MSMD-Net: Deep Stereo Matching with Multi-scale and Multi-dimension Cost Volume

Deep end-to-end learning based stereo matching methods have achieved great success as witnessed by the leaderboards across different benchmarking datasets (KITTI, Middlebury, ETH3D, etc). However, real scenarios not only require approaches to have state-of-the-art performance but also real-time speed and domain-across generalization, which cannot be satisfied by existing methods. In this paper, we propose MSMD-Net (Multi-Scale and Multi-Dimension) to construct multi-scale and multi-dimension cost volume. At the multi-scale level, we generate four 4D combination volumes at different scales and integrate them with an encoder-decoder process to predict an initial disparity estimation. At the multi-dimension level, we additionally construct a 3D warped correlation volume and use it to refine the initial disparity map with residual learning. These two dimensional cost volumes are complementary to each other and can boost the performance of disparity estimation. Additionally, we propose a switch training strategy to alleviate the overfitting issue appeared in the pre-training process and further improve the generalization ability and accuracy of final disparity estimation. Our proposed method was evaluated on several benchmark datasets and ranked first on KITTI 2012 leaderboard and second on KITTI 2015 leaderboard as of September 9. In addition, our method shows strong domain-across generalization and outperforms best prior work by a noteworthy margin with three or even five times faster speed. The code of MSMD-Net is available at https://github.com/gallenszl/MSMD-Net

N 1,N 4,3,6-Tetra­methyl-1,2,4,5-tetra­zine-1,4-dicarboxamide

The asymmetric unit of the title compound, C8H14N6O2, contains two independent mol­ecules. In one mol­ecule, the amide-substituted N atoms of the tetra­zine ring deviate from the plane [maximum deviation = 0.028 (1) Å] through the four other atoms in the ring by 0.350 (2) and 0.344 (2) Å, forming a boat conformation, and the mean planes of the two carboxamide groups form dihedral angles of 10.46 (13) and 20.41 (12)° with the four approximtely planar atoms in the tetra­zine ring. In the other mol­ecule, the amide-substituted N atoms of the tetra­zine ring deviate from the plane [maximum deviation = 0.033 (1) Å] through the four other atoms in the ring by 0.324 (2) and 0.307 (2) Å, forming a boat conformation, and the mean planes of the two carboxamide groups form dihedral angles of 14.66 (11) and 17.08 (10)° with the four approximately planar atoms of the tetra­zine ring. In the crystal, N—H⋯O hydrogen bonds connect mol­ecules to form a two-dimensional network parallel to (1-1-1). Intra­molecular N—H⋯N hydrogen bonds are observed

3,6-Dimethyl-N 1,N 4-bis­(pyridin-2-yl)-1,2,4,5-tetra­zine-1,4-dicarboxamide

In the title mol­ecule, C16H16N8O2, four atoms of the tetra­zine ring are coplanar, with the largest deviation from the plane being 0.0236 (12) Å; the other two atoms of the tetra­zine ring deviate on the same side from this plane by 0.320 (4) and 0.335 (4) Å. Therefore, the central tetra­zine ring exhibits a boat conformation. The dihedral angles between the mean plane of the four coplanar atoms of the tetrazine ring and the two pyridine rings are 26.22 (10) and 6.97 (5)°. The two pyridine rings form a dihedral angle of 31.27 (8)°. In the molecule, there are a number of short C—H⋯O interactions. In the crystal, molecules are linked via a C—H⋯O interaction to form zigzag chains propagating along the [010] direction

Hierarchically Fusing Long and Short-Term User Interests for Click-Through Rate Prediction in Product Search

Estimating Click-Through Rate (CTR) is a vital yet challenging task in personalized product search. However, existing CTR methods still struggle in the product search settings due to the following three challenges including how to more effectively extract users' short-term interests with respect to multiple aspects, how to extract and fuse users' long-term interest with short-term interests, how to address the entangling characteristic of long and short-term interests. To resolve these challenges, in this paper, we propose a new approach named Hierarchical Interests Fusing Network (HIFN), which consists of four basic modules namely Short-term Interests Extractor (SIE), Long-term Interests Extractor (LIE), Interests Fusion Module (IFM) and Interests Disentanglement Module (IDM). Specifically, SIE is proposed to extract user's short-term interests by integrating three fundamental interests encoders within it namely query-dependent, target-dependent and causal-dependent interest encoder, respectively, followed by delivering the resultant representation to the module LIE, where it can effectively capture user long-term interests by devising an attention mechanism with respect to the short-term interests from SIE module. In IFM, the achieved long and short-term interests are further fused in an adaptive manner, followed by concatenating it with original raw context features for the final prediction result. Last but not least, considering the entangling characteristic of long and short-term interests, IDM further devises a self-supervised framework to disentangle long and short-term interests. Extensive offline and online evaluations on a real-world e-commerce platform demonstrate the superiority of HIFN over state-of-the-art methods.Comment: accpeted by CIKM'22 as a Full Pape