Recently, the leading performance of human pose estimation is dominated by top-down methods. Being a fundamental component in training and inference, data processing has not been systematically considered community, to best our knowledge. In this paper, we focus on problem find that devil estimator biased processing. Specifically, investigating standard state-of-the-art approaches mainly including transformation encoding-decoding, results obtained common flipping strategy are unaligned with...

Existing RGB and CNN-based methods in video action recognition mostly do not distinguish human body from the environment, thus easily overfit scenes objects of training sets. In this work, we present a conceptually simple, general high-performance framework for videos, aiming at person-centric modeling. The method, called Action Machine, is based on person bounding boxes instance-level analysis. It extends Inflated 3D ConvNet (I3D) by adding branch pose estimation 2D CNN pose-based...

World models, especially in autonomous driving, are trending and drawing extensive attention due to their capacity for comprehending driving environments. The established world model holds immense potential the generation of high-quality videos, policies safe maneuvering. However, a critical limitation relevant research lies its predominant focus on gaming environments or simulated settings, thereby lacking representation real-world scenarios. Therefore, we introduce DriveDreamer, pioneering...

Gait benchmarks empower the research community to train and evaluate high-performance gait recognition systems. Even though growing efforts have been devoted cross-view recognition, academia is restricted by current existing databases captured in controlled environment. In this paper, we contribute a new benchmark strong baseline for REcognition Wild (GREW). The GREW dataset constructed from natural videos, which contain hundreds of cameras thousands hours streams open With tremendous manual...

Both accuracy and efficiency are significant for pose estimation tracking in videos. State-of-the-art performance is dominated by two-stages top-down methods. Despite the leading results, these methods impractical real-world applications due to their separated architectures complicated calculation. This paper addresses task of articulated multi-person towards real-time speed. An end-to-end multi-task network (MTN) designed perform human detection, estimation, person re-identification (Re-ID)...

Existing methods in video action recognition mostly do not distinguish human body from the environment and easily overfit scenes objects. In this work, we present a conceptually simple, general high-performance framework for trimmed videos, aiming at person-centric modeling. The method, called Action Machine, takes as inputs videos cropped by person bounding boxes. It extends Inflated 3D ConvNet (I3D) adding branch pose estimation 2D CNN pose-based recognition, being fast to train test....

Being a fundamental component in training and inference, data processing has not been systematically considered human pose estimation community, to the best of our knowledge. In this paper, we focus on problem find that devil evolution is biased processing. Specifically, by investigating standard state-of-the-art approaches mainly including coordinate system transformation keypoint format (i.e., encoding decoding), results obtained common flipping strategy are unaligned with original ones...

The practical application requests both accuracy and efficiency on multi-person pose estimation algorithms. But the high fast inference speed are dominated by top-down methods bottom-up respectively. To make a better trade-off between efficiency, we propose novel framework, SIngle-network with Mimicking Point Learning for Bottom-up Human Pose Estimation (SIMPLE). Specifically, in training process, enable SIMPLE to mimic knowledge from high-performance pipeline, which significantly promotes...

This study investigates the development dilemma of ride-sharing services using real-world mobility datasets from nine cities and calibrated customers' price detour elasticity. Through massive numerical experiments, this reveals that while can benefit social welfare, it may also lead to a loss revenue for transportation network companies (TNCs) or drivers compared with solo-hailing, which limits TNCs' motivation develop services. Three key factors contributing are identified: (1) low...

Human pose estimation has witnessed a significant advance thanks to the development of deep learning. Recent human approaches tend directly predict location heatmaps, which causes quantization errors and inevitably deteriorates performance within reduced network output. Aim at solving it, we revisit heatmap-offset aggregation method propose Offset-guided Network (OGN) with an intuitive but effective fusion strategy for both two-stages Mask R-CNN. For estimation, greedy box generation is also...

Both appearance cue and constraint are vital for human pose estimation. However, there is a tendency in most existing works to overfitting the former overlook latter. In this paper, we propose Augmentation by Information Dropping (AID) verify tackle dilemma. Alone with AID as prerequisite effectively exploiting its potential, customized training schedules, which designed analyzing pattern of loss performance process from perspective information supplying. experiments, model-agnostic...

Human pose estimation are of importance for visual understanding tasks such as action recognition and human-computer interaction. In this work, we present a Multiple Stage High-Resolution Network (Multi-Stage HRNet) to tackling the problem multi-person in images. Specifically, follow top-down pipelines high-resolution representations maintained during single-person estimation. addition, multiple stage network cross feature aggregation adopted further refine keypoint position. The resulting...

Action recognition based on 3D skeleton sequences has gained considerable attention in recent years. Due to effectively representing the spatial and temporal characters of sequences, Covariance Matrix (CM) features combined with Long Short-Term Memory (LSTM) network is an effective reasonable roadmap enhance action accuracy. However, CM existing models are computed from raw data without normalization or static normalization. Moreover, a feature calculated all coordinates one frame, treating...

The advancement of mine industry produces substantial volumes acidic mining wastewater (AMW) annually, posing significant environmental risks. Porous ceramsite, a typical water treatment material, could be potentially apply for treating AMW with alkaline source. This study delves into the efficiency and mechanism ceramsite (WTC) derived from dredged sludge, biomass waste, source as raw materials. Results show that WTC 8% CaCO3 effectively increased pH to 7 within nearly 60 min. mineral...

Gait benchmarks empower the research community to train and evaluate high-performance gait recognition systems. Even though growing efforts have been devoted cross-view recognition, academia is restricted by current existing databases captured in controlled environment. In this paper, we contribute a new benchmark strong baseline for REcognition Wild (GREW). The GREW dataset constructed from natural videos, which contain hundreds of cameras thousands hours streams open With tremendous manual...

The practical application requests both accuracy and efficiency on multi-person pose estimation algorithms. But the high fast inference speed are dominated by top-down methods bottom-up respectively. To make a better trade-off between efficiency, we propose novel framework, SIngle-network with Mimicking Point Learning for Bottom-up Human Pose Estimation (SIMPLE). Specifically, in training process, enable SIMPLE to mimic knowledge from high-performance pipeline, which significantly promotes...

Student behavior in the classroom is an important part of analysis process and effectiveness. Due to different physiological psychological states learners environment, it very common that there are differences between same types actions, diversity actions will change over time. To solve online recognition problem kind with high a domain adaptive continual learning method for skeleton-based proposed, which achieves transfer ability models actions. Experiments show this has better...