Skill UI

This guide models the comprehensive review pipeline for ECAI submissions, detailing the single-round, double-blind process. It covers the summary-reject phase, full review by area chairs, the single rebuttal window, and the final accept/reject decision. It contrasts this process with journal revisions and other major AI conferences (AAAI, IJCAI) to help authors strategize for submission success.

A comprehensive guide for authors submitting to major AI conferences (like ECAI). It teaches the critical skill of separating core, decision-critical arguments (must be in the main body) from supporting details, proofs, and code (which belong in the optional supplement). This ensures reviewers can fairly judge the contribution despite strict page limits.

This comprehensive guide outlines a detailed, backward-planning workflow for submitting research papers to major AI conferences like ECAI or IJCAI-ECAI. It covers the entire academic lifecycle, from initial topic selection and evidence type planning to abstract submission, full paper drafting, rebuttal response, and final camera-ready publication, ensuring authors meet strict deadlines and adhere to rigorous review standards.

This guide provides a detailed strategy for managing and releasing research artifacts (code, models, data) associated with a vision paper submitted to ECCV. It covers two critical phases: the sealed, anonymous archival submission during review, and the structured public release runway post-acceptance, ensuring reproducibility and adherence to conference policies.

A comprehensive guide for designing and auditing high-stakes computer vision experiments for top-tier conferences. It covers critical areas like 'matched-substrate fairness,' 'six-month-staleness testing,' and rigorous ablation studies, ensuring that claims are robust, reproducible, and withstand intense peer review scrutiny.

A detailed guide for modeling the complete academic peer-review lifecycle, specifically tailored to the ECCV conference structure. It covers the timeline from submission through rebuttal and AC discussion to final decision, detailing critical points such as co-author enforcement regimes, LLM policy adherence, panel reviewer composition, and strategic points influencing the final outcome. Essential for researchers targeting top-tier vision conferences.

This guide outlines the complete project management workflow for major computer vision conferences like ECCV, CVPR, and ICCV. It covers the entire academic cycle, from initial registration and submission through peer review, acceptance, camera-readiness, and post-conference retargeting strategies. Useful for research teams planning multiple submissions across even and odd years.

A comprehensive guide for drafting and compressing academic manuscripts, focusing on strict formatting constraints (e.g., LNCS single-column, 14-page limit) typical of major conferences like ECCV. Learn how to structure your paper, optimize the first page for non-specialists, and phrase claims to withstand rigorous peer review and rebuttal.

A comprehensive guide detailing the structured process for authors responding to peer reviews in top-tier academic conferences. It distinguishes between the initial author-feedback reply and the critical revise-and-resubmit change letter. The guide emphasizes providing concrete evidence, addressing every reviewer request systematically, and structuring the response to maximize acceptance chances.

A comprehensive guide detailing the multi-cycle, rolling model for EDBT research submissions. It covers the entire review pipeline, from initial program committee evaluation and author-feedback exchange, through the accept/revise/reject decision, and critically, the in-cycle revise-and-resubmit stage. The guide also contrasts EDBT's open-access, three-cycle cadence with other top-tier database conferences (e.g., SIGMOD, VLDB).

This guide outlines the strict rules for dividing a research paper into the main body and supplementary materials (artifact/appendices), particularly for database systems conferences like EDBT. It emphasizes that all decision-critical evidence must be presented within the reviewed pages, ensuring reviewers can judge the work without needing to open external files. It covers page budget discipline, reproducibility standards, and practical partitioning techniques.

A comprehensive guide for researchers on packaging and documenting NLP artifacts, such as datasets, prompts, and evaluation code, for academic publication. It details best practices for ensuring reproducibility, managing provenance, adhering to licensing standards, and structuring evidence for top-tier conferences like EMNLP.