Matteo Dunnhofer

As an Highlight ✨ in the main conference, we will present results of a new investigation on object tracking in first-person vision by comparison against third-person videos. Work done at the MLP lab at the University of Udine led by Christian Micheloni

📆 Oct 21th, 15:00 - 17:00
📍Poster #542

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At the Human-inspired Computer Vision #HiCV2025 workshop, I will present a poster with recent results on comparing video-based ANNs and the primate visual system. Ongoing project at the ViTA lab @yorkuniversity.bsky.social led by @kohitij.bsky.social

📆 Oct 20th, 8:30 - 12:30
📍Room 309

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On my way to #ICCV2025 in Honolulu 🏝️
@iccv.bsky.social

I will share results on ongoing projects I am working on at
@yorkuniversity.bsky.social
and at the University of Udine

Looking forward to discussions!

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Our #ICCV2025 paper will be presented as an Highlight ✨

Joint work with @zairamanigrasso.bsky.social and Christian Micheloni

Funded by PRIN 2022 PNRR, MSCA Actions

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Is Tracking really more challenging in First Person Egocentric Vision? Visual object tracking and segmentation are becoming fundamental tasks for understanding human activities in egocentric vision. Recent research has benchmarked state-of-the-art methods and concluded t...

All details can be found in our paper.

📄 arXiv: arxiv.org/abs/2507.16015
🌐 Webpage: machinelearning.uniud.it/datasets/vista

The VISTA benchmark will be released soon. Stay tuned!

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These FPV-specific challenges include:
- Frequent object disappearances
- Continuous camera motion altering object appearance
- Object distractors
- Wide field-of-view distortions near frame edges

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- Trackers learn viewpoint biases and perform best on the viewpoint used during training.
- FPV tracking presents its specific challenges.

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Key takeaways from our study:

- FPV is challenging for state-of-the-art generalistic trackers.
- Tracking objects in human-object interaction videos is difficult across both first- and third-person viewpoints.

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We specifically examined whether these drops are due to FPV itself or to the complexity of human-object interaction scenarios.

To do this, we designed VISTA, a benchmark using synchronized first and third-person recordings of the same activities.

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Is Tracking really more challenging in First Person Egocentric Vision?

Our new #ICCV2025 paper follows up our IJCV 2023 study (bit.ly/4nVRJw9).

We further investigate the causes of performance drops in object and segmentation tracking under egocentric FPV settings.

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In the past weeks we have been around NETI at @utaustin.bsky.social, @vssmtg.bsky.social 2025, and the CVR-CIAN conf. @yorkuniversity.bsky.social, to discuss early findings on modeling object motion in the macaque visual system by deep neural networks. Details to appear soon! @kohitij.bsky.social

Details: sciencedirect.com/special-issu...

Submissions open: June 1, 2025
Deadline: September 30, 2025

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The teams behind the workshops on Computer Vision for Winter Sports at @wacvconference.bsky.social and on Computer Vision in Sports at @cvprconference.bsky.social have joined forces in the organisation of a special issue in CVIU on topics related to computer vision applications in sports.

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Honored to be on the list this year!

We are now live with the 3d Workshop on Computer Vision for Winter Sports @wacvconference.bsky.social #WACV2025.

Make sure to attend if you are around!

This paper contributes to our projects PRIN 2022 EXTRA EYE and Project PRIN 2022 PNRR TEAM funded by European Union-NextGenerationEU.

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This work was led by Moritz Nottebaum (stop by his poster!) at the Machine Learning and Perception Lab of the University of Udine

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LowFormer achieves significant speedups in image throughput and latency on various hardware platforms, while maintaining or surpassing the accuracy of current state-of-the-art models across image recognition, object detection, and semantic segmentation.

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We used insights from such an analysis to enhance the hardware efficiency of backbones at the macro level, and introduced a slimmed-down version of multi-head self-attention to improve efficiency in the micro design.

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We empirically found out that MACs alone do not accurately account for inference speed.

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LowFormer: Hardware Efficient Design for Convolutional Transformer Backbones Research in efficient vision backbones is evolving into models that are a mixture of convolutions and transformer blocks. A smart combination of both, architecture-wise and component-wise is mandatory...

Today at @wacvconference.bsky.social #WACV2025, we present LowFormer, a new family of convolutional-transformer architectures for computer vision that improve efficiency by optimizing running time on hardware rather than minimizing MAC operations.

arXiv: arxiv.org/abs/2409.03460

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Is attendance open to YorkU researchers (e.g. postdocs)? Would like a lot to learn from your teaching style!

Did the same a few weeks ago in Toronto. I think this is the best pizza flavor you can get in Canada 😂

CV4WS@WACV2025 UPDATE (11/19/24): DEADLINE EXTENDED

The top-performing teams will be invited to present their solution at the 3rd Workshop on Computer Vision for Winter Sports at #WACV2025!

📄 sites.google.com/unitn.it/cv4...

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The challenge platform is hosted on CodaLab and you can find all the submission instructions there.

Deadline for submission is January 31st 2025.

🏆 codalab.lisn.upsaclay.fr/competitions...

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[WACV 2024] Tracking Skiers from the Top to the Bottom - qualitative examples YouTube video by Matteo Dunnhofer

The #SkiTB Visual Tracking Challenge at #WACV2025 is open for submissions!

The goal is to track a skier in a video capturing his/her full performance across multiple video cameras, and it is based on our recently released SkiTB dataset.

🎥 youtu.be/Aos5iKrYM5o

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Would be happy to be added as well :) I am working on visual tracking, currently at YorkU ;)

The outgoing phase of my MSCA project PRINNEVOT has started a few days ago. I am now at the #CentreforVisionResearch of #YorkUniversity.

I am looking forward to the next two years of research at the intersection of computer vision and visual neuroscience! 🤖🧠