ULAO, a CLIP-based framework with object-first sequential prediction and prediction-based hard negative contrastive training, reports state-of-the-art compositional zero-shot learning results on MIT-States, UT-Zappos, and C-GQA.
Zero-Shot Compositional Concept Learning
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abstract
In this paper, we study the problem of recognizing compositional attribute-object concepts within the zero-shot learning (ZSL) framework. We propose an episode-based cross-attention (EpiCA) network which combines merits of cross-attention mechanism and episode-based training strategy to recognize novel compositional concepts. Firstly, EpiCA bases on cross-attention to correlate concept-visual information and utilizes the gated pooling layer to build contextualized representations for both images and concepts. The updated representations are used for a more in-depth multi-modal relevance calculation for concept recognition. Secondly, a two-phase episode training strategy, especially the transductive phase, is adopted to utilize unlabeled test examples to alleviate the low-resource learning problem. Experiments on two widely-used zero-shot compositional learning (ZSCL) benchmarks have demonstrated the effectiveness of the model compared with recent approaches on both conventional and generalized ZSCL settings.
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cs.CV 1years
2024 1verdicts
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Compositional Zero-Shot Learning with Contextualized Cues and Adaptive Contrastive Training
ULAO, a CLIP-based framework with object-first sequential prediction and prediction-based hard negative contrastive training, reports state-of-the-art compositional zero-shot learning results on MIT-States, UT-Zappos, and C-GQA.