How papers are selected for Best Paper, Oral, or Highlight presentation at major ML/CV conferences such as CVPR, ICCV, ECCV, NeurIPS, and ICLR? [D]

The query regarding the selection process for Best Paper, Oral, and Highlight presentations at premier ML/CV conferences is a surprisingly opaque one, and /u/National-Resident244’s questions strike at the heart of a system often shrouded in a degree of mystery. The process isn’t a simple popularity contest driven by reviewer scores; it’s a more nuanced evaluation conducted by Area Chairs (ACs), Senior Area Chairs (SACs), and the Program Chairs, sometimes with input from dedicated award committees. It’s a process that reveals a fascinating tension between quantitative assessment (reviewer scores) and qualitative judgment (novelty, impact, and discussion) - a tension that resonates with broader debates around evaluating research quality. The fact that this question is even being asked highlights a need for greater transparency within these conferences, particularly for researchers navigating the competitive landscape. It’s a topic of ongoing discussion, as evidenced by the recent thread on BMVC 2026 review discussions BMVC 2026 Review Discussion Thread, demonstrating that the intricacies of review processes and selection criteria are frequently on the minds of the ML community.

The reliance on ACs and SACs for final selection, rather than direct reviewer votes, underscores the importance of expert judgment. These individuals possess a deeper understanding of the field and can assess the broader impact and significance of a paper beyond the numerical scores assigned by reviewers. Interestingly, the decision is typically made based on the camera-ready version, allowing the committee to evaluate the final presentation and clarity of the work. This suggests that effective communication of research findings is just as critical as the technical merit of the work itself. The potential for “paper fishing,” as discussed in another recent post What do you think about paper fishing?, also becomes relevant here; the ability to polish and present a compelling narrative can significantly influence the final assessment. It’s a reminder that strategic presentation is a skill, and one that can impact recognition even for groundbreaking research.

The weight given to factors like novelty and impact, alongside reviewer scores, hints at a system striving for more than just incremental progress. Conferences are not solely seeking papers that perform slightly better than existing methods; they aim to identify work that pushes the boundaries of knowledge and has the potential to shape the future of the field. This emphasis on impact is increasingly important as the ML/CV community grapples with questions of responsible AI and real-world applicability. The need for a strong mathematical foundation in research is underscored by discussions within the community, as seen in a recent thread about resources for improving mathematical skills Books/Resources to improve mathematical foundations for ML research, reflecting an understanding that rigorous methodology and theoretical understanding are crucial for impactful contributions. This selection process, therefore, serves as a filter, elevating work that demonstrates not only technical prowess but also a clear vision for the future.

Ultimately, the selection process for prestigious conference awards highlights the inherent subjectivity in evaluating scientific contributions. While reviewer scores provide a valuable starting point, the final decision rests on the judgment of experts who consider a range of factors, including novelty, impact, and presentation quality. Increased transparency in these processes would benefit the entire community, fostering greater trust and ensuring that the most impactful and innovative work receives the recognition it deserves. Moving forward, it will be interesting to see if conferences adopt more formalized and publicly accessible criteria for these awards, perhaps incorporating elements of peer review beyond the initial submission phase, to further mitigate potential biases and ensure a more equitable selection process.