2-Methyl­carbamoyl-4-{4-[3-(trifluoro­meth­yl)benzamido]phen­oxy}pyridinium 4-methyl­benzene­sulfonate monohydrate

The asymmetric unit of the title compound, C21H17F3N3O3 +·C7H7O3S−·H2O, contains two formula units. In one of the cations, the pyridinium and trifluoro­methyl benzene rings form dihedral angles of 87.42 (8) and 45.92 (8)°, respectively, with the central benzene ring [79.56 (8) and 43.52 (8)° in the other cation]. In the crystal structure, N—H⋯O, O—H⋯O and C—H⋯O hydrogen bonds link the ions and water mol­ecules, forming a three-dimensional network

4-[4-(3-Methoxy­benzamido)phen­oxy]-N-methyl­picolinamide

In the title compound, C21H19N3O4, the central benzene ring makes dihedral angles of 78.54 (6) and 75.30 (6)° with the pyridine and 3-methoxy­phenyl rings, respectively. An intra­molecular N—H⋯N interaction occurs, generating an S(?). The crystal packing shows inter­molecular N—H⋯O hydrogen-bonding inter­actions between the N—H groups and the O atoms of the 3-methoxy­phenyl ring and the carbonyl groups of the amide functions. Inter­molecular C—H⋯O inter­actions are also present

4-{[4-(3,5-Dimeth­oxy­benzamido)­phen­yl]sulfan­yl}-N-methyl­pyridine-2-carboxamide

There are two independent mol­ecules in the asymmetric unit of the title compound, C22H21N3O4S. The central benzene ring makes dihedral angles of 74.28 (6) and 68.84 (6)° with the pyridine and 3,5-dimeth­oxy­phenyl rings, respectively, in one molecule [86.66 (6) and 81.14 (6)° respectively, in the other]. Each of the mol­ecules forms a centrosymmetric dimer with another mol­ecule via pairs of inter­molecular N—H⋯O hydrogen bonds. These hydrogen bonds connect the N—H groups and the O atoms of the carbonyl groups next to the 3,5-dimeth­oxy­phenyl rings. Additional inter­molecular N—H⋯O inter­actions link the dimers in the crystal structure

You Only Need Two Detectors to Achieve Multi-Modal 3D Multi-Object Tracking

Firstly, a new multi-object tracking framework is proposed in this paper based on multi-modal fusion. By integrating object detection and multi-object tracking into the same model, this framework avoids the complex data association process in the classical TBD paradigm, and requires no additional training. Secondly, confidence of historical trajectory regression is explored, possible states of a trajectory in the current frame (weak object or strong object) are analyzed and a confidence fusion module is designed to guide non-maximum suppression of trajectory and detection for ordered association. Finally, extensive experiments are conducted on the KITTI and Waymo datasets. The results show that the proposed method can achieve robust tracking by using only two modal detectors and it is more accurate than many of the latest TBD paradigm-based multi-modal tracking methods. The source codes of the proposed method are available at https://github.com/wangxiyang2022/YONTD-MOTComment: 10 pages, 9 figure