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04/22/2023
Detecting Adversarial Faces Using Only Real Face Self-Perturbations
Adversarial attacks aim to disturb the functionality of a target system ...
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02/01/2023
Learning Prototype Classifiers for Long-Tailed Recognition
The problem of long-tailed recognition (LTR) has received attention in r...
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12/15/2022
Urban Scene Semantic Segmentation with Low-Cost Coarse Annotation
For best performance, today's semantic segmentation methods use large an...
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12/05/2022
I2MVFormer: Large Language Model Generated Multi-View Document Supervision for Zero-Shot Image Classification
Recent works have shown that unstructured text (documents) from online s...
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09/21/2022
I2DFormer: Learning Image to Document Attention for Zero-Shot Image Classification
Despite the tremendous progress in zero-shot learning(ZSL), the majority...
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04/04/2022
Attribute Prototype Network for Any-Shot Learning
Any-shot image classification allows to recognize novel classes with onl...
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03/20/2022
VGSE: Visually-Grounded Semantic Embeddings for Zero-Shot Learning
Human-annotated attributes serve as powerful semantic embeddings in zero...
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11/29/2021
3D Compositional Zero-shot Learning with DeCompositional Consensus
Parts represent a basic unit of geometric and semantic similarity across...
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05/03/2021
Learning Graph Embeddings for Open World Compositional Zero-Shot Learning
Compositional Zero-Shot learning (CZSL) aims to recognize unseen composi...
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04/22/2021
Distilling Audio-Visual Knowledge by Compositional Contrastive Learning
Having access to multi-modal cues (e.g. vision and audio) empowers some ...
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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 ...
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02/03/2021
Learning Graph Embeddings for Compositional Zero-shot Learning
In compositional zero-shot learning, the goal is to recognize unseen com...
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01/29/2021
Open World Compositional Zero-Shot Learning
Compositional Zero-Shot learning (CZSL) requires to recognize state-obje...
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11/30/2020
A Few Guidelines for Incremental Few-Shot Segmentation
Reducing the amount of supervision required by neural networks is especi...
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08/19/2020
Attribute Prototype Network for Zero-Shot Learning
From the beginning of zero-shot learning research, visual attributes hav...
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07/09/2020
Generalized Many-Way Few-Shot Video Classification
Few-shot learning methods operate in low data regimes. The aim is to lea...
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02/05/2020
Analyzing the Dependency of ConvNets on Spatial Information
Intuitively, image classification should profit from using spatial infor...
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03/25/2019
f-VAEGAN-D2: A Feature Generating Framework for Any-Shot Learning
When labeled training data is scarce, a promising data augmentation appr...
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12/04/2017
Feature Generating Networks for Zero-Shot Learning
Suffering from the extreme training data imbalance between seen and unse...
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07/03/2017
Zero-Shot Learning - A Comprehensive Evaluation of the Good, the Bad and the Ugly
Due to the importance of zero-shot learning, i.e. classifying images whe...
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03/13/2017
Zero-Shot Learning - The Good, the Bad and the Ugly
Due to the importance of zero-shot learning, the number of proposed appr...
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03/29/2016