Context Bias is the reliance of models on co-occurring objects, scene context, or spatial relationships for classification — the model learns that certain objects always appear together (e.g., keyboard with monitor) and uses context cues rather than object-specific features for prediction.

Context Bias Examples

• Co-Occurrence: "Tennis racket" prediction relies on detecting "tennis court" or "tennis ball" in the image.
• Spatial Context: Object detection accuracy depends on where in the scene the object appears — unusual positions cause misses.
• Scene Priors: Indoor scenes bias toward "furniture" classes, outdoor toward "vehicles" — regardless of actual content.
• Language Bias: In VQA, models learn statistical priors ("What color is the banana?" → "yellow") without looking at the image.

Why It Matters

• Counter-Intuitive Scenes: Models fail on unusual contexts — a boat on land, a car in a living room.
• Out-of-Context Detection: Context bias undermines the ability to detect objects in unusual settings.
• Causal vs. Correlational: Models learn correlational context rather than causal features of the target object.

Context Bias is guilt by association — classifying objects based on their usual companions rather than their own distinctive features.