Search Results for author: Yongqin Xian

Found 29 papers, 16 papers with code

LIME: Localized Image Editing via Attention Regularization in Diffusion Models

no code implementations14 Dec 2023 Enis Simsar, Alessio Tonioni, Yongqin Xian, Thomas Hofmann, Federico Tombari

Diffusion models (DMs) have gained prominence due to their ability to generate high-quality, varied images, with recent advancements in text-to-image generation.

Denoising Semantic Segmentation +1

LALM: Long-Term Action Anticipation with Language Models

no code implementations29 Nov 2023 Sanghwan Kim, Daoji Huang, Yongqin Xian, Otmar Hilliges, Luc van Gool, Xi Wang

Understanding human activity is a crucial yet intricate task in egocentric vision, a field that focuses on capturing visual perspectives from the camera wearer's viewpoint.

Action Anticipation Action Recognition +4

SILC: Improving Vision Language Pretraining with Self-Distillation

no code implementations20 Oct 2023 Muhammad Ferjad Naeem, Yongqin Xian, Xiaohua Zhai, Lukas Hoyer, Luc van Gool, Federico Tombari

However, the contrastive objective used by these models only focuses on image-text alignment and does not incentivise image feature learning for dense prediction tasks.

Classification Contrastive Learning +8

Detecting Adversarial Faces Using Only Real Face Self-Perturbations

1 code implementation22 Apr 2023 Qian Wang, Yongqin Xian, Hefei Ling, Jinyuan Zhang, Xiaorui Lin, Ping Li, Jiazhong Chen, Ning Yu

Adversarial attacks aim to disturb the functionality of a target system by adding specific noise to the input samples, bringing potential threats to security and robustness when applied to facial recognition systems.

Face Detection

Learning Prototype Classifiers for Long-Tailed Recognition

1 code implementation1 Feb 2023 Saurabh Sharma, Yongqin Xian, Ning Yu, Ambuj Singh

In this work, we show that learning prototype classifiers addresses the biased softmax problem in LTR.

Long-tail Learning

Weakly-Supervised Domain Adaptive Semantic Segmentation With Prototypical Contrastive Learning

1 code implementation CVPR 2023 Anurag Das, Yongqin Xian, Dengxin Dai, Bernt Schiele

In this work, we propose a common framework to use different weak labels, e. g. image, point and coarse labels from target domain to reduce this performance gap.

Contrastive Learning Semantic Segmentation +1

Urban Scene Semantic Segmentation with Low-Cost Coarse Annotation

no code implementations15 Dec 2022 Anurag Das, Yongqin Xian, Yang He, Zeynep Akata, Bernt Schiele

For best performance, today's semantic segmentation methods use large and carefully labeled datasets, requiring expensive annotation budgets.

Data Augmentation Scene Segmentation +1

I2DFormer: Learning Image to Document Attention for Zero-Shot Image Classification

no code implementations21 Sep 2022 Muhammad Ferjad Naeem, Yongqin Xian, Luc van Gool, Federico Tombari

In order to distill discriminative visual words from noisy documents, we introduce a new cross-modal attention module that learns fine-grained interactions between image patches and document words.

Generalized Zero-Shot Learning Image Classification +2

Attribute Prototype Network for Any-Shot Learning

no code implementations4 Apr 2022 Wenjia Xu, Yongqin Xian, Jiuniu Wang, Bernt Schiele, Zeynep Akata

While a visual-semantic embedding layer learns global features, local features are learned through an attribute prototype network that simultaneously regresses and decorrelates attributes from intermediate features.

Attribute Few-Shot Image Classification +2

VGSE: Visually-Grounded Semantic Embeddings for Zero-Shot Learning

1 code implementation CVPR 2022 Wenjia Xu, Yongqin Xian, Jiuniu Wang, Bernt Schiele, Zeynep Akata

Our model visually divides a set of images from seen classes into clusters of local image regions according to their visual similarity, and further imposes their class discrimination and semantic relatedness.

Transfer Learning Word Embeddings +1

Learning Graph Embeddings for Open World Compositional Zero-Shot Learning

2 code implementations3 May 2021 Massimiliano Mancini, Muhammad Ferjad Naeem, Yongqin Xian, Zeynep Akata

In this work, we overcome this assumption operating on the open world setting, where no limit is imposed on the compositional space at test time, and the search space contains a large number of unseen compositions.

Compositional Zero-Shot Learning

Distilling Audio-Visual Knowledge by Compositional Contrastive Learning

1 code implementation CVPR 2021 Yanbei Chen, Yongqin Xian, A. Sophia Koepke, Ying Shan, Zeynep Akata

Having access to multi-modal cues (e. g. vision and audio) empowers some cognitive tasks to be done faster compared to learning from a single modality.

Audio Tagging audio-visual learning +5

A Closer Look at Self-training for Zero-Label Semantic Segmentation

1 code implementation21 Apr 2021 Giuseppe Pastore, Fabio Cermelli, Yongqin Xian, Massimiliano Mancini, Zeynep Akata, Barbara Caputo

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.

Segmentation Semantic Segmentation

Learning Graph Embeddings for Compositional Zero-shot Learning

1 code implementation CVPR 2021 Muhammad Ferjad Naeem, Yongqin Xian, Federico Tombari, Zeynep Akata

In compositional zero-shot learning, the goal is to recognize unseen compositions (e. g. old dog) of observed visual primitives states (e. g. old, cute) and objects (e. g. car, dog) in the training set.

Compositional Zero-Shot Learning Graph Embedding +1

Open World Compositional Zero-Shot Learning

2 code implementations CVPR 2021 Massimiliano Mancini, Muhammad Ferjad Naeem, Yongqin Xian, Zeynep Akata

After estimating the feasibility score of each composition, we use these scores to either directly mask the output space or as a margin for the cosine similarity between visual features and compositional embeddings during training.

Compositional Zero-Shot Learning

Prototype-based Incremental Few-Shot Semantic Segmentation

1 code implementation30 Nov 2020 Fabio Cermelli, Massimiliano Mancini, Yongqin Xian, Zeynep Akata, Barbara Caputo

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.

Few-Shot Semantic Segmentation Incremental Learning +3

Attribute Prototype Network for Zero-Shot Learning

no code implementations NeurIPS 2020 Wenjia Xu, Yongqin Xian, Jiuniu Wang, Bernt Schiele, Zeynep Akata

As an additional benefit, our model points to the visual evidence of the attributes in an image, e. g. for the CUB dataset, confirming the improved attribute localization ability of our image representation.

Attribute Representation Learning +1

Analyzing the Dependency of ConvNets on Spatial Information

no code implementations5 Feb 2020 Yue Fan, Yongqin Xian, Max Maria Losch, Bernt Schiele

In this paper, we are pushing the envelope and aim to further investigate the reliance on spatial information.

Image Classification Object Recognition

f-VAEGAN-D2: A Feature Generating Framework for Any-Shot Learning

no code implementations CVPR 2019 Yongqin Xian, Saurabh Sharma, Bernt Schiele, Zeynep Akata

When labeled training data is scarce, a promising data augmentation approach is to generate visual features of unknown classes using their attributes.

Data Augmentation Few-Shot Learning +2

Feature Generating Networks for Zero-Shot Learning

4 code implementations CVPR 2018 Yongqin Xian, Tobias Lorenz, Bernt Schiele, Zeynep Akata

Suffering from the extreme training data imbalance between seen and unseen classes, most of existing state-of-the-art approaches fail to achieve satisfactory results for the challenging generalized zero-shot learning task.

Generalized Zero-Shot Learning Generative Adversarial Network

Zero-Shot Learning -- A Comprehensive Evaluation of the Good, the Bad and the Ugly

9 code implementations3 Jul 2017 Yongqin Xian, Christoph H. Lampert, Bernt Schiele, Zeynep Akata

Due to the importance of zero-shot learning, i. e. classifying images where there is a lack of labeled training data, the number of proposed approaches has recently increased steadily.

Zero-Shot Learning

Zero-Shot Learning -- The Good, the Bad and the Ugly

1 code implementation CVPR 2017 Yongqin Xian, Bernt Schiele, Zeynep Akata

Due to the importance of zero-shot learning, the number of proposed approaches has increased steadily recently.

Zero-Shot Learning

Latent Embeddings for Zero-shot Classification

no code implementations CVPR 2016 Yongqin Xian, Zeynep Akata, Gaurav Sharma, Quynh Nguyen, Matthias Hein, Bernt Schiele

We train the model with a ranking based objective function which penalizes incorrect rankings of the true class for a given image.

Classification General Classification +1

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