effectiveness in a wide range of tasks, deep architectures...
research
∙
06/04/2019
Knowledge is Never Enough: Towards Web Aided Deep Open World Recognition
While today's robots are able to perform sophisticated tasks, they can o...
research
∙
04/01/2019
The RGB-D Triathlon: Towards Agile Visual Toolboxes for Robots
Deep networks have brought significant advances in robot perception, ena...
research
∙
03/17/2019
AdaGraph: Unifying Predictive and Continuous Domain Adaptation through Graphs
The ability to categorize is a cornerstone of visual intelligence, and a...
research
∙
03/17/2019
AdaGraph: Unifying Predictive and ContinuousDomain Adaptation through Graphs
The ability to categorize is a cornerstone of visual intelligence, and a...
research
∙
07/03/2018
Kitting in the Wild through Online Domain Adaptation
Technological developments call for increasing perception and action cap...
research
∙
06/15/2018
Best sources forward: domain generalization through source-specific nets
A long standing problem in visual object categorization is the ability o...
research
∙
05/30/2018
Robust Place Categorization with Deep Domain Generalization
Traditional place categorization approaches in robot vision assume that ...
research
∙
05/28/2018
Adding New Tasks to a Single Network with Weight Trasformations using Binary Masks
Visual recognition algorithms are required today to exhibit adaptive abi...
research
∙
05/03/2018
Boosting Domain Adaptation by Discovering Latent Domains
Current Domain Adaptation (DA) methods based on deep architectures assum...
research
∙
07/22/2017
Inspiring Computer Vision System Solutions
The "digital Michelangelo project" was a seminal computer vision project...
research
∙
02/25/2017
Learning Deep NBNN Representations for Robust Place Categorization
This paper presents an approach for semantic place categorization using ...
research
∙
12/08/2016
