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Gaze Embeddings for Zero-Shot Image Classification

CVPR 2017 · Label-efficient and robust learning › Zero-/few-shot and meta-learning · approach: Transductive and semantic-dictionary zero-shot · 109 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use GFS instead of GH or GFG gaze embeddingsGaze-based zero-shot classification · CUB-VWaccuracyHelped
Combine participant embeddings by early fusion instead of averagingGFS gaze embeddings · CUB-VWaccuracy: 56.6% -> 69.8%Helped
Combine participant embeddings by late fusion instead of averagingGFS gaze embeddings · CUB-VWaccuracy: 56.6% -> 69.6%Helped
Add gaze duration (d) to location features (ablation)GFS gaze features · CUB-VWaccuracyHelped
Add sequence angles (α1, α2) to location and duration features (ablation)GFS gaze features · CUB-VWaccuracyHelped
Add pupil diameter (R) (ablation)GFS gaze features · CUB-VWaccuracyNo clear effect
Restrict the full gaze data to the images annotated with bubbles (ablation)GFS EARLY gaze embeddings · CUB-VWaccuracy: 73.9% -> 69.7%Hurt
Restrict gaze points to locations inside bubbles (ablation)GFS EARLY gaze embeddings · CUB-VWaccuracy: 73.9% -> 64.0%Hurt
Average rather than concatenate gaze points inside bubbles (ablation)GFS EARLY gaze embeddings · CUB-VWaccuracy: 73.9% -> 55.0%Hurt
Use one random gaze point inside each bubble (ablation)GFS EARLY gaze embeddings · CUB-VWaccuracy: 73.9% -> 49.2%Hurt
Combine gaze embeddings with expert attributesZero-shot image classification · CUB-VWaccuracy: 73.9% -> 78.2%Helped
Use gaze rather than expert attributesZero-shot image classification · CUB-VWSWaccuracy: 42.7 -> 26.0Hurt

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