# Unifying Identification and Context Learning for Person Recognition

arXiv: https://arxiv.org/abs/1806.03084 · HTML: https://mlsanity.com/paper/1806.03084/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jun 2018.

## Built on by (most cited first, 10 of 10)

- [Large-Scale Spatio-Temporal Person Re-identification: Algorithms and Benchmark](https://mlsanity.com/paper/2105.15076/) (2021)
- [MovieCuts: A New Dataset and Benchmark for Cut Type Recognition](https://mlsanity.com/paper/2109.05569/) (2021)
- [Exploring Visual Context for Weakly Supervised Person Search](https://mlsanity.com/paper/2106.10506/) (2021)
- [Transformation vs Tradition: Artificial General Intelligence (AGI) for Arts and Humanities](https://mlsanity.com/paper/2310.19626/) (2023)
- [MEGA: Multimodal Alignment Aggregation and Distillation For Cinematic Video Segmentation](https://mlsanity.com/paper/2308.11185/) (2023)
- [Automated Conversion of Music Videos into Lyric Videos](https://mlsanity.com/paper/2308.14922/) (2023)
- [An Open-World, Diverse, Cross-Spatial-Temporal Benchmark for Dynamic Wild Person Re-Identification](https://mlsanity.com/paper/2403.15119/) (2024)
- [SynopGround: A Large-Scale Dataset for Multi-Paragraph Video Grounding from TV Dramas and Synopses](https://mlsanity.com/paper/2408.01669/) (2024)
- [Head and Body: Unified Detector and Graph Network for Person Search in Media](https://mlsanity.com/paper/2111.13888/) (2021)
- [Actor-identified Spatiotemporal Action Detection -- Detecting Who Is Doing What in Videos](https://mlsanity.com/paper/2208.12940/) (2022)

## Abstract (arXiv)

Despite the great success of face recognition techniques, recognizing persons under unconstrained settings remains challenging. Issues like profile views, unfavorable lighting, and occlusions can cause substantial difficulties. Previous works have attempted to tackle this problem by exploiting the context, e.g. clothes and social relations. While showing promising improvement, they are usually limited in two important aspects, relying on simple heuristics to combine different cues and separating the construction of context from people identities. In this work, we aim to move beyond such limitations and propose a new framework to leverage context for person recognition. In particular, we propose a Region Attention Network, which is learned to adaptively combine visual cues with instance-dependent weights. We also develop a unified formulation, where the social contexts are learned along with the reasoning of people identities. These models substantially improve the robustness when working with the complex contextual relations in unconstrained environments. On two large datasets, PIPA and Cast In Movies (CIM), a new dataset proposed in this work, our method consistently achieves state-of-the-art performance under multiple evaluation policies.