Conceptualizing Multi-scale Wavelet Attention and Ray-based Encoding for Human-Object Interaction Detection
This addresses the need for more efficient and accurate HOI detection in complex visual scenes, though it appears incremental as it builds on existing methods with novel architectural components.
The paper tackles the problem of inefficient and unreliable human-object interaction detection by proposing a wavelet attention-like backbone and a ray-based encoder, achieving potential improvements on benchmark datasets like ImageNet and HICO-DET.
Human-object interaction (HOI) detection is essential for accurately localizing and characterizing interactions between humans and objects, providing a comprehensive understanding of complex visual scenes across various domains. However, existing HOI detectors often struggle to deliver reliable predictions efficiently, relying on resource-intensive training methods and inefficient architectures. To address these challenges, we conceptualize a wavelet attention-like backbone and a novel ray-based encoder architecture tailored for HOI detection. Our wavelet backbone addresses the limitations of expressing middle-order interactions by aggregating discriminative features from the low- and high-order interactions extracted from diverse convolutional filters. Concurrently, the ray-based encoder facilitates multi-scale attention by optimizing the focus of the decoder on relevant regions of interest and mitigating computational overhead. As a result of harnessing the attenuated intensity of learnable ray origins, our decoder aligns query embeddings with emphasized regions of interest for accurate predictions. Experimental results on benchmark datasets, including ImageNet and HICO-DET, showcase the potential of our proposed architecture. The code is publicly available at [https://github.com/henry-pay/RayEncoder].