The End Of Knowledge - Vault 5/98 - CVPR - 2024 - CVPR: past, present, and future

graph LR classDef conference fill:#f9d4d4, font-weight:bold, font-size:14px classDef research fill:#d4f9d4, font-weight:bold, font-size:14px classDef review fill:#d4d4f9, font-weight:bold, font-size:14px classDef future fill:#f9f9d4, font-weight:bold, font-size:14px classDef ethics fill:#f9d4f9, font-weight:bold, font-size:14px A[CVPR: past, present,
and future] --> B[Conference Growth
and Impact] A --> C[Research Trends
and Focus] A --> D[Review Process
and Challenges] A --> E[Future Directions
and Considerations] A --> F[Ethical and
Cultural Aspects] B --> G[Top scientific venue,
significant growth. 1] B --> H[Networking, inspiration at CVPR valued. 13] B --> I[Debating CVPR growth vs. maintenance. 22] C --> J[Niche to widespread public interest. 2] C --> K[Specialized models to multimodal approaches. 4] C --> L[Lasting impact over incremental improvements. 5] C --> M[Multimodal, interactive computer vision research. 10] C --> N[Custom, domain-specific over general-purpose systems. 11] C --> O[Scientific contributions over engineering improvements. 19] D --> P[Disappearing reviewers, maintaining review quality. 6] D --> Q[Discussion on increasing process transparency. 7] D --> R[Improving reviewer participation and consequences. 8] D --> S[Pros/cons of benchmark-based paper assessment. 9] D --> T[Suggestions for review system enhancement. 16] D --> U[Difficulty finding qualified paper reviewers. 17] E --> V[Rapid research pace concerns authors. 14] E --> W[Tech companies influence research directions. 15] E --> X[Frequent publishing impacts research quality. 20] E --> Y[Science-focused, ecologically valid CVPR vision. 26] E --> Z[Post-solved computer vision activities discussed. 27] F --> AA[Addressing concerns, biases in research. 12] F --> AB[Cultural consideration in vision applications. 18] F --> AC[Importance of interdisciplinary stakeholder involvement. 21] F --> AD[Community contribution through reviewing emphasized. 25] F --> AE[Better global application diversity needed. 29] class B,G,H,I conference class C,J,K,L,M,N,O research class D,P,Q,R,S,T,U review class E,V,W,X,Y,Z future class F,AA,AB,AC,AD,AE ethics

Resume:

1.- CVPR Growth: The conference has grown significantly, becoming one of the top scientific publication venues in recent years.

2.- AI Popularization: AI has evolved from a niche research topic to a subject of widespread public interest and discussion.

3.- Technological Advancements: CVPR has been at the forefront of technological advancements in computer vision.

4.- Research Evolution: The field has moved from specialized models for proxy tasks to more complex, multimodal approaches.

5.- Impactful Papers: Panelists emphasize the importance of papers with lasting impact rather than just incremental improvements.

6.- Review Process Challenges: The conference faces issues with disappearing reviewers and maintaining review quality at scale.

7.- Transparency in Reviews: There's discussion about making the review process more transparent, potentially revealing reviews publicly.

8.- Reviewer Accountability: Suggestions for improving reviewer accountability, including potential consequences for non-participation.

9.- Artifact-based Evaluation: The panel discusses the pros and cons of evaluating papers based on benchmarks and numerical improvements.

10.- Multimodal Research: The future of computer vision research is seen as increasingly multimodal and interactive.

11.- Domain-specific Solutions: There's a call for more custom, domain-specific solutions rather than just general-purpose vision systems.

12.- Ethical Considerations: The panel emphasizes the importance of addressing ethical concerns and biases in computer vision research.

13.- Community Building: CVPR is valued as a place for networking, inspiration, and community building among researchers.

14.- Paper Obsolescence: The rapid pace of research raises concerns about papers becoming obsolete before presentation.

15.- Funding Influence: The panel discusses the influence of large-scale funding from tech companies and VCs on research directions.

16.- Review System Improvement: Various suggestions are made for improving the review system, including reviewer ratings and incentives.

17.- Reviewer Recruitment: The difficulty of recruiting enough qualified reviewers for the growing number of submissions is discussed.

18.- Cultural Diversity: The importance of considering cultural and linguistic diversity in computer vision applications is emphasized.

19.- Science vs. Engineering: There's a desire to shift focus back to more scientific contributions rather than just engineering improvements.

20.- Publication Pressure: The panel discusses the pressure to publish frequently and its impact on research quality.

21.- Interdisciplinary Collaboration: The importance of collaborating with stakeholders from various domains is highlighted.

22.- Conference Size: There's discussion about whether CVPR should continue growing or maintain its current size.

23.- Paper Quality: The panel emphasizes the need for papers with novel ideas rather than just incremental improvements.

24.- Reviewer Education: The importance of educating new reviewers and providing feedback on review quality is discussed.

25.- Community Responsibility: The panel stresses the importance of giving back to the community through reviewing and other contributions.

26.- Future Vision: Panelists share their visions for CVPR in 20 years, including more science-focused and ecologically valid research.

27.- Solved Computer Vision: The panel speculates on what they would do if computer vision were completely solved.

28.- Academia vs. Industry: The role of academia in advancing computer vision despite resource limitations is highlighted.

29.- Global Representation: The need for better representation of applications from different countries and cultures is discussed.

30.- Brave Innovation: The panel encourages future conference organizers to be brave in trying new ideas to improve the conference.

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