@billpsomas
MSCA Postdoctoral Fellow @ Visual Recognition Group, CTU in Prague. Deep Learning for Computer Vision. Former IARAI, Inria, Athena RC intern. Photographer. Crossfit freak. 📍Prague, CZ. 🔗 http://users.ntua.gr/psomasbill/
Retrieve and Segment: Are a Few Examples Enough to Bridge the Supervision Gap in Open-Vocabulary Segmentation?
Tilemachos Aravanis @stojnicv.xyz @billpsomas.bsky.social Nikos Komodakis @gtolias.bsky.social
tl;dr: almost yes if use 1-3 images, no if more(fig 6)
arxiv.org/abs/2602.23339
#CVPR2026
🎉🎉🎉
Excited to share that our paper "Global-Aware Edge Prioritization for Pose Graph Initialization" has been accepted to CVPR 2026! #CVPR2026 See you soon in Denver!🥳🥳 Code is coming soon🚧
❓How would you do an accurate and efficient pose graph initialization in a global manner? arxiv.org/abs/2602.21963
Global-Aware Edge Prioritization for Pose Graph Initialization
@weitong8591.bsky.social, @gtolias.bsky.social, Jiri Matas, @danielbarath.bsky.social
tl;dr: rank pose graph edges->global consistency->improve SfM
arxiv.org/abs/2602.21963
Sleeping while waiting on an “anywhere in the world” paper decision release. #CVPR2026
8/8 Resources 📄
Paper: arxiv.org/abs/2506.10178
Code: github.com/billpsomas/e...
Joint work with: Dionysis Christopoulos,@eirinibaltzi.bsky.social,@ikakogeorgiou.bsky.social, @tim-arav.bsky.social,Nikos Komodakis,Konstantinos Karantzalos,Yannis Avrithis,@gtolias.bsky.social.
See you @ ICLR 2026🇧🇷
7/n Take-home messages 💡
EP:
- Plug-and-play.
- Compatible with all pre-training families.
- Unlocks the potential of encoders optimized for local representations.
- Complementary with PEFT.
- Better to have it, than not to have it. 👀
6/n EP + PEFT = 🔥
- EP captures information that LoRA alone does not, and vice versa.
- LoRA+EP improves over both pure EP and pure LoRA.
📌 Example: a LoRA+EP configuration with 250K params reaches 72%, 4.3% above linear probing (67.7%), while using over 3× fewer parameters.
5/n Interpretability 🔍
- EP queries specialize in distinct spatial regions.
- Attention maps are complementary.
- Semantic correspondences emerge (e.g. tails, feet).
- Verified quantitatively too.
4/n Designed for local representations🧩
📊 Across ImageNet-1K:
- Consistent gains over k-NN and Linear Probing (LP).
- Particularly strong improvements for MIM, VL, and generative.
- Minimal overhead.
3/n Core observation ⚙️
Prior attentive probing uses redundant projections.
🔍 Introducing Efficient Probing (EP):
📌 Multi-query cross-attention.
🔌 Plug-and-play on top of frozen encoders.
💸 Lightweight and parameter-efficient.
2/n Why revisit probing? 🤔
- Linear probing underestimates encoders optimizing local representations.
- Full fine-tuning is costly at scale.
- Attentive probing helps, yet methods are over-parametrized and not well-studied.
👉 Can we get attention benefits without that much overhead?
1/n Attention, Please! 🚀
Our work “Revisiting Attentive Probing Through the Lens of Efficiency” has been accepted at #ICLR2026.
We introduce Efficient Probing (EP) — a lightweight, multi-query attentive probing method for frozen encoders.
Paper + code at the end 👇
BBoxMaskPose v2: Expanding Mutual Conditioning to 3D
Miroslav Purkrabek, Constantin Kolomiiets, Jiri Matas
tl;dr: sota in human pose estimations, especially for the hard cases
arxiv.org/abs/2601.15200
Would love to try
Best promo anyone could make for this position 👏🏾🏰 And, amazingly, everything said is true 🎆
I have an opening for a two years post-doc position on instance-level (personalized) visual generation. Eligibility: (i) <=7 years from Ph.D. (ii) studies or 1 year outside of Czechia (ii) >=3 journal with IF or CORE A*/A conference papers. Deadline: 15 Feb.
Details: www.euraxess.cz/jobs/399390
🚀New task: Instance-level Image+Text→Image Retrieval
🔎Given a query image + an edit (“during night”), retrieve the same specific instance after the change — not just any similar object.
🛢New dataset on HF: i-CIR huggingface.co/datasets/bil...
🔥Download, run, and share results!
12/12 Joint work with Giorgos Petsangourakis, Christos Sgouropoulos, Theodoros Giannakopoulos, Giorgos Sfikas, @ikakogeorgiou.bsky.social.
11/n Summary🏁
REGLUE shows that the way we leverage VFM semantics matters for diffusion. Combining compact local semantics with global context yields faster convergence and state-of-the-art image generation.
📄arXiv: arxiv.org/abs/2512.16636
💻Project: reglueyourlatents.github.io
10/n Faster convergence🔥
REGLUE (SiT-B/2) achieves 12.9 and 28.7 FID at 400K iterations in conditional and unconditional generation, respectively, outperforming REPA, ReDi, and REG. REGLUE (SiT-XL/2) matches 1M-step SOTA performance in just 700k iterations (~30% fewer steps).
9/n Alignment effects ⚓
External alignment complements joint modeling, but its benefits depend on the signal. Local alignment yields consistent gains, whereas global-only alignment can degrade performance. Spatial joint modeling remains the primary driver.
8/n Local > Global Semantics🧩
Our analysis shows that jointly modeling with patch-level semantics drives most gains. The global [CLS] helps, but fine-grained spatial features deliver a strongly larger FID improvement, highlighting the importance of local structure for diffusion.
7/n Semantic preservation under compression📉
Do compressed patch features retain VFM semantics?
Points show frozen compressed DINOv2 semantics (x: ImageNet top-1 / Cityscapes mIoU) vs SiT-B generation quality (y: ImageNet FID) when trained on VAE latents + compressed features.
6/n Non-linear compression matters 💎
Linear PCA can limit patch-level semantics (e.g., ReDi). We introduce a lightweight non-linear semantic compressor that aggregates multi-layer VFM features into a compact, semantics-preserving space, boosting quality (21.4 → 13.3 FID).
5/n Our method 🧠
REGLUE puts these into one unified model and jointly models:
1️⃣ VAE latents (pixels)
2️⃣ local semantics (compressed patch features)
3️⃣ global [CLS] (concept)
➕ alignment loss as a complementary auxiliary boost.
4/n Main insight 💡
Jointly modeling compressed patch-level semantics ➕ VAE latents provides spatial guidance and yields larger gains than alignment-only (REPA) or global-only (REG).
Alignment loss and a global [CLS] token stay complementary, orthogonal signals.
3/n Key design choice 🧩 Compact spatial semantics matter!
To leverage VFMs effectively, diffusion should jointly model VAE latents with multi-layer VFM spatial (patch-level) semantics, via a compact, non-linearly compressed representation.
2/n More semantics are needed! ➕
Existing joint modeling and external alignment approaches (e.g., REPA, REG) inject only a “narrow slice” of VFM features into diffusion. We argue richer semantics are needed to unlock their full potential.
1/n REGLUE Your Latents! 🚀
We introduce REGLUE: a unified framework that entangles VAE latents ➕ Global ➕ Local semantics for faster, higher-fidelity image generation.
Links (paper + code) at the end👇