Papers with RGB
Ensemble Romanian Dependency Parsing with Neural Networks (L18-1)
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| Challenge: | SSPR is a Python 3.5 application based on the Microsoft Cognitive Toolkit 2.0 Python API. |
| Approach: | a Python 3.5 application is based on the Microsoft Cognitive Toolkit 2.0 Python API. |
| Outcome: | SSPR outperforms the best individual parser at the CONLL 2017 dependency parsing shared task. |
HIRAG: Hierarchical-Thought Instruction-Tuning Retrieval-Augmented Generation (2025.findings-emnlp)
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| Challenge: | In-depth research on the specific capabilities needed by the RAG generation model is lacking, leading to inconsistent document quality and retrieval system imperfections. |
| Approach: | They propose that RAG models should possess three progressively hierarchical abilities: (1) Filtering: the ability to select relevant information; (2) Combination: the capability to combine semantic information across paragraphs; (3) RAG-specific reasoning: the capacity to further process external knowledge using internal knowledge. |
| Outcome: | Experiments show that the proposed method significantly improves the model’s open-book examination capability on datasets such as RGB, PopQA, MuSiQue, HotpotQA, and PubmedQA. |
Dynamic Feature Fusion for Sign Language Translation Using HyperNetworks (2025.findings-naacl)
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| Challenge: | Using RGB and keypoint streams, sign language translation is highly dependent on the brain's ability to process color, shape, and motion simultaneously. |
| Approach: | They propose a hypernetwork-based fusion method that extracts salient features from RGB and keypoint streams and introduces self-distillation and SST contrastive learning to maintain feature advantages while aligning the global semantic space. |
| Outcome: | The proposed method achieves state-of-the-art performance on two public sign language datasets, reducing model parameters by about two-thirds. |