ISLS: An Illumination-Aware Sauce-Packet Leakage Segmentation Method.

The segmentation of abnormal regions is vital in smart manufacturing. The blurring sauce-packet leakage segmentation task (BSLST) is designed to distinguish the sauce packet and the leakage's foreground and background at the pixel level. However, the existing segmentation system for detecting sauce-packet leakage on intelligent sensors encounters an issue of imaging blurring caused by uneven illumination.

NRVC: Neural Representation for Video Compression with Implicit Multiscale Fusion Network.

Recently, end-to-end deep models for video compression have made steady advancements. However, this resulted in a lengthy and complex pipeline containing numerous redundant parameters. The video compression approaches based on implicit neural representation (INR) allow videos to be directly represented as a function approximated by a neural network, resulting in a more lightweight model, whereas the singularity of the feature extraction pipeline limits the network's ability to fit the mapping function for video frames.

A Thermal Infrared Pedestrian-Detection Method for Edge Computing Devices.

The thermal imaging pedestrian-detection system has excellent performance in different lighting scenarios, but there are problems regarding weak texture, object occlusion, and small objects. Meanwhile, large high-performance models have higher latency on edge devices with limited computing power. To solve the above problems, in this paper, we propose a real-time thermal imaging pedestrian-detection method for edge computing devices.

Learning Visible Thermal Person Re-Identification via Spatial Dependence and Dual-Constraint Loss.

Visible thermal person re-identification (VT Re-ID) is the task of matching pedestrian images collected by thermal and visible light cameras. The two main challenges presented by VT Re-ID are the intra-class variation between pedestrian images and the cross-modality difference between visible and thermal images. Existing works have principally focused on local representation through cross-modality feature distribution, but ignore the internal connection of the local features of pedestrian body parts.

Dual Catalytic Hairpin Assembly-Based Automatic Molecule Machine for Amplified Detection of Auxin Response Factor-Targeted MicroRNA-160.

MicroRNA160 plays a crucial role in plant development by negatively regulating the auxin response factors (ARFs). In this manuscript, we design an automatic molecule machine (AMM) based on the dual catalytic hairpin assembly (D-CHA) strategy for the signal amplification detection of miRNA160. The detection system contains four hairpin-shaped DNA probes (HP1, HP2, HP3, and HP4).

Cascaded Cross-Modality Fusion Network for 3D Object Detection.

We focus on exploring the LIDAR-RGB fusion-based 3D object detection in this paper. This task is still challenging in two aspects: (1) the difference of data formats and sensor positions contributes to the misalignment of reasoning between the semantic features of images and the geometric features of point clouds. (2) The optimization of traditional IoU is not equal to the regression loss of bounding boxes, resulting in biased back-propagation for non-overlapping cases.