Seeing the Intangible: Surveying Automatic High-Level Visual Understanding from Still Images

The field of Computer Vision (CV) was born with the single grand goal of complete image understanding: providing a complete semantic interpretation of an input image. What exactly this goal entails is not immediately straightforward, but theoretical hierarchies of visual understanding point towards a top level of full semantics, within which sits the most complex and subjective information humans can detect from visual data. In particular, non-concrete concepts including emotions, social values and ideologies seem to be protagonists of this "high-level" visual semantic understanding. While such "abstract concepts" are critical tools for image management and retrieval, their automatic recognition is still a challenge, exactly because they rest at the top of the "semantic pyramid": the well-known semantic gap problem is worsened given their lack of unique perceptual referents, and their reliance on more unspecific features than concrete concepts. Given that there seems to be very scarce explicit work within CV on the task of abstract social concept (ASC) detection, and that many recent works seem to discuss similar non-concrete entities by using different terminology, in this survey we provide a systematic review of CV work that explicitly or implicitly approaches the problem of abstract (specifically social) concept detection from still images. Specifically, this survey performs and provides: (1) A study and clustering of high level visual understanding semantic elements from a multidisciplinary perspective (computer science, visual studies, and cognitive perspectives); (2) A study and clustering of high level visual understanding computer vision tasks dealing with the identified semantic elements, so as to identify current CV work that implicitly deals with AC detection

Multi-level transformer-based social relation recognition

Social relationships refer to the connections that exist between people and indicate how people interact in society. The effective recognition of social relationships is conducive to further understanding human behavioral patterns and thus can be vital for more complex social intelligent systems, such as interactive robots and health self-management systems. The existing works about social relation recognition (SRR) focus on extracting features on different scales but lack a comprehensive mechanism to orchestrate various features which show different degrees of importance. In this paper, we propose a new SRR framework, namely Multi-level Transformer-Based Social Relation Recognition (MT-SRR), for better orchestrating features on different scales. Specifically, a vision transformer (ViT) is firstly employed as a feature extraction module for its advantage in exploiting global features. An intra-relation transformer (Intra-TRM) is then introduced to dynamically fuse the extracted features to generate more rational social relation representations. Next, an inter-relation transformer (Inter-TRM) is adopted to further enhance the social relation representations by attentionally utilizing the logical constraints among relationships. In addition, a new margin related to inter-class similarity and a sample number are added to alleviate the challenges of a data imbalance. Extensive experiments demonstrate that MT-SRR can better fuse features on different scales as well as ameliorate the bad effect caused by a data imbalance. The results on the benchmark datasets show that our proposed model outperforms the state-of-the-art methods with significant improvement

Srr-lgr: local–global information-reasoned social relation recognition for human-oriented observation

People’s interactions with each other form the social relations in society. Understanding human social relations in the public space is of great importance for supporting the public admin-istrations. Recognizing social relations through visual data captured by remote sensing cameras is one of the most efficient ways to observe human interactions in a public space. Generally speaking, persons in the same scene tend to know each other, and the relations between person pairs are strongly correlated. The scene information in which people interact is also one of the important cues for social relation recognition (SRR). The existing works have not explored the correlations between the scene information and people’s interactions. The scene information has only been extracted on a simple level and high level semantic features to support social relation understanding are lacking. To address this issue, we propose a social relation structure-aware local–global model for SRR to exploit the high-level semantic global information of the scene where the social relation structure is explored. In our proposed model, the graph neural networks (GNNs) are employed to reason through the interactions (local information) between social relations and the global contextual information contained in the constructed scene-relation graph. Experiments demonstrate that our proposed local–global information-reasoned social relation recognition model (SRR-LGR) can reason through the local–global information. Further, the results of the final model show that our method outperforms the state-of-the-art methods. In addition, we have further discussed whether the global information contributes equally to different social relations in the same scene, by exploiting an attention mechanism in our proposed model. Further applications of SRR for human-observation are also exploited