InsAT: Instance-aware Semantic Alignment and Transfer from Human–Object Keypoints for Zero-to-Few-shot Action Understanding (2026.acl-long)
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| Challenge: | Existing zero-shot (ZS) approaches emphasize human motion while underutilizing contextual information, particularly human–object interactions. |
| Approach: | They propose a framework for ZS recognition and zero-to-few-shot adaptation that leverages instance-level language descriptions. |
| Outcome: | The proposed framework outperforms keypoint-based ZS methods while remaining data-efficient and robust. |
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| Challenge: | Existing approaches to cross-lingual Named Entity Recognition focus on Latin script language (LSL) for non-Latin script language, performance often degrades due to deep structural differences. |
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Decoupling Structure and Lexicon for Zero-Shot Semantic Parsing (D18-1)
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| Challenge: | Existing methods for training semantic parsers in new domains require expensive supervision and lack the ability to generalize to new domain. |
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OpenFMNav: Towards Open-Set Zero-Shot Object Navigation via Vision-Language Foundation Models (2024.findings-naacl)
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| Challenge: | Existing methods for object navigation are limited to household datasets with close-set objects, and they lack the ability to generalize to new environments in a zero-shot manner. |
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AlignRE: An Encoding and Semantic Alignment Approach for Zero-Shot Relation Extraction (2024.findings-acl)
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| Challenge: | Existing prototype-based methods for ZSRE ignore abundant side information and suffer from a significant encoding gap between prototypes and sentences. |
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| Challenge: | Existing LLMs cannot generalize to domain-specific parsing tasks in a zero-shot setting. |
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Text2Model: Text-based Model Induction for Zero-shot Image Classification (2024.findings-emnlp)
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| Challenge: | Existing approaches to zero-shot learning are limited in two ways: Query-dependence and richness of language description. |
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Z-GMOT: Zero-shot Generic Multiple Object Tracking (2024.findings-naacl)
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Kim Tran, Anh Duy Le Dinh, Tien-Phat Nguyen, Thinh Phan, Pha Nguyen, Khoa Luu, Donald Adjeroh, Gianfranco Doretto, Ngan Le
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| Challenge: | Recent studies on zero-shot and few-shot stance detection neglect implicit yet semantically important targets. |
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Look-up and Adapt: A One-shot Semantic Parser (D19-1)
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| Challenge: | Current conversational agents such as Siri, Alexa or Google Assistant do not cater to the specific phrasing of a user or the specific action. |
| Approach: | They propose a semantic parser that generalizes to out-of-domain examples by adapting the logical forms of seen utterances to fit an unseen utterant. |
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Zero-Shot Semantic Parsing for Instructions (P19-1)
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| Challenge: | Recent years have seen an increasing number of applications that have a natural language interface, such as chatbots or "intelligent personal assistants" |
| Approach: | They propose a new training algorithm that trains a semantic parser on examples from a set of source domains and augment it with features and a logical form candidate filtering logic to support zero-shot adaptation. |
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