qhjqhj00 appearance-free-action-eval
Appearance-free Action Recognition: Zero-shot Generalization in Humans and a Two-Pathway Model — Prerana Kumar, Martin A. Giese (2026) (arXiv:2604.16675, 2026)
What this evaluates
Evaluates zero-shot generalization of action recognition models to appearance-free videos generated by warping noise or random dots with optical flow, testing reliance on motion cues over static shape and texture.
Datasets
- UCF5 — total ?; splits: train (-1), test (-1)
- AFD5 — total ?; splits: test (-1)
- AFF5 — total ?; splits: test (-1)
Metrics
accuracy(primary) — range: [0, 1]- Fraction of correctly classified video instances out of the total number of instances in a given split.
Transfer Score— range: [0, 1]- Mean of accuracy on AFD5 and AFF5: (AFD5_acc + AFF5_acc) / 2.
Input / output format
Input: RGB video frames or optical flow sequences representing action clips.
Output: Discrete action class label.
Scoring recipe
def compute_accuracy(predictions, gold_labels):
correct = sum(1 for p, g in zip(predictions, gold_labels) if p == g)
return correct / len(gold_labels)
def compute_transfer_score(acc_afd5, acc_aff5):
return (acc_afd5 + acc_aff5) / 2
Common pitfalls
- Models with explicit optical flow inputs still suffer large performance drops on appearance-free videos without a coherence-gating mechanism.
- RGB-only models collapse to near-chance performance on appearance-free stimuli, highlighting the necessity of motion cues.
- Human performance is reported as a baseline but is not used to train or tune the models.
Evidence (verbatim from paper)
Mean accuracy across participants was $0.9891\pm 0.0146$ on UCF5, $0.8400\pm 0.0613$ on AFD5, and $0.7872\pm 0.0539$ on AFF5. Transfer Score is $(\mathrm{AFD5}+\mathrm{AFF5})/2$ computed from the reported means .
Citation
@misc{kumar2026appearancefree,
title={Appearance-free Action Recognition: Zero-shot Generalization in Humans and a Two-Pathway Model},
author={Prerana Kumar, Martin A. Giese (2026)},
year={2026},
note={arXiv:2604.16675}
}
- arXiv: 2604.16675