Few-Shot Human-Object Interaction Recognition With Semantic-Guided Attentive Prototypes Network

Semantic-Aware Dynamic Generation Networks for Few-Shot Human-Object Interaction Recognition

Recognizing human-object interaction (HOI) aims at inferring various relationships between actions and objects. Although great progress in HOI has been made, the long-tail problem and combinatorial explosion problem are still practical challenges. To this end, we formulate HOI as a few-shot task to tackle both challenges and design a novel dynamic generation method to address this task. The proposed approach is called semantic-aware dynamic generation networks (SADG-Nets). Specifically, SADG-Net first assigns semantic-aware task representations for different batches of data, which further generates dynamic parameters. It obtains the features that highlight intercategory discriminability and intracategory commonality adaptively. In addition, we also design a dual semantic-aware encoder module (DSAE-Module), that is, verb-aware and noun-aware branches, to yield both action and object prototypes of HOI for each task space, which generalizes to novel combinations by transferring similarities among interactions. Extensive experimental results on two benchmark datasets, that is, humans interacting with common objects (HICO)-FS and trento universal HOI (TUHOI)-FS, illustrate that our SADG-Net achieves superior performance over state-of-the-art approaches, which proves its impressive effectiveness on few-shot HOI recognition.

Few-Shot Multi-Agent Perception With Ranking-Based Feature Learning

In this article, we focus on performing few-shot learning (FSL) under multi-agent scenarios in which participating agents only have scarce labeled data and need to collaborate to predict labels of query observations. We aim at designing a coordination and learning framework in which multiple agents, such as drones and robots, can collectively perceive the environment accurately and efficiently under limited communication and computation conditions. We propose a metric-based multi-agent FSL framework which has three main components: an efficient communication mechanism that propagates compact and fine-grained query feature maps from query agents to support agents; an asymmetric attention mechanism that computes region-level attention weights between query and support feature maps; and a metric-learning module which calculates the image-level relevance between query and support data fast and accurately. Furthermore, we propose a specially designed ranking-based feature learning module, which can fully utilize the order information of training data by maximizing the inter-class distance, while minimizing the intra-class distance explicitly. We perform extensive numerical studies and demonstrate that our approach can achieve significantly improved accuracy in visual and acoustic perception tasks such as face identification, semantic segmentation, and sound genre recognition, consistently outperforming the state-of-the-art baselines by 5%-20%.

An Adaptive Embedding Network with Spatial Constraints for the Use of Few-Shot Learning in Endangered-Animal Detection

Image recording is now ubiquitous in the fields of endangered-animal conservation and GIS. However, endangered animals are rarely seen, and, thus, only a few samples of images of them are available. In particular, the study of endangered-animal detection has a vital spatial component. We propose an adaptive, few-shot learning approach to endangered-animal detection through data augmentation by applying constraints on the mixture of foreground and background images based on species distributions. First, the pre-trained, salient network U2-Net segments the foregrounds and backgrounds of images of endangered animals. Then, the pre-trained image completion network CR-Fill is used to repair the incomplete environment. Furthermore, our approach identifies a foreground–background mixture of different images to produce multiple new image examples, using the relation network to permit a more realistic mixture of foreground and background images. It does not require further supervision, and it is easy to embed into existing networks, which learn to compensate for the uncertainties and nonstationarities of few-shot learning. Our experimental results are in excellent agreement with theoretical predictions by different evaluation metrics, and they unveil the future potential of video surveillance to address endangered-animal detection in studies of their behavior and conservation.

Imposing Semantic Consistency of Local Descriptors for Few-Shot Learning

Few-shot learning suffers from the scarcity of labeled training data. Regarding local descriptors of an image as representations for the image could greatly augment existing labeled training data. Existing local descriptor based few-shot learning methods have taken advantage of this fact but ignore that the semantics exhibited by local descriptors may not be relevant to the image semantic. In this paper, we deal with this issue from a new perspective of imposing semantic consistency of local descriptors of an image. Our proposed method consists of three modules. The first one is a local descriptor extractor module, which can extract a large number of local descriptors in a single forward pass. The second one is a local descriptor compensator module, which compensates the local descriptors with the image-level representation, in order to align the semantics between local descriptors and the image semantic. The third one is a local descriptor based contrastive loss function, which supervises the learning of the whole pipeline, with the aim of making the semantics carried by the local descriptors of an image relevant and consistent with the image semantic. Theoretical analysis demonstrates the generalization ability of our proposed method. Comprehensive experiments conducted on benchmark datasets indicate that our proposed method achieves the semantic consistency of local descriptors and the state-of-the-art performance.

Information Symmetry Matters: A Modal-Alternating Propagation Network for Few-Shot Learning

Semantic information provides intra-class consistency and inter-class discriminability beyond visual concepts, which has been employed in Few-Shot Learning (FSL) to achieve further gains. However, semantic information is only available for labeled samples but absent for unlabeled samples, in which the embeddings are rectified unilaterally by guiding the few labeled samples with semantics. Therefore, it is inevitable to bring a cross-modal bias between semantic-guided samples and nonsemantic-guided samples, which results in an information asymmetry problem. To address this problem, we propose a Modal-Alternating Propagation Network (MAP-Net) to supplement the absent semantic information of unlabeled samples, which builds information symmetry among all samples in both visual and semantic modalities. Specifically, the MAP-Net transfers the neighbor information by the graph propagation to generate the pseudo-semantics for unlabeled samples guided by the completed visual relationships and rectify the feature embeddings. In addition, due to the large discrepancy between visual and semantic modalities, we design a Relation Guidance (RG) strategy to guide the visual relation vectors via semantics so that the propagated information is more beneficial. Extensive experimental results on three semantic-labeled datasets, i.e., Caltech-UCSD-Birds 200-2011, SUN Attribute Database and Oxford 102 Flower, have demonstrated that our proposed method achieves promising performance and outperforms the state-of-the-art approaches, which indicates the necessity of information symmetry.

IPGN: Interactiveness Proposal Graph Network for Human-Object Interaction Detection

Human-Object Interaction (HOI) Detection is an important task to understand how humans interact with objects. Most of the existing works treat this task as an exhaustive triplet 〈 human, verb, object 〉 classification problem. In this paper, we decompose it and propose a novel two-stage graph model to learn the knowledge of interactiveness and interaction in one network, namely, Interactiveness Proposal Graph Network (IPGN). In the first stage, we design a fully connected graph for learning the interactiveness, which distinguishes whether a pair of human and object is interactive or not. Concretely, it generates the interactiveness features to encode high-level semantic interactiveness knowledge for each pair. The class-agnostic interactiveness is a more general and simpler objective, which can be used to provide reasonable proposals for the graph construction in the second stage. In the second stage, a sparsely connected graph is constructed with all interactive pairs selected by the first stage. Specifically, we use the interactiveness knowledge to guide the message passing. By contrast with the feature similarity, it explicitly represents the connections between the nodes. Benefiting from the valid graph reasoning, the node features are well encoded for interaction learning. Experiments show that the proposed method achieves state-of-the-art performance on both V-COCO and HICO-DET datasets.