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[2103.05983] Reformulating HOI Detection as Adaptive Set Prediction
Source: arxiv.org
Article summary:
1. This paper proposes a novel formulation of HOI detection as an adaptive set prediction problem.
2. The proposed Adaptive Set-based one-stage framework (AS-Net) uses a transformer to map a trainable interaction query set to an interaction prediction set.
3. The proposed method outperforms previous state-of-the-art methods on three challenging HOI detection datasets, achieving over 31% relative improvement on the large-scale HICO-DET dataset.
Article analysis:
The article is generally trustworthy and reliable, as it provides evidence for its claims in the form of experiments conducted on three challenging HOI detection datasets, with results showing that the proposed method outperforms previous state-of-the-art methods by over 31% relative improvement on the large-scale HICO-DET dataset. The article also provides code for reproducibility purposes, which further adds to its trustworthiness and reliability.
However, there are some potential biases in the article that should be noted. For example, the authors do not explore any counterarguments or alternative approaches to their proposed method, which could have provided more insight into its effectiveness and limitations. Additionally, while the authors provide evidence for their claims in terms of experimental results, they do not provide any evidence for why their approach is superior to other existing methods or how it addresses existing challenges in HOI detection. This could be addressed by providing more detailed explanations of their approach and how it differs from existing methods.