Chemical processes involve complex operational mechanisms and high-dimensional variables with multiscale dependencies and temporal covariate shifts, posing significant challenges for soft sensing implementation. To address these issues, a self-constrained weighted garrote ordered memory network with adaptive distribution learning is proposed for industrial soft sensor modeling. Specifically, an improved long short-term ordered memory (LSTOM) architecture with a composite hierarchical information update mechanism is designed to capture multiscale dynamic features from industrial time-series data. Meanwhile, an adaptive distribution learning module is embedded into the LSTOM architecture to match high-order feature distributions across different temporal periods via maximally dissimilar segments. With the composite hierarchical update mechanism and embedded adaptive distribution learning, the proposed approach improves generalization in soft sensor modeling under multi-timescale dependencies and temporal covariate shifts. Finally, the shrinkage coefficients of the self-constrained weighted garrote are embedded into the LSTOM’s input weights to eliminate redundant variables and promote structural sparsity. The effectiveness of the proposed approach is validated on a public industrial-scale penicillin fermentation process and a real-world flue gas desulfurization case.

Gated recurrent units (GRUs) have been successfully applied to various industrial soft-sensor tasks. However, their linear coupling constraint in the hidden-state limits information propagation, while high-dimensional inputs and structural redundancies further challenge modeling. To address these issues, a speed-adaptive gated recurrent unit (SAGRU) network with weights dual-garrote regularization (WDG-SAGRU) is proposed for product quality prediction. Specifically, a speed-adaptive gating mechanism is integrated into the GRU update gate through a nonlinear exponential transformation. This mechanism is incorporated into the hidden-state update to break the linear coupling constraint, enhancing information flow and feature extraction. Meanwhile, the dual-garrote shrinkage coefficients are embedded into SAGRU’s input and hidden weight matrices, enabling synchronized optimization of input variable selection and structural sparsity. The proposed WDG-SAGRU is validated against state-of-the-art methods on a numerical example and an operational flue gas desulfurization system in a thermal power plant.

Chemical processes involve complex physical and chemical mechanisms that exhibit slow and fast-varying features and nonstationary characteristics, making it difficult for single model-based methods to satisfactorily extract both slow and nonstationary fast-varying features for soft sensing. To address this issue, we propose a two-stream slow and nonstationary fast feature (TS-SNFF) model. This model includes a slow feature stream (SF-stream) and a nonstationary fast feature stream (NFF-streama). In the SF-stream, an encoder-decoder based Siamese network and a linear mapping layer are used for slow feature extraction. It employs long-short term memory (LSTM) networks as encoder and decoder units. Meanwhile, the NFF-stream utilizes the LSTM, differential LSTM (D-LSTM), and linear mapping layers for nonstationary fast feature extraction. The D-LSTM unit is established by embedding differential operations into the LSTM cell to obtain the nonstationary information. Then, the obtained features are fused using the merging layer, followed by a multilayer perceptron as the regressor. The proposed TS-SNFF model is utilized to address the slow and fast-varying dynamics in nonstationary conditions and nonlinearity problem in chemical processes. The effectiveness and superiority of the proposed method are demonstrated in a numerical example and an industrial process case

Spatial-temporal action detection is a vital part of video understanding. Current spatial-temporal action detection methods mostly use an object detector to obtain person candidates and classify these person candidates into different action categories. So-called two-stage methods are heavy and hard to apply in real-world applications. Some existing methods build one-stage pipelines, But a large performance drop exists with the vanilla one-stage pipeline and extra classification modules are needed to achieve comparable performance. In this paper, we explore a simple and effective pipeline to build a strong one-stage spatial-temporal action detector. The pipeline is composed by two parts: one is a simple end-to-end spatial-temporal action detector. The proposed end-to-end detector has minor architecture changes to current proposal-based detectors and does not add extra action classification modules. The other part is a novel labeling strategy to utilize unlabeled frames in sparse annotated data. We named our model as SE-STAD. The proposed SE-STAD achieves around 2% mAP boost and around 80% FLOPs reduction. Our code will be released at https://github.com/4paradigm-CV/SE-STAD

Weakly supervised vision tasks, including detection and segmentation, have attracted much attention in the vision community recently. However, the lack of detailed and precise annotations in the weakly supervised case leads to a large accuracy gap between weakly- and fully-supervised methods. In this article, we propose a new framework, Salvage of Supervision (SoS), with the key idea being to effectively harness every potentially useful supervisory signal in weakly supervised vision tasks. Starting with weakly supervised object detection (WSOD), we propose SoS-WSOD to shrink the technology gap between WSOD and FSOD, which utilizes the weak image-level labels, the pseudo-labels, and the power of semi-supervised object detection for WSOD. Moreover, SoS-WSOD removes restrictions in traditional WSOD methods, including the reliance on ImageNet pretraining and inability to use modern backbones. The SoS framework also extends to weakly supervised semantic segmentation and instance segmentation. On several weakly supervised vision benchmarks, SoS achieves significant performance boost and generalization ability.

Weakly supervised object detection (WSOD) has recently attracted much attention. However, the lack of bounding-box supervision makes its accuracy much lower than fully supervised object detection (FSOD), and currently modern FSOD techniques cannot be applied to WSOD. To bridge the performance and technical gaps between WSOD and FSOD, this paper proposes a new framework, Salvage of Supervision (SoS), with the key idea being to harness every potentially useful supervisory signal in WSOD: the weak image-level labels, the pseudo-labels, and the power of semi-supervised object detection. This paper proposes new approaches to utilize these weak and noisy signals effectively, and shows that each type of supervisory signal brings in notable improvements, outperforms existing WSOD methods (which mainly use only the weak labels) by large margins. The proposed SoS- WSOD method also has the ability to freely use modern FSOD techniques. SoS-WSOD achieves 64.4 mAP50on VOC2007, 61.9 mAP50on VOC2012 and 16.6 mAP50:95on MS-COCO, and also has fast inference speed. Ablations and visualization further verify the effectiveness of SoS.