Social NCE: Contrastive Learning of Socially-aware Motion Representations (2012.11717v3)

Abstract: Learning socially-aware motion representations is at the core of recent advances in multi-agent problems, such as human motion forecasting and robot navigation in crowds. Despite promising progress, existing representations learned with neural networks still struggle to generalize in closed-loop predictions (e.g., output colliding trajectories). This issue largely arises from the non-i.i.d. nature of sequential prediction in conjunction with ill-distributed training data. Intuitively, if the training data only comes from human behaviors in safe spaces, i.e., from "positive" examples, it is difficult for learning algorithms to capture the notion of "negative" examples like collisions. In this work, we aim to address this issue by explicitly modeling negative examples through self-supervision: (i) we introduce a social contrastive loss that regularizes the extracted motion representation by discerning the ground-truth positive events from synthetic negative ones; (ii) we construct informative negative samples based on our prior knowledge of rare but dangerous circumstances. Our method substantially reduces the collision rates of recent trajectory forecasting, behavioral cloning and reinforcement learning algorithms, outperforming state-of-the-art methods on several benchmarks. Our code is available at https://github.com/vita-epfl/social-nce.

• The paper introduces Social NCE, a novel contrastive learning method that integrates social context into motion representation.
• It leverages context-sensitive features to accurately model and predict interactions in complex, dynamic scenes.
• Experimental results demonstrate superior performance over baselines, highlighting its potential for robust motion prediction in crowded scenarios.

Analysis of ICCV Author Guidelines Document

The paper "LaTeX Author Guidelines for ICCV Proceedings" provides a comprehensive set of instructions for authors aiming to submit manuscripts to the International Conference on Computer Vision (ICCV). While not presenting novel experimental research or results, this document serves an essential role in guiding contributors through the procedural requirements and formatting stipulations necessary to conform to the standards expected by the conference.

Key Elements Addressed in the Guidelines

The guidelines cover several critical aspects for authors to address while preparing their manuscripts. Below is a summary of the key components delineated in the paper:

• Language and Submission Standards: Manuscripts must be written in English, with specific attention to policies regarding dual submissions to ensure diversity and integrity in conference content.
• Paper Specifications: The guidelines specify that papers should not exceed eight pages excluding references, noting the absence of additional page charges. The firm stance on overlength papers underscores the conference’s commitment to maintaining a level playing field whereby all submissions comply uniformly with set limits.
• Review Process: There is an emphasis on the blind review process, including strategies to anonymize submissions without compromising the attribution of prior work. The document clarifies common misconceptions about citation practices in blind submissions, aiming to uphold fairness and anonymity.
• Formatting Details: Authors are provided with explicit instructions on formatting, encompassing the required use of a two-column layout, font styles, headings, and the positioning of figures and tables. The inclusion of detailed margin, type-style, and page numbering requirements facilitate the uniformity of submitted documents.

Implications and Future Directions

The paper's implications are largely procedural, seeking to streamline the submission process while also enhancing the ease of manuscript review. By enforcing a standardized format, ICCV aids reviewers in efficiently assessing submissions, thereby potentially accelerating the overall review process.

Looking forward, as academic publishing continues to evolve, these guidelines may incorporate more dynamic content considerations, such as interactive visualizations or data sharing protocols. Additionally, as the field of computer vision grows, the guidelines might expand to address new ethical considerations or data-driven methodologies.

Conclusion

In sum, the "LaTeX Author Guidelines for ICCV Proceedings" document is a crucial resource that delineates the structural and procedural norms for manuscript submissions. While it does not contribute empirical findings to the field of computer science or computer vision, it plays a foundational role in aligning submissions to meet professional standards, ensuring the efficient function of the peer review process. Consequently, adherence to these guidelines facilitates the conference's mission of disseminating high-quality, innovative research within the computer vision community.