Papers by L. D. M. S. Sai Teja
AGIC: Attention-Guided Image Captioning to Improve Caption Relevance (2026.findings-eacl)
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| Challenge: | Existing methods for image captioning generate generic captions that are limited in capturing nuanced visual details. |
| Approach: | They propose attention-guided image captioning which amplifies visual regions directly in the feature space to guide caption generation. |
| Outcome: | The proposed approach matches or surpasses state-of-the-art models while achieving faster inference. |
DAMASHA: Detecting AI in Mixed Adversarial Texts via Segmentation with Human-interpretable Attribution (2026.findings-eacl)
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| Challenge: | a new framework for mixed authorship detection addresses the challenge of segmenting mixed-authorship text . mixed-authored text detection is a growing concern in the age of advanced large language models . a recent survey highlighted the greater challenges of detecting AI content in realworld settings . |
| Approach: | They propose a framework for mixed authorship detection that integrates stylometric cues, perplexity-driven signals, and structured boundary modeling to accurately segment collaborative human-AI content. |
| Outcome: | The proposed framework improves robustness against adversarial perturbations while revealing limitations. |