Sep 2026· Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence· pp. 1333-1341· 0 citations· 42 references
TL;DR
MLDA is introduced, a test-time adaptation framework for CZSL that jointly derives multi-level prototypes and performs dynamic vision–language alignment and leverages optimal transport to achieve consistent matching of semantically salient information across modalities.
Abstract
Compositional Zero-Shot Learning (CZSL) aims to recognize novel attribute–object compositions by leveraging shared knowledge from seen primitives. While recent works have begun to incorporate test-time adaptation to mitigate performance degradation caused by label-space distribution shifts, they suffer from primitive-level knowledge underutilization and cross-modal semantic asymmetry, hindering robust understanding of novel compositions. To address these issues, we introduce MLDA, a test-time adaptation framework for CZSL that jointly derives multi-level prototypes and performs dynamic vision–language alignment. Specifically, we construct and progressively update composition- and primitive-level prototype sets to accumulate structured semantic knowledge. By augmenting test inputs with diverse visual perspectives and enriched compositional descriptions, MLDA leverages optimal transport to achieve consistent matching of semantically salient information across modalities. Furthermore, composition activation scores are dynamically updated over the test stream to emphasize informative compositions and suppress irrelevant ones. Extensive experiments are conducted to verify the effectiveness of the proposed method. The code is available at https://github.com/keepgoingjkg/MLDA.
DIFFCZSL is proposed, a diffusion-augmented framework that injects generative priors from pre-trained diffusion models into CLIP-based CZSL pipelines and highlights the complementary strengths of generative diffusion representations and discriminative vision-language models for compositional generalization.
Hangyu Tian, Zhenqi He, Yanghao Wang et al.· 0 citations
While generative models have become a standard approach for addressing the semantic-to-visual gap in Generalized Zero-Shot Learning (GZSL), existing architectures often struggle with two persistent limitations: cross-modal interference during condition fusion and severe overfitting to the visual distributions of seen classes. To address these bottlenecks, this paper introduces SemanticFlowNet, a framework based on Decoupled Semantic Flow Matching. Specifically, we propose a Decoupled Multi-modal Conditioning mechanism that relies on channel-wise concatenation of temporal encodings, semantic attributes, and visual contexts, which preserves the orthogonal subspaces of each modality and reduces interference. Additionally, we integrate a Dropout-enhanced Adaptive Layer Normalization (AdaLN) module to perturb the rigid memorization of seen classes, utilizing stochastic dropout within the state evolution to simulate the distributional variance of unseen domains. Finally, a Time-Aware Dynamic Reconstruction Penalty is introduced to enforce progressively stricter semantic alignment as the generative ordinary differential equation (ODE) trajectory converges to the target manifold. Evaluations on the CUB, SUN, and AWA2 benchmarks demonstrate the effectiveness of the proposed framework. Notably, SemanticFlowNet achieves a harmonic mean of 77.90% on the CUB dataset in the single-seed full-model setting, providing a competitive baseline for generative GZSL applications.
Chu-Yang Song, Mingyi Song, Yang Liu et al.· Electronics· 0 citations
Compared to prior CLIP-enhancement methods, MLLMCLIP achieves state-of-the-art compositional accuracy while delivering consistent gains on standard zero-shot classification and image-text retrieval, showing that feature-level distillation strengthens both compositional and general vision-language representation capability.
Jongsuk Kim, Qiyu Wu, Zhuoyuan Mao et al.· 0 citations
This work proposes a principled VLM TTA method called \algname, and theoretically reveals that the InfoNCE loss can be neatly reformulated as a Wasserstein OT formulation, thereby unifying the objectives of the inference and adaptation of VLMs to achieve their mutual benefits.
Qi Yu, Zhichen Zeng, Katherine Tieu et al.· 0 citations
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 new perspective that views primitive variations as the context-driven activation of concrete visual cues rather than independent entities. Based on it, we propose CLEAR, a CLoze-style rEAsoning-based Re-ranking framework inspired by human perceptual processes. CLEAR extracts conditional variants from the primitive candidate set in a coarse-to-fine manner, performs cloze-style reasoning to infer high-level semantics, and re-ranks predictions to correct biases toward salient concrete primitives. Extensive experiments demonstrate that CLEAR consistently improves the Base Model and outperforms state-of-the-art methods on the challenging C-GQA and MIT-States datasets. Code is available at https://github.com/buptLwz/CLEAR.
In CoDA, a new adaptation framework that explicitly disentangles and coordinates cross-modal semantic alignment and intra-modal structural consistency is proposed, and it is shown that CoDA outperforms state-of-the-art parameter-efficient methods, particularly under few-shot learning and distribution-shift scenarios.
Yi Zhang, Rui Zhu, Chan-Ni Li et al.· Proceedings of the Thirty-Fi...· 0 citations
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