Papers needing PDFs
Updated 2026-09-21. Six supplied PDFs have now been incorporated into the publication list and detail pages. 99 of 100 pages have their abstract and two captioned figures.
Still needed
| Suggested filename | Paper | Link | Material still needed |
|---|---|---|---|
031.pdf |
Assessing Domain Generalization of Semantic Segmenters with Synthetic Data | Workshop page | Full PDF, original abstract, and two figures with captions. The current description and significance section describe the research scope only. |
Supplied and completed
The files in tmp/ used the checklist numbers without leading zeros; their identities were checked against the PDF title pages.
| Supplied file | Paper |
|---|---|
78.pdf |
mSOP-765k — A Benchmark For Multi-Modal Structured Output Predictions |
85.pdf |
Fréchet Inception Distance is Failing to Preserve Rank Consistency for Synthetic Out-of-Distribution Samples |
89.pdf |
Parameterized Adverse Lens Corruptions to Probe Model Robustness to Optical Tolerances |
95.pdf |
Position: We need theory-grounded explainability for abstract classification to quantify model bias |
96.pdf |
Combining Fine-Tuning and RAG for Structured Output Prediction on a Multimodal High Velocity Retail Dataset |
97.pdf |
Unsupervised Visual Concept Bottlenecks for Interpretable Bioimage Classification |
These pages now use abstracts and two figures from the supplied full papers. The publication list includes paper-derived teasers, and provisional summaries were revised against the findings. Verified title and author corrections also appear in the BibTeX citations. Source URLs, PDF checksums, and figure crops are recorded in _data/publication-review.yaml. The input tmp/ directory is excluded from the Jekyll build.
Shared preprints
The workshop entries “Robust Models are less Over-Confident” (ICML 2022) and “Data-Efficient Visual Concept Bottleneck Models” (CVPR 2025 workshop) use the linked public preprints also used by their later full-conference entries. Separate workshop PDFs are welcome if their abstract or figures differ.
Filling the remaining gap
The page is stored in _publications/. Add the verified abstract, abstract_source, and two or three figures entries, then set content_status: complete. Put optimized figure images in images/publications/details/, add a teaser to _data/sources.yaml, and record provenance in _data/publication-review.yaml. Run python auto-cite/auto-cite.py after editing the source list.