Research map / Vision-language and multimodal / Human and embodied modeling
The Affective Growth of Computer Vision
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Growth of deep learning and computer vision | Computer vision research community · Researchers’ stories | reported emotional experience | Mixed |
| Emphasis on benchmark gains and engineering solutions | Computer vision research practices · Researchers’ stories | perceived scientific fulfillment | Hurt |
| Increasing prominence of industry research and collaborations | Computer vision researchers and academic labs · Researchers’ stories | professional experience | Mixed |
| Rapid field growth and increasingly competitive conferences | Computer vision researchers · Researchers’ stories | sense of belonging | Hurt |
| Treating ethics and equity discussions as secondary to technical topics | Computer vision and machine learning research venues · Researchers’ stories | inclusion of ethics-focused researchers | Hurt |
| Applying deep learning to problems with real-world applications | Computer vision researchers · Researchers’ stories | reported opportunities and impact | Helped |
Closest papers by meaning
- egoEMOTION: Egocentric Vision and Physiological Signals for Emotion and Personality Recognition in Real-world Tasks (NeurIPS 2025 · similarity 0.84)
- FEEL: Quantifying Heterogeneity in Physiological Signals for Generalizable Emotion Recognition (NeurIPS 2025 · similarity 0.82)
- Customizing Visual Emotion Evaluation for MLLMs: An Open-vocabulary, Multifaceted, and Scalable Approach (ICLR 2026 · similarity 0.81)
- Would Deep Generative Models Amplify Bias in Future Models? (CVPR 2024 · similarity 0.81)
- EmoNet-Face: An Expert-Annotated Benchmark for Synthetic Emotion Recognition (NeurIPS 2025 · similarity 0.81)
- The 3D-PC: a benchmark for visual perspective taking in humans and machines (ICLR 2025 · similarity 0.81)
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