Papers with ZS
MLLM-I2W: Harnessing Multimodal Large Language Model for Zero-Shot Composed Image Retrieval (2025.coling-main)
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| Challenge: | Existing methods for combining image retrieval are supervised and zero-shot . however, the challenge of mapping pseudo-words to images within the joint image-text embedding space is still a challenge. |
| Approach: | They propose a novel image-text mapping network which converts description-related image information into pseudo-word markers for precise ZS-CIR. |
| Outcome: | The proposed model improves on COCO, CIRR, and Fashion-IQ benchmarks. |
Training Dynamics for Curriculum Learning: A Study on Monolingual and Cross-lingual NLU (2022.emnlp-main)
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| Challenge: | Current approaches for NLU use CL to improve in-distribution data performance via heuristic-oriented or task-agnostic difficulties. |
| Approach: | They propose to use CL to improve in-distribution data performance by taking advantage of training dynamics as difficulty metrics instead of heuristic-oriented or task-agnostic difficulties. |
| Outcome: | The proposed model schedulers improve on in-distribution, out-of-distortion and zero-shot cross-lingual transfer datasets while being 20% faster on average. |
Towards a Better Understanding of Variations in Zero-Shot Neural Machine Translation Performance (2023.emnlp-main)
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| Challenge: | Prior work has investigated causes of poor zero-shot performance, but new study suggests it does not exhibit poor zero shot capability. |
| Approach: | They propose to investigate the presence of significant variations in zero-shot performance . target-side translation quality is most influential factor, with vocabulary overlap impacting zero- shot capabilities . |
| Outcome: | The results show that the target side translation quality is the most influential factor . linguistic properties, such as language family and writing system, play a role . |
Incorporating Structured Representations into Pretrained Vision & Language Models Using Scene Graphs (2023.emnlp-main)
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Roei Herzig, Alon Mendelson, Leonid Karlinsky, Assaf Arbelle, Rogerio Feris, Trevor Darrell, Amir Globerson
| Challenge: | Vision and language models (VLMs) have demonstrated remarkable zero-shot (ZS) performance in a variety of tasks. |
| Approach: | They propose to integrate structured annotations into visual and textual representations to improve VLMs' understanding of compositional scenes. |
| Outcome: | The proposed method improves VLMs on multiple VL datasets with only a mild degradation in ZS capabilities. |
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. |