Inaccurate detections remain a critical bottleneck in 3D multi-object tracking (MOT). Recent detection fusion-based methods incorporate camera detections as supplementary to reduce false detections and compensate for missing ones in LiDAR. However, their unidirectional camera-LiDAR correction lacks a feedback mechanism, precluding iterative mutual refinement between modalities for more robust LiDAR-based tracking. Inspired by the coarse-to-fine strategy in two-stage object detection, we introduceCrossTracker, a novel two-stage framework for online multi-modal 3D MOT. CrossTracker first constructs coarse camera and LiDAR trajectories independently, then performs trajectory fusion using both current and historical frames, without requiring future data. This ensures more robust mutual refinement between modalities. Specifically, CrossTracker comprises three core modules: i) the multi-modal modeling (M3) module, which fuses data from images, point clouds, and even planar geometry derived from images to establish a robust tracking constraint; ii) the coarse trajectory generation (C-TG) module, which independently generates coarse trajectories for both modalities using the M3constraint; and iii) the trajectory fusion (TF) module, which applies mutual refinement between coarse LiDAR and camera trajectories through cross correction to ensure robust LiDAR trajectories. Extensive experiments show that CrossTracker outperforms 19 state-of-the-art methods, highlighting its effectiveness in leveraging the synergistic strengths of camera and LiDAR sensors for robust multi-modal 3D MOT. The code is available at https://github.com/lipeng-gu/CrossTracker.

Searching by image is popular yet still challenging in e-commerce due to the extensive interference arising from (i) data variations (e.g., background, pose, visual angle, brightness) of real-world captured images and (ii) similar images in the query dataset. This article studies a practically meaningful problem of beauty product retrieval (BPR) by neural networks. We broadly extract different types of image features and raise an intriguing question that whether these features are beneficial to (i) suppress data variations of real-world captured images and (ii) distinguish one image from others which look very similar but are intrinsically different beauty products in the dataset, therefore leading to an enhanced capability of BPR. To answer it, we present a novel v ariable-attention neural network to understand the combination of m ultiple features (termed VM-Net) of beauty product images. Considering that there are few publicly released training datasets for BPR, we establish a new dataset with more than one million images classified into more than 20K categories to improve both the generalization and anti-interference abilities of VM-Net and other methods. We verify the performance of VM-Net and its competitors on the benchmark dataset Perfect-500K, where VM-Net shows clear improvements over the competitors in terms of \(MAP@7\) . The source code and dataset will be released upon publication.