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From Model Patterns to Abstract Semantics in Compositional Zero-Shot Learning

Paper recorded by Signals 4 on 2026-09-14 in cs.CV. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-14 on arXiv · recorded by Signals 4 on 2026-09-15

Category: cs.CV · 计算机视觉 · first seen 2026-09-15

Abstract

Compositional Zero Shot Learning aims to recognize unseen compositions by recombining learned primitives. Recent methods rely on vision language models and attempt to explicitly model contextual variations of primitives through multiple representations. However, such approaches are limited by fixed variant capacity and competition between abstract and concrete semantics. In this work, we present a

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#61 most recent of 237 cs.CV papers we have recorded · ↑ newer: Kaininja: Extending Native 3D Generators to the Part Level · ↓ older: SNAP3D: Physically Grounded 3D Parts for Assembly from a Single Image
Cite this page: From Model Patterns to Abstract Semantics in Compositional Zero-Shot Learning: the #61 most recent of 237 cs.CV papers we have recorded (as of 2026-09-14). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/from-model-patterns-to-abstract-semantics-in-compositional-zero-shot-learning.html
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