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Marcel Worring

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8 papers

2 author rows

AAAI Conference 2026 Conference Paper

Veli: Unsupervised Method and Unified Benchmark for Low-Cost Air Quality Sensor Correction

Yahia Dalbah
Marcel Worring
Yen-Chia Hsu

Urban air pollution is a major health crisis causing millions of premature deaths annually, underscoring the urgent need for accurate and scalable monitoring of air quality (AQ). While low-cost sensors (LCS) offer a scalable alternative to expensive reference-grade stations, their readings are affected by drift, calibration errors, and environmental interference. To address these challenges, we introduce Veli (Reference free Variational Estimation via Latent Inference), an unsupervised Bayesian model that leverages variational inference to correct LCS readings without requiring co-location with reference stations, eliminating a major deployment barrier. Specifically, Veli constructs a disentangled representation of the LCS readings, effectively separating the true pollutant reading from the sensor noise. To build our model and address the lack of standardized benchmarks in AQ monitoring, we also introduce the Air Quality Sensor Data Repository (AQ-SDR). AQ-SDR is the largest AQ sensor benchmark to date, with readings from 23,737 LCS and reference stations across multiple regions. Veli demonstrates strong generalization across both in-distribution and out-of-distribution settings, effectively handling sensor drift and erratic sensor behavior. Appendices are available in the extended version.

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ICLR Conference 2025 Conference Paper

TULIP: Token-length Upgraded CLIP

Ivona Najdenkoska
Mohammad Mahdi Derakhshani
Yuki M. Asano
Nanne van Noord
Marcel Worring
Cees G. M. Snoek

We address the challenge of representing long captions in vision-language models, such as CLIP. By design these models are limited by fixed, absolute positional encodings, restricting inputs to a maximum of 77 tokens and hindering performance on tasks requiring longer descriptions. Although recent work has attempted to overcome this limit, their proposed approaches struggle to model token relationships over longer distances and simply extend to a fixed new token length. Instead, we propose a generalizable method, named TULIP, able to upgrade the token length to any length for CLIP-like models. We do so by improving the architecture with relative position encodings, followed by a training procedure that (i) distills the original CLIP text encoder into an encoder with relative position encodings and (ii) enhances the model for aligning longer captions with images. By effectively encoding captions longer than the default 77 tokens, our model outperforms baselines on cross-modal tasks such as retrieval and text-to-image generation. The code repository is available at https://github.com/ivonajdenkoska/tulip.

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NeurIPS Conference 2024 Conference Paper

GO4Align: Group Optimization for Multi-Task Alignment

Jiayi Shen
Qi Wang
Zehao Xiao
Nanne van Noord
Marcel Worring

This paper proposes GO4Align, a multi-task optimization approach that tackles task imbalance by explicitly aligning the optimization across tasks. To achieve this, we design an adaptive group risk minimization strategy, comprising two techniques in implementation: (i) dynamical group assignment, which clusters similar tasks based on task interactions; (ii) risk-guided group indicators, which exploit consistent task correlations with risk information from previous iterations. Comprehensive experimental results on diverse benchmarks demonstrate our method's performance superiority with even lower computational costs.

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TMLR Journal 2024 Journal Article

Intriguing Properties of Hyperbolic Embeddings in Vision-Language Models

Sarah Ibrahimi
Mina Ghadimi Atigh
Nanne van Noord
Pascal Mettes
Marcel Worring

Vision-language models have in short time been established as powerful networks, demonstrating strong performance on a wide range of downstream tasks. A key factor behind their success is the learning of a joint embedding space where pairs of images and textual descriptions are contrastively aligned. Recent work has explored the geometry of the joint embedding space, finding that hyperbolic embeddings provide a compelling alternative to the commonly used Euclidean embeddings. Specifically, hyperbolic embeddings yield improved zero-shot generalization, better visual recognition, and more consistent semantic interpretations. In this paper, we conduct a deeper study into the hyperbolic embeddings and find that they open new doors for vision-language models. In particular, we find that hyperbolic vision-language models provide spatial awareness that Euclidean vision-language models lack, are better capable of dealing with ambiguity, and effectively discriminate between distributions. Our findings shed light on the greater potential of hyperbolic embeddings in large-scale settings, reaching beyond conventional down-stream tasks. Our code is available at https://github.com/saibr/hypvl

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NeurIPS Conference 2023 Conference Paper

Episodic Multi-Task Learning with Heterogeneous Neural Processes

Jiayi Shen
Xiantong Zhen
Qi Wang
Marcel Worring

This paper focuses on the data-insufficiency problem in multi-task learning within an episodic training setup. Specifically, we explore the potential of heterogeneous information across tasks and meta-knowledge among episodes to effectively tackle each task with limited data. Existing meta-learning methods often fail to take advantage of crucial heterogeneous information in a single episode, while multi-task learning models neglect reusing experience from earlier episodes. To address the problem of insufficient data, we develop Heterogeneous Neural Processes (HNPs) for the episodic multi-task setup. Within the framework of hierarchical Bayes, HNPs effectively capitalize on prior experiences as meta-knowledge and capture task-relatedness among heterogeneous tasks, mitigating data-insufficiency. Meanwhile, transformer-structured inference modules are designed to enable efficient inferences toward meta-knowledge and task-relatedness. In this way, HNPs can learn more powerful functional priors for adapting to novel heterogeneous tasks in each meta-test episode. Experimental results show the superior performance of the proposed HNPs over typical baselines, and ablation studies verify the effectiveness of the designed inference modules.

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ICLR Conference 2023 Conference Paper

Meta Learning to Bridge Vision and Language Models for Multimodal Few-Shot Learning

Ivona Najdenkoska
Xiantong Zhen
Marcel Worring

Multimodal few-shot learning is challenging due to the large domain gap between vision and language modalities. Existing methods are trying to communicate visual concepts as prompts to frozen language models, but rely on hand-engineered task induction to reduce the hypothesis space. To make the whole process learnable, we introduce a multimodal meta-learning approach. Specifically, our approach decomposes the training of the model into a set of related multimodal few-shot tasks. We define a meta-mapper network, acting as a meta-learner, to efficiently bridge frozen large-scale vision and language models and leverage their already learned capacity. By updating the learnable parameters only of the meta-mapper, it learns to accrue shared meta-knowledge among these tasks. Thus, it can rapidly adapt to newly presented samples with only a few gradient updates. Importantly, it induces the task in a completely data-driven manner, with no need for a hand-engineered task induction. We evaluate our approach on recently proposed multimodal few-shot benchmarks, measuring how rapidly the model can bind novel visual concepts to words and answer visual questions by observing only a limited set of labeled examples. The experimental results show that our meta-learning approach outperforms the baseline across multiple datasets and various training settings while being computationally more efficient.

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NeurIPS Conference 2022 Conference Paper

Association Graph Learning for Multi-Task Classification with Category Shifts

Jiayi Shen
Zehao Xiao
Xiantong Zhen
Cees Snoek
Marcel Worring

In this paper, we focus on multi-task classification, where related classification tasks share the same label space and are learned simultaneously. In particular, we tackle a new setting, which is more realistic than currently addressed in the literature, where categories shift from training to test data. Hence, individual tasks do not contain complete training data for the categories in the test set. To generalize to such test data, it is crucial for individual tasks to leverage knowledge from related tasks. To this end, we propose learning an association graph to transfer knowledge among tasks for missing classes. We construct the association graph with nodes representing tasks, classes and instances, and encode the relationships among the nodes in the edges to guide their mutual knowledge transfer. By message passing on the association graph, our model enhances the categorical information of each instance, making it more discriminative. To avoid spurious correlations between task and class nodes in the graph, we introduce an assignment entropy maximization that encourages each class node to balance its edge weights. This enables all tasks to fully utilize the categorical information from related tasks. An extensive evaluation on three general benchmarks and a medical dataset for skin lesion classification reveals that our method consistently performs better than representative baselines.

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NeurIPS Conference 2021 Conference Paper

Variational Multi-Task Learning with Gumbel-Softmax Priors

Jiayi Shen
Xiantong Zhen
Marcel Worring
Ling Shao

Multi-task learning aims to explore task relatedness to improve individual tasks, which is of particular significance in the challenging scenario that only limited data is available for each task. To tackle this challenge, we propose variational multi-task learning (VMTL), a general probabilistic inference framework for learning multiple related tasks. We cast multi-task learning as a variational Bayesian inference problem, in which task relatedness is explored in a unified manner by specifying priors. To incorporate shared knowledge into each task, we design the prior of a task to be a learnable mixture of the variational posteriors of other related tasks, which is learned by the Gumbel-Softmax technique. In contrast to previous methods, our VMTL can exploit task relatedness for both representations and classifiers in a principled way by jointly inferring their posteriors. This enables individual tasks to fully leverage inductive biases provided by related tasks, therefore improving the overall performance of all tasks. Experimental results demonstrate that the proposed VMTL is able to effectively tackle a variety of challenging multi-task learning settings with limited training data for both classification and regression. Our method consistently surpasses previous methods, including strong Bayesian approaches, and achieves state-of-the-art performance on five benchmark datasets.

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