Bill Psomas
@billpsomas.bsky.social
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/
📍Prague, CZ. 🔗 http://users.ntua.gr/psomasbill/
⬇️ Grab i-CIR, run your method, tell us how it handles instance-level composed image retrieval.
📄 arxiv.org/abs/2510.25387
🧪 github.com/billpsomas/i...
George Retsinas, @nikos-efth.bsky.social, Panagiotis Filntisis, Yannis Avrithis, Petros Maragos, Ondrej Chum, @gtolias.bsky.social.
📄 arxiv.org/abs/2510.25387
🧪 github.com/billpsomas/i...
George Retsinas, @nikos-efth.bsky.social, Panagiotis Filntisis, Yannis Avrithis, Petros Maragos, Ondrej Chum, @gtolias.bsky.social.
Instance-Level Composed Image Retrieval
The progress of composed image retrieval (CIR), a popular research direction in image retrieval, where a combined visual and textual query is used, is held back by the absence of high-quality training...
arxiv.org
November 6, 2025 at 12:08 PM
⬇️ Grab i-CIR, run your method, tell us how it handles instance-level composed image retrieval.
📄 arxiv.org/abs/2510.25387
🧪 github.com/billpsomas/i...
George Retsinas, @nikos-efth.bsky.social, Panagiotis Filntisis, Yannis Avrithis, Petros Maragos, Ondrej Chum, @gtolias.bsky.social.
📄 arxiv.org/abs/2510.25387
🧪 github.com/billpsomas/i...
George Retsinas, @nikos-efth.bsky.social, Panagiotis Filntisis, Yannis Avrithis, Petros Maragos, Ondrej Chum, @gtolias.bsky.social.
A method for i-CIR and CIR in general:
⚡BASIC: training-free pipeline (centering, projection with PCA, textual contextualization, Harris-style fusion) with strong results across i-CIR and class-level CIR benchmarks.
⚡BASIC: training-free pipeline (centering, projection with PCA, textual contextualization, Harris-style fusion) with strong results across i-CIR and class-level CIR benchmarks.
November 6, 2025 at 12:07 PM
A method for i-CIR and CIR in general:
⚡BASIC: training-free pipeline (centering, projection with PCA, textual contextualization, Harris-style fusion) with strong results across i-CIR and class-level CIR benchmarks.
⚡BASIC: training-free pipeline (centering, projection with PCA, textual contextualization, Harris-style fusion) with strong results across i-CIR and class-level CIR benchmarks.
Compact ⚖️ but hard 🔥:
📊~750K images, 202 instances, ~1,900 composed queries. Despite small per-query DBs (~3.7K images), i-CIR matches the difficulty of searching with >40M random distractors.
📊~750K images, 202 instances, ~1,900 composed queries. Despite small per-query DBs (~3.7K images), i-CIR matches the difficulty of searching with >40M random distractors.
November 6, 2025 at 12:05 PM
Compact ⚖️ but hard 🔥:
📊~750K images, 202 instances, ~1,900 composed queries. Despite small per-query DBs (~3.7K images), i-CIR matches the difficulty of searching with >40M random distractors.
📊~750K images, 202 instances, ~1,900 composed queries. Despite small per-query DBs (~3.7K images), i-CIR matches the difficulty of searching with >40M random distractors.
How i-CIR is structured:
🗂️ Per instance we share a database and define:
- composed positives (same object + modification)
- hard negatives:
- visual (same/similar object, wrong text)
- textual (right text, wrong instance)
- composed (near-miss on both).
🗂️ Per instance we share a database and define:
- composed positives (same object + modification)
- hard negatives:
- visual (same/similar object, wrong text)
- textual (right text, wrong instance)
- composed (near-miss on both).
November 6, 2025 at 12:04 PM
How i-CIR is structured:
🗂️ Per instance we share a database and define:
- composed positives (same object + modification)
- hard negatives:
- visual (same/similar object, wrong text)
- textual (right text, wrong instance)
- composed (near-miss on both).
🗂️ Per instance we share a database and define:
- composed positives (same object + modification)
- hard negatives:
- visual (same/similar object, wrong text)
- textual (right text, wrong instance)
- composed (near-miss on both).
Why this matters:
🔎 Gap in the community: Existing CIR benchmarks are class-level, ambiguous, without explicit hard negatives, and often reward text-only behaviour. We needed a dataset that truly requires both image and text, at the instance level. i-CIR fills that gap.
🔎 Gap in the community: Existing CIR benchmarks are class-level, ambiguous, without explicit hard negatives, and often reward text-only behaviour. We needed a dataset that truly requires both image and text, at the instance level. i-CIR fills that gap.
November 6, 2025 at 12:03 PM
Why this matters:
🔎 Gap in the community: Existing CIR benchmarks are class-level, ambiguous, without explicit hard negatives, and often reward text-only behaviour. We needed a dataset that truly requires both image and text, at the instance level. i-CIR fills that gap.
🔎 Gap in the community: Existing CIR benchmarks are class-level, ambiguous, without explicit hard negatives, and often reward text-only behaviour. We needed a dataset that truly requires both image and text, at the instance level. i-CIR fills that gap.
done! thanks for the suggestions, Vangeli :D
May 7, 2025 at 2:17 PM
done! thanks for the suggestions, Vangeli :D