--- title: "Xin1212 at NakbaVirality Shared Task: Frozen CLIP with" description: "This system paper addresses the NakbaVirality shared task. 0.4559 F1. 6 references. Among them: 0." authors: ["Xinyan Zhang", "Bingzhou Yang"] year: 2026 venue: "Proceedings of the 2nd International Workshop on Nakba Narratives as Language Resources @ LREC 2026" source: "https://aclanthology.org/2026.nakbanlp-1.19/" doi: "10.63317/3myy3h8imvfz" licence: "CC BY-NC 4.0 (ELRA, LREC proceedings)" licence_url: "https://creativecommons.org/licenses/by-nc/4.0/" summary_licence: "Elamur Cosmic License" canonical: "https://research.elamur.ai/free/en/papers/zhang-2026-xin1212-nakbavirality-shared-task-frozen/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/zhang-2026-xin1212-nakbavirality-shared-task-frozen/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2026](https://md.research.elamur.ai/free/en/papers/2026/page/57/) / Xin1212 at NakbaVirality Shared Task: Frozen CLIP with Residual Adapter for Multimodal Virality Classification # Xin1212 at NakbaVirality Shared Task: Frozen CLIP with Residual Adapter for Multimodal Virality Classification **Authors:** [Xinyan Zhang](https://md.research.elamur.ai/free/en/authors/xinyan-zhang/) and Bingzhou Yang · **Year:** 2026 · **Venue:** [Proceedings of the 2nd International Workshop on Nakba Narratives as Language Resources @ LREC 2026](https://md.research.elamur.ai/free/en/venues/nakbanlp/) ([International Workshop on Nakba Narratives as Language Resources @ LREC 2026](https://md.research.elamur.ai/free/en/venues/nakbanlp/2026/)) · **Publisher:** [European Language Resources Association (ELRA)](https://md.research.elamur.ai/free/en/publishers/european-language-resources-association-elra/) · **Licence:** [CC BY-NC 4.0 (ELRA, LREC proceedings)](https://creativecommons.org/licenses/by-nc/4.0/) **Links:** [Publisher's page](https://aclanthology.org/2026.nakbanlp-1.19/) · [PDF](https://aclanthology.org/2026.nakbanlp-1.19.pdf) · [DOI 10.63317/3myy3h8imvfz](https://doi.org/10.63317/3myy3h8imvfz) · [OpenAlex](https://openalex.org/W7166506241) **Citations:** 0 citations (OpenAlex, 2026-10-14) ## Summary This system paper addresses the NakbaVirality shared task. The task asks participants to predict how viral a social-media post will be (Low, Medium or High) from its text and attached image. The posts come from X and Reddit and are multilingual and noisy. The labelled data is small and class-imbalanced, so Macro-F1 is the main metric. The authors keep a LAION CLIP encoder completely frozen and concatenate its text and image embeddings. The fused vector passes through a residual two-layer MLP adapter that learns a task-specific correction, and a small MLP head with dropout assigns the final label. Training uses class-weighted cross-entropy with label smoothing. The authors tested a versioned series of models, from Baseline to V9, on a stratified development split. V9 performed best there with 0.5492 Macro-F1, and it scored 0.4559 F1 on the blind test set. The work shows that a lightweight design with a frozen backbone can improve minority-class detection in low-resource multimodal virality prediction, at some cost to plain accuracy. ## Key points - **Backbone:** the encoder is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, kept frozen. - Text and image embeddings are joined by late fusion (concatenation). - A residual adapter then refines the fused vector (h̃ = h + A(h)). - **Data:** 1,691 labelled instances were split 80/20 (stratified, seed 42), giving a 339-example development set. The official blind test set has 872 posts. - **Development results (V9 vs. Baseline):** - Macro-F1 rose from 0.4798 to 0.5492. - Virality-weighted F1 rose from 0.3924 to 0.5252. - Accuracy fell from 0.6401 to 0.5959. - **Official test scores:** F1 0.4559, accuracy 0.6089, precision 0.6000, recall 0.6089. - **Training setup:** - AdamW optimizer, batch size 16, 20 epochs, learning rate 1e-3. - Cosine schedule with warmup. - Label smoothing with ε = 0.1, plus class-weighted loss for the three classes. *Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.* ## Institutions - [Dalian University of Technology](https://md.research.elamur.ai/free/en/universities/dalian-university-of-technology/) — University, Dalian, China · 20 works · 46 citations 📚 Shelves (5): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 18,614 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 17,965 works · [👁️ Computer Vision and Pattern Recognition](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-vision-and-pattern-recognition/) · 3,486 works · [🤝 Social Sciences](https://md.research.elamur.ai/free/en/shelves/social-sciences/) · 1,954 works · [🤝 Sociology and Political Science](https://md.research.elamur.ai/free/en/shelves/social-sciences/sociology-and-political-science/) · 626 works 🧭 Topics (1): [🖼️ Vision & language](https://md.research.elamur.ai/free/en/topics/multimodal/) · 811 works ## Cite Cite as: Xinyan Zhang and Bingzhou Yang. 2026. Xin1212 at NakbaVirality Shared Task: Frozen CLIP with Residual Adapter for Multimodal Virality Classification. Proceedings of the 2nd International Workshop on Nakba Narratives as Language Resources @ LREC 2026. The paper itself, not this page. BibTeX from the library's record of the work. ```bibtex @inproceedings{zhang2026xin1212, title = {{Xin1212 at NakbaVirality Shared Task: Frozen CLIP with Residual Adapter for Multimodal Virality Classification}}, author = {Xinyan Zhang and Bingzhou Yang}, booktitle = {Proceedings of the 2nd International Workshop on Nakba Narratives as Language Resources @ LREC 2026}, year = {2026}, doi = {10.63317/3myy3h8imvfz}, url = {https://doi.org/10.63317/3myy3h8imvfz} } ``` - [← previous XiaoM at SemEval-2026 Task 7: A Qwen-based System for Accurate Retrieval of Everyday Knowledge Across Diverse Languages and Cultures](https://md.research.elamur.ai/free/en/papers/yao-2026-xiaom-semeval-2026-task-7/) - [next → xLM: A Python Package for Non-Autoregressive Language Models](https://md.research.elamur.ai/free/en/papers/patel-2026-xlm-python-package-non-autoregressive/) - [↑ back to papers of 2026](https://md.research.elamur.ai/free/en/papers/2026/page/57/) ## References 6 references, 0 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet. 1. 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