We just released our #ECCV2026 paper on Model Merging for Computer Vision 🎓 arxiv.org/abs/2604.12935 Joint work w @pdejorge.bsky.social Cesar De Souza @bjoernmichele.bsky.social @mbsariyildiz.bsky.social @weinzaepfelp.bsky.social Florent Perronnin & @skamalas.bsky.social See P…
Task Alignment: A Simple Proxy for Practical Model Merging Across Diverse Vision Tasks arxiv.org
AI Weekly's analysis
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- A NAVER Labs Europe paper accepted at ECCV 2026 introduces a 'task alignment proxy' for selecting model-merging hyperparameters without training the decoder for each candidate.
- The method targets heterogeneous vision tasks with trainable decoders, moving beyond the CLIP image classification setup that dominated prior model merging work.
- The authors claim the proxy speeds up hyperparameter selection by orders of magnitude while retaining downstream performance.
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Our #ECCV2026 IDeaL paper tackles Data-Free Multi-Teacher Distillation 🎓 arxiv.org/abs/2608.24759 see Bülent's thread below 🔽 joint work w @fyavuz1.bsky.social @mbsariyildiz.bsky.social at @naverlabseurope.bsky.social
IDeaL: Data-Free Multi-Teacher Distillation via Improved Dead Leaves arxiv.org
AI Weekly's analysis
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- IDeaL generates teacher-specific synthetic samples using decorrelation losses at patch and image levels, removing the need for the teachers' original training images.
- With a 1K-image budget, students trained on IDeaL samples match or surpass those trained on a 1K-image subset of ImageNet.
- The paper, by Feyza Yavuz, Mert Bülent Sarıyıldız and Diane Larlus, is accepted at ECCV 2026.
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Project page: jacobchalk.github.io/Whareformer/ Dima's 🧵: bsky.app/profile/dima...
Whareformer: Learning to Track What is Where in Long Egocentric Videos jacobchalk.github.io
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Research Scientist in Visual Representation Learning (multiple positions) F/M - Permanent contract in Meylan | NAVER LABS Europe careers.werecruit.io
