Exploring Predicate Visual Context in Detecting of Human-Object Interactions

Recently, the DETR framework has emerged as the dominant approach for human--object interaction (HOI) research. In particular, two-stage transformer-based HOI detectors are amongst the most performant and training-efficient approaches. However, these often condition HOI classification on object features that lack fine-grained contextual information, eschewing pose and orientation information in favour of visual cues about object identity and box extremities. This naturally hinders the recognition of complex or ambiguous interactions. In this work, we study these issues through visualisations and carefully designed experiments. Accordingly, we investigate how best to re-introduce image features via cross-attention. With an improved query design, extensive exploration of keys and values, and box pair positional embeddings as spatial guidance, our model with enhanced predicate visual context (PViC) outperforms state-of-the-art methods on the HICO-DET and V-COCO benchmarks, while maintaining low training cost.

Efficient Two-Stage Detection of Human-Object Interactions with a Novel Unary-Pairwise Transformer

Recent developments in transformer models for visual data have led to significant improvements in recognition and detection tasks. In particular, using learnable queries in place of region proposals has given rise to a new class of one-stage detection models, spearheaded by the Detection Transformer (DETR). Variations on this one-stage approach have since dominated human-object interaction (HOI) detection. However, the success of such one-stage HOI detectors can largely be attributed to the representation power of transformers. We discovered that when equipped with the same transformer, their two-stage counterparts can be more performant and memory-efficient, while taking a fraction of the time to train. In this work, we propose the Unary-Pairwise Transformer, a two-stage detector that exploits unary and pairwise representations for HOIs. We observe that the unary and pairwise parts of our transformer network specialise, with the former preferentially increasing the scores of positive examples and the latter decreasing the scores of negative examples. We evaluate our method on the HICO-DET and V-COCO datasets, and significantly outperform state-of-the-art approaches. At inference time, our model with ResNet50 approaches real-time performance on a single GPU.

Spatio-attentive Graphs for Human-Object Interaction Detection

We address the problem of detecting human--object interactions in images using graphical neural networks. Our network constructs a bipartite graph of nodes representing detected humans and objects, wherein messages passed between the nodes encode relative spatial and appearance information. Unlike existing approaches that separate appearance and spatial features, our method fuses these two cues within a single graphical model allowing information conditioned on both modalities to influence the prediction of interactions with neighboring nodes. Through extensive experimentation we demonstrate the advantages of fusing relative spatial information with appearance features in the computation of adjacency structure, message passing and the ultimate refined graph features. On the popular HICO-DET benchmark dataset, our model outperforms state-of-the-art with an mAP of 27.18, a 10% relative improvement.