Summary of Scene-graph Vit: End-to-end Open-vocabulary Visual Relationship Detection, by Tim Salzmann et al.
Scene-Graph ViT: End-to-End Open-Vocabulary Visual Relationship Detection
by Tim Salzmann, Markus Ryll, Alex Bewley, Matthias Minderer
First submitted to arxiv on: 21 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Computation and Language (cs.CL); Machine Learning (cs.LG); Robotics (cs.RO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach to visual relationship detection is proposed, which eliminates the need for separate relationship modules or decoders in existing object detection architectures. The model consists of a Transformer-based image encoder that represents objects as tokens and models their relationships implicitly, with an attention mechanism that selects object pairs likely to form a relationship. The architecture is trained on a mixture of object and relationship detection data using a single-stage recipe, achieving state-of-the-art performance on Visual Genome and GQA benchmarks at real-time inference speeds. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to identify objects in a picture and their relationships. Most methods require adding extra parts to the object detection system, making it complicated and hard to train. This new approach eliminates those extra steps by using a special image encoder that represents objects as tokens and models their relationships automatically. It also uses an attention mechanism to select which object pairs are likely related. The model is trained on a mix of data for object and relationship detection, achieving top performance on two important benchmark datasets. |
Keywords
* Artificial intelligence * Attention * Encoder * Inference * Object detection * Transformer
