Existing methods of multiple human parsing (MHP) apply deep models to learn instance-level representations for segmenting each person into non-overlapped body parts. However, learned representations often contain many spurious correlations that degrade model generalization, leading learned models to be vulnerable to visually contextual variations in images (e.g., unseen image styles/external interventions). To tackle this, we present a causal property integrated parsing model termed CPI-Parser, which is driven by fundamental causal principles involving two causal properties for human parsing (i.e., the causal diversity and the causal invariance). Specifically, we assume that an image is constructed by a mix of causal factors (the characteristics of body parts) and non-causal factors (external contexts), where only the former ones decide the essence of human parsing. Since causal/non-causal factors are unobservable, the proposed CPI-Parser is required to separate key factors that satisfy the causal properties from an image. In this way, the parser is able to rely on causal factors w.r.t relevant evidence rather than non-causal factors w.r.t spurious correlations, thus alleviating model degradation and yielding improved parsing ability. Notably, the CPI-Parser is designed in a flexible way and can be integrated into any existing MHP frameworks. Extensive experiments conducted on three widely used benchmarks demonstrate the effectiveness and generalizability of our method. Code and models are released (https://github.com/HAG-uestc/CPI-Parser) for research purpose.

Multiple human parsing (MHP) is typically treated as two sub-tasks, i.e., instance separation and body part segmentation. Existing methods usually tackle the sub-tasks by adopting a two-stage strategy, which regards MHP as an ROI-based (i.e., detect-then-segment) or grouping-based (i.e., segment-then-grouping) paradigm. However, the strong dependence between the two sub-tasks limits the potential of an MHP method, since it often requires qualified prior predictions. Besides, isolated models responsible for the two sub-tasks bring a significant computational burden. Unlike existing methods, we regard MHP as a hierarchical set prediction problem and handle two sub-tasks using several landmarks of body parts. Motivated by this, we propose a novel multiple human parser with representative sets, termed ReSParser. In ReSParser, several landmarks of body parts are hierarchically estimated, resulting in coarse-to-fine representative sets. After that, each representative set is adaptively responsible for segmenting pixels into semantically consistent regions belonging to the corresponding person. In such a manner, the ReSParser simultaneously addresses two sub-tasks in a fully convolutional fashion, thus eliminating the dependence between two sub-tasks and significantly alleviating computational complexity. Extensive experiments on two challenging benchmarks demonstrate that our proposed ReSParser is an efficient framework with a superior parsing performance, which significantly outperforms that of other ROI-free yet grouping-free methods. Besides, it achieves competitive results to that of the best two-stage methods such as RP-RCNN, but requires a much lower inference time, showing a good precision-speed trade-off. We hope the ReSParser serves as a new baseline for multiple human parsing research in the future.

The divide-and-conquer strategy, which interprets part-level action parsing as a detect-then-parsing pipeline, has been widely used and become a general tool for part-level action understanding. However, existing methods that derive from the strategy usually suffer from either strong dependence on prior detection or high computational complexity. In this paper, we present the first fully end-to-end part-level action parsing framework with transformers, termed PATR. Unlike existing methods, our method regards part-level action parsing as a hierarchical set prediction problem and unifies person detection, body part detection, and action state recognition into one model. In PATR, predefined learnable representations, including general instance representations and general part representations, are guided to adaptively attend to the image features that are relevant to target body parts. Then, conditioning on corresponding learnable representations, attended image features are hierarchically decoded into corresponding semantics (i.e., person location, body part location, and action states for each body part). In this way, PATR relies on characteristics of body parts, instead of prior predictions like bounding boxes, to parse action states, thus removing the strong dependence between sub-tasks and eliminating the computational burdens caused by the multi-stage paradigm. Extensive experiments conducted on challenging Kinetic-TPS indicate that our method achieves very competitive results. In particular, our model outperforms all state-of-the-art part-level action parsing approaches by a margin, reaching around 3.8±2.0% Accphigher than previous methods. These findings indicate the potential of PATR to serve as a new baseline for part-level action parsing methods in the future. Our code and models are publicly available.1

Existing solutions to lightweight human pose estimation typically adopt a depthwise separable strategy, i.e., a normal 2D convolution is factorized into channel aggregation and spatial aggregation. However, this strategy cannot well capture multi-scale Effective Receptive Field (ERF), which is essential to dense prediction tasks like human pose estimation. To address this issue, we propose a novel lightweight network for human pose estimation, namely effective aggregation net (EANet). In EANet, we introduce two lightweight computational units: effective channel aggregating (ECA) and effective spatial aggregating (ESA), which are respectively responsible for channel-wise feature aggregation and pixel-wise feature aggregation. Unlike typical channel-wise aggregation using pointwise (1 × 1) convolution, the ECA aggregates few feature points that are estimated as effective ones. Moreover, the ESA is designed with re-parameterizing techniques, and it aggregates effective spatial feature points with multi-scale shared convolutions. Comprehensive experiments are conducted on three challenging datasets, i.e., COCO, Crowd-Pose, Wholebody-COCO. Our EANet demonstrates superior results on human pose estimation over previous lightweight methods, reaching a new state-of-the-art performance with a good trade-off. Our code and models are publicly available1.

Part-level attribute parsing is a fundamental but challenging task, which requires the region-level visual understanding to provide explainable details of body parts. Most existing approaches address this problem by adding a regional convolutional neural network (RCNN) with an attribute prediction head to a two-stage detector, in which attributes of body parts are identified from localwise part boxes. However, localwise part boxes with limit visual clues (i.e., part appearance only) lead to unsatisfying parsing results, since attributes of body parts are highly dependent on comprehensive relations among them. In this article, we propose a knowledge-embedded RCNN (KE-RCNN) to identify attributes by leveraging rich knowledge, including implicit knowledge (e.g., the attribute “above-the-hip” for a shirt requires visual/geometry relations of shirt-hip) and explicit knowledge (e.g., the part of “shorts” cannot have the attribute of “hoodie” or “lining”). Specifically, the KE-RCNN consists of two novel components, that is: 1) implicit knowledge-based encoder (IK-En) and 2) explicit knowledge-based decoder (EK-De). The former is designed to enhance part-level representation by encoding part–part relational contexts into part boxes, and the latter one is proposed to decode attributes with a guidance of prior knowledge about part–attribute relations. In this way, the KE-RCNN is plug-and-play, which can be integrated into any two-stage detectors, for example, Attribute-RCNN, Cascade-RCNN, HRNet-based RCNN, and SwinTransformer-based RCNN. Extensive experiments conducted on two challenging benchmarks, for example, Fashionpedia and Kinetics-TPS, demonstrate the effectiveness and generalizability of the KE-RCNN. In particular, it achieves higher improvements over all existing methods, reaching around 3% of${\mathrm{ AP}}^{\mathrm{ all}}_{\rm IoU+F_{1}}$on Fashionpedia and around 4% of${\mathrm{ Acc}}_{p}$on Kinetics-TPS. Code and models are publicly available at:https://github.com/sota-joson/KE-RCNN.

Nowadays, the supermarket scale and items increase gradually. Customers will take a long time if they want to buy all things they need. Sometimes customers don't know the location of the goods, or they have to walk a repeated route, which leading to a waste of time, so we need to find the best shopping route. In this paper, we use genetic algorithm to solve this problem for customers. Due to the particularity of this problem, the start and end points of route are fixed, so we need to do some change for operator of GA. This algorithm can calculate a shortest route that isn't repeated and takes the shortest time to help customers shopping quickly. In the process of experiment, we apply it into a shopping route problem with twenty commodities. The results show that this algorithm can find the best solution after certain number of iterations.