Skill UI

This guide models the comprehensive two-round, journal-style peer review process used at major computer vision conferences like WACV. It details the distinct stages: Round 1 outcomes (Accept, Revise and Resubmit, Reject), the optional R1 rebuttal phase, and the final R2 decision. It emphasizes that the most critical phase for authors is executing high-quality revisions during the Revise-and-Resubmit stage, rather than solely relying on rebuttals.

A comprehensive guide for authors submitting to major conferences (like WACV). It details how to properly split content between the main 8-page paper body and the supplementary material. Key focus areas include maintaining double-blind anonymity, structuring evidence for reviewers, and ensuring consistency across multiple revision rounds.

This guide helps researchers determine the optimal venue for their Computer Vision paper, specifically addressing the unique identity of WACV (Applications-first) versus major conferences like CVPR, ICCV, and ECCV. It guides authors on whether their contribution is driven by real-world applications/constraints or by advancing core algorithms, and how to choose between the Applications or Algorithms track within WACV.

Provides comprehensive guidelines for packaging and submitting research artifacts (datasets, models, code) for major conferences. It details the difference between mere 'existence' (badge verification) and true academic 'reproducibility,' covering archival strategies, web data licensing, takedown resilience, and minimal viable package structure for long-term scholarly impact.

This comprehensive guide details the strategic process of writing a high-impact rebuttal for major academic conferences. It teaches users how to triage mixed reviewer feedback, differentiate between in-track, adjacent-track, and web-fit objections, and structure responses using concrete evidence, specific citations, and confident concessions to maximize the paper's acceptance chances.

A comprehensive guide for authors preparing camera-ready submissions for Web Conferences. It covers the critical pre-acceptance steps, such as de-anonymization, restoring acknowledgements, managing ACM e-rights, and submitting associated artifacts. Learn how to navigate the tight production pipeline, ensuring compliance with ACM standards and meeting strict page limits.

A comprehensive guide for authors submitting papers to web conferences. It details how to structure empirical claims, ensuring evidence matches the claim's scope (scale, freshness, generalizability). The guide covers avoiding methodological flaws like temporal or popularity leakage, selecting datasets with proper provenance, and performing rigorous ablations and statistical analysis for maximum academic rigor.

This guide provides an expert methodology for writing the 'Related Work' section for top-tier web conferences (like WWW). It teaches authors how to establish the paper's academic lineage, contrast the work against competing venues (e.g., SIGIR, KDD), and manage citation hygiene, ensuring maximum impact within strict page limits.

This skill models the entire peer-review pipeline for Web Conferences (WWW), detailing the stages from abstract submission to final notification. It guides authors and reviewers on the implications of track-scoped double-blind review, emphasizing criteria such as Web-centricity, evidence scale, and ethical considerations required for acceptance.

A comprehensive audit guide for submitting research papers to major Web Conferences (WWW). It details critical, irreversible steps such as author list freezing, restrictive page arithmetic (12 pages total), track selection protocols, anonymity checks, and managing submission caps. Essential reading for research teams preparing for demanding academic deadlines.

This guide provides a comprehensive strategy for authors submitting to conferences like WSDM, which historically do not have a dedicated rebuttal phase. It teaches preemptive objection engineering, how to structure answers directly into the PDF, managing post-submission communication channels, and planning next steps whether accepted or rejected.

This guide provides a comprehensive framework for designing rigorous and defensible evaluation sections for machine learning and information retrieval papers, particularly for top-tier conferences like WSDM. It covers the four critical evidence quadrants—Effectiveness, Validity, Efficiency, and Robustness—and details necessary protocols, such as temporal splits, proper baseline selection (including heuristics), ablation studies, and hardware-aware performance reporting. Use this when auditing or writing the experimental section of a research paper.