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20232026
most citedNeurosymbolic Information Extraction from Transactional Documents

1 citations · 1 across the 10 of their papers we have counts for

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cs.CV2026

Structured Visual Target Learning For Cross-Subject eeg-to-image retrieval

Salini Yadav, Taveena Lotey, Mickaël Coustaty +2

Cross-subject EEG-to-image retrieval requires a neural represen- tation trained on source subjects to remain aligned with a visual embedding space for an unseen subject. Whereas ex…

cs.CV2026

When Superpixels Fail on Documents: A Study of Segmentation for LIME Explanations

Quentin Telnoff, Emanuela Boros, Mickaël Coustaty +3

Post-hoc explanation methods are widely used to inspect image classifiers, but their reliability depends on design choices that are often treated as implementation details. We stud…

cs.CV2026

Active Learning for Cascaded Object Detection: Balancing Coverage and Uncertainty in Table Extraction Pipelines

Eliott Thomas, Mickael Coustaty, Aurelie Joseph +3

Table extraction from business documents relies on a cascaded pipeline where Table Detection (TD) first localizes tables and Table Structure Recognition (TSR) then recovers their i…

cs.CV2026

ConRTF: Edge-Constrained Boundary Distribution Refinement for Realtime TransFormer Table Structure Recognition

Eliott Thomas, Tri-Cong Pham, Mickael Coustaty +5

Table Structure Recognition (TSR) aims to recover the row and column layout of tables from document images, a key step in document understanding pipelines. Accurate TSR depends on…

cs.CV2026

DocQT: Improving Document Forgery Localization Robustness via Diverse JPEG Quantization Tables

Kylian Ronfleux-Corail, Guillaume Bernard, Mickaël Coustaty +1

Document manipulation localization models achieve strong performance on public benchmarks yet fail to generalize to operational document workflows. We identify a critical and overl…