Search Results for author: Yanbei Chen

Found 18 papers, 13 papers with code

Learning Joint Visual Semantic Matching Embeddings for Language-guided Retrieval

no code implementations ECCV 2020 Yanbei Chen, Loris Bazzani

Interactive image retrieval is an emerging research topic with the objective of integrating inputs from multiple modalities as query for retrieval, e. g., textual feedback from users to guide, modify or refine image retrieval.

Image Retrieval Retrieval +2

Distilling Knowledge from Self-Supervised Teacher by Embedding Graph Alignment

1 code implementation23 Nov 2022 Yuchen Ma, Yanbei Chen, Zeynep Akata

In this work, we formulate a new knowledge distillation framework to transfer the knowledge from self-supervised pre-trained models to any other student network by a novel approach named Embedding Graph Alignment.

Knowledge Distillation Representation Learning +1

Cross-Modal Fusion Distillation for Fine-Grained Sketch-Based Image Retrieval

1 code implementation19 Oct 2022 Abhra Chaudhuri, Massimiliano Mancini, Yanbei Chen, Zeynep Akata, Anjan Dutta

Representation learning for sketch-based image retrieval has mostly been tackled by learning embeddings that discard modality-specific information.

Cross-Modal Retrieval Knowledge Distillation +3

Semi-Supervised and Unsupervised Deep Visual Learning: A Survey

no code implementations24 Aug 2022 Yanbei Chen, Massimiliano Mancini, Xiatian Zhu, Zeynep Akata

Semi-supervised learning and unsupervised learning offer promising paradigms to learn from an abundance of unlabeled visual data.

BayesCap: Bayesian Identity Cap for Calibrated Uncertainty in Frozen Neural Networks

1 code implementation14 Jul 2022 Uddeshya Upadhyay, Shyamgopal Karthik, Yanbei Chen, Massimiliano Mancini, Zeynep Akata

Moreover, many of the high-performing deep learning models that are already trained and deployed are non-Bayesian in nature and do not provide uncertainty estimates.

Autonomous Driving Deblurring +2

Attention Consistency on Visual Corruptions for Single-Source Domain Generalization

1 code implementation27 Apr 2022 Ilke Cugu, Massimiliano Mancini, Yanbei Chen, Zeynep Akata

Generalizing visual recognition models trained on a single distribution to unseen input distributions (i. e. domains) requires making them robust to superfluous correlations in the training set.

Domain Generalization

Probabilistic Compositional Embeddings for Multimodal Image Retrieval

1 code implementation12 Apr 2022 Andrei Neculai, Yanbei Chen, Zeynep Akata

Without bells and whistles, we show that our probabilistic model formulation significantly outperforms existing related methods on multimodal image retrieval while generalizing well to query with different amounts of inputs given in arbitrary visual and (or) textual modalities.

Image Retrieval Retrieval

Robustness via Uncertainty-aware Cycle Consistency

1 code implementation NeurIPS 2021 Uddeshya Upadhyay, Yanbei Chen, Zeynep Akata

Unpaired image-to-image translation refers to learning inter-image-domain mapping without corresponding image pairs.

Autonomous Driving Image-to-Image Translation +1

Uncertainty-Guided Progressive GANs for Medical Image Translation

1 code implementation29 Jun 2021 Uddeshya Upadhyay, Yanbei Chen, Tobias Hepp, Sergios Gatidis, Zeynep Akata

However, the state-of-the-art GAN-based frameworks do not estimate the uncertainty in the predictions made by the network that is essential for making informed medical decisions and subsequent revision by medical experts and has recently been shown to improve the performance and interpretability of the model.

Denoising Image-to-Image Translation +2

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

Uncertainty-aware Generalized Adaptive CycleGAN

1 code implementation23 Feb 2021 Uddeshya Upadhyay, Yanbei Chen, Zeynep Akata

Unpaired image-to-image translation refers to learning inter-image-domain mapping in an unsupervised manner.

Image Denoising Image-to-Image Translation +1

Instance-Guided Context Rendering for Cross-Domain Person Re-Identification

no code implementations ICCV 2019 Yanbei Chen, Xiatian Zhu, Shaogang Gong

To tackle this limitation, we propose a novel Instance-Guided Context Rendering scheme, which transfers the source person identities into diverse target domain contexts to enable supervised re-id model learning in the unlabelled target domain.

Image Generation Person Re-Identification

Multiband gravitational-wave event rates and stellar physics

1 code implementation31 Jan 2019 Davide Gerosa, Sizheng Ma, Kaze W. K. Wong, Emanuele Berti, Richard O'Shaughnessy, Yanbei Chen, Krzysztof Belczynski

We use population synthesis simulations of isolated binary stars to explore some of the stellar physics that could be constrained with multiband events, and we show that specific formation pathways might be overrepresented in multiband events compared to ground-only detections.

High Energy Astrophysical Phenomena General Relativity and Quantum Cosmology

Semi-Supervised Deep Learning with Memory

1 code implementation ECCV 2018 Yanbei Chen, Xiatian Zhu, Shaogang Gong

We consider the semi-supervised multi-class classification problem of learning from sparse labelled and abundant unlabelled training data.

General Classification Multi-class Classification +1

Deep Association Learning for Unsupervised Video Person Re-identification

1 code implementation22 Aug 2018 Yanbei Chen, Xiatian Zhu, Shaogang Gong

In this work, to address the video person re-id task, we formulate a novel Deep Association Learning (DAL) scheme, the first end-to-end deep learning method using none of the identity labels in model initialisation and training.

Association Unsupervised Person Re-Identification +1

Optimizing LIGO with LISA forewarnings to improve black-hole spectroscopy

no code implementations29 Jun 2018 Rhondale Tso, Davide Gerosa, Yanbei Chen

The early inspiral of massive stellar-mass black-hole binaries merging in LIGO's sensitivity band will be detectable at low frequencies by the upcoming space mission LISA.

General Relativity and Quantum Cosmology High Energy Astrophysical Phenomena

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