Skill UI

This comprehensive guide provides researchers with structured methodologies for designing and auditing Computer Vision (CV) experiments for major conferences like WACV. It details the critical differences between the Applications and Algorithms tracks, emphasizing the need to report performance under real deployment constraints (e.g., power, latency) and ensuring fair, rigorous comparisons with baselines. Ideal for revising submissions to meet high review standards.

A comprehensive guide for structuring the 'Related Work' section in academic papers, especially in Computer Vision (CV). It teaches authors how to strategically position their research contribution by covering multiple literature shelves: vision methods, domain constraints, concurrent arXiv work, and prior art. This ensures the paper clearly defines a unique gap rather than merely listing citations, adhering to double-blind review standards.

A comprehensive guide for strengthening the reproducibility of academic papers, especially those involving systems claims (e.g., power usage, latency). It details maintaining a 'recipe ledger' for all reported metrics, ensuring constraint-aware measurement of environmental variables, and managing data/artifact consistency across the entire two-round revision process.

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 audit guide for authors submitting to WACV. It covers critical submission decisions, including choosing between Round 1 or Round 2, selecting the appropriate Applications or Algorithms track, adhering to the 8-page limit, maintaining double-blind anonymity, and ensuring all coauthors have valid OpenReview profiles to prevent desk rejection.

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.

Comprehensive guide detailing the end-to-end academic submission workflow for the WACV conference. This specialized workflow accounts for the two-round submission model, including key branching points like Round 1 reviews, optional rebuttals, Revise-and-Resubmit stages, Round 2 revisions, and final camera-ready preparation, which single-deadline plans often miss.

A comprehensive guide to structuring and writing academic papers for WACV, focusing on maximizing acceptance rates. It details the critical differences between writing for 'Applications' vs. 'Algorithms' tracks, ensuring the first page immediately captures reviewer attention. Key advice includes adhering to the strict 8-page budget, making figures self-contained, scoping all claims precisely, and moving excessive detail to supplements.

This guide provides comprehensive advice for positioning applied computer vision research for the IEEE/CVF WACV conference. It helps authors assess the manuscript's fit, re-frame the narrative from journal or arXiv styles, and ensure the contribution highlights practical systems, strong empirical evaluation, and application grounding required by top CV venues.

This skill guides the researcher through the comprehensive process of updating the AI agent security threat intelligence database. It involves executing targeted searches for new malicious skills, CVEs, attack vectors, and defensive tools. The process mandates critical analysis, de-duplication, risk assessment, and structured version control to maintain a cutting-edge security posture for AI agents.