SymTrack is the first systematic detection-free framework for scene text tracking that constructs benchmarks from video text spotting datasets and reports up to 11.97% AUC gains over prior trackers.
arXiv preprint arXiv:2410.12896 , year=
5 Pith papers cite this work, alongside 7 external citations. Polarity classification is still indexing.
5
Pith papers citing it
7
external citations · external index
representative citing papers
KL regularization aligning model predictions with empirical transition patterns improves macro-F1 by 9-42% in next dialogue act prediction on German counselling data and transfers to other datasets.
Plan-and-Act trains a dedicated Planner on synthetic plan-annotated trajectories to generate high-level plans that an Executor follows, reaching 57.58% success on WebArena-Lite and 81.36% on WebVoyager.
Modifying nationality and language parameters in English-centric personas for mental health dialogues introduces clinical inconsistencies across languages and causes LLM judges to perform inaccurately on non-English depression severity assessments.
citing papers explorer
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Beyond Detection: A Structure-Aware Framework for Scene Text Tracking
SymTrack is the first systematic detection-free framework for scene text tracking that constructs benchmarks from video text spotting datasets and reports up to 11.97% AUC gains over prior trackers.
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Transition-Matrix Regularization for Next Dialogue Act Prediction in Counselling Conversations
KL regularization aligning model predictions with empirical transition patterns improves macro-F1 by 9-42% in next dialogue act prediction on German counselling data and transfers to other datasets.
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Plan-and-Act: Improving Planning of Agents for Long-Horizon Tasks
Plan-and-Act trains a dedicated Planner on synthetic plan-annotated trajectories to generate high-level plans that an Executor follows, reaching 57.58% success on WebArena-Lite and 81.36% on WebVoyager.
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Creating Multilingual Mental Health Dialogue Datasets: Limits of Persona-Based Localization via Nationality and Language
Modifying nationality and language parameters in English-centric personas for mental health dialogues introduces clinical inconsistencies across languages and causes LLM judges to perform inaccurately on non-English depression severity assessments.
- VISHC at PsyDefDetect: Mitigating Data Scarcity in Psychological Defense Classification with Context-Aware Synthetic Augmentation