3D multiple object tracking (MOT) plays a crucial role in autonomous driving perception. Recent end-to-end query-based trackers simultaneously detect and track objects, which have shown promising potential for the 3D MOT task. However, existing methods are still in the early stages of development and lack systematic improvements, failing to track objects in certain complex scenarios, like occlusions and the small size of target object’s situations. In this paper, we first summarize the current end-to-end 3D MOT framework by decomposing it into three constituent parts: query initialization, query propagation, and query matching. Then we propose corresponding improvements, which lead to a strong yet simple tracker: S2-Track. Specifically, for query initialization, we present 2D-Prompted Query Initialization, which leverages predicted 2D object and depth information to prompt an initial estimate of the object’s 3D location. For query propagation, we introduce an Uncertainty-aware Probabilistic Decoder to capture the uncertainty of complex environment in object prediction with probabilistic attention. For query matching, we propose a Hierarchical Query Denoising strategy to enhance training robustness and convergence. As a result, our S2-Track achieves state-of-the-art performance on nuScenes benchmark, i.e., 66.3% AMOTA on test split, surpassing the previous best end-to-end solution by a significant margin of 8.9% AMOTA. We achieve 1st place on the nuScenes tracking task leaderboard.

Semi-Supervised Object Detection (SSOD) aims to improve performance by leveraging a large amount of unlabeled data. Existing works usually adopt the teacher-student framework to enforce student to learn consistent predictions over the pseudo-labels generated by teacher. However, the performance of the student model is limited since the noise inherently exists in pseudo-labels. In this paper, we investigate the causes and effects of noisy pseudo-labels and propose a simple yet effective approach denoted as Self-Correction Mean Teacher(SCMT) to reduce the adverse effects. Specifically, we propose to dynamically re-weight the unsupervised loss of each student's proposal with additional supervision information from the teacher model, and assign smaller loss weights to possible noisy proposals. Extensive experiments on MS-COCO benchmark have shown the superiority of our proposed SCMT, which can significantly improve the supervised baseline by more than 11% mAP under all 1%, 5% and 10% COCO-standard settings, and surpasses state-of-the-art methods by about 1.5% mAP. Even under the challenging COCO-additional setting, SCMT still improves the supervised baseline by 4.9% mAP, and significantly outperforms previous methods by 1.2% mAP, achieving a new state-of-the-art performance.

Tumor localization and segmentation in breast ultrasound (BUS) images is an important as well as intractable problem for computer-aided diagnosis (CAD) due to the high variation in shape and appearance. We propose a novel algorithm in this paper without making any assumption on tumor, compared to most previous works. Heterogeneous features are collected via a hierarchical over-segmentation framework, which we have shown has the multiscale property. The superpixels are then classified with their confidences nested into the bottom layer. The ultimate segmentation is made by using an efficient conditional random field model. Experiments on challenging data set show that our algorithm is able to handle almost all kinds of benign and malignant tumors, and also confirm the superiority of our work through a comparison with other two different approaches.

State-of-the-art pansharpening methods generally inject the spatial details extracted from the panchromatic (Pan) image into the multispectral (MS) images by considering different injection models. The fusion performances severely rely on the accuracy of the modeling and the estimation of model parameters. In this letter, we propose an optimized approach to avoid explicitly modeling the detail injection process. The solution employs the gradient field of the Pan image for spatial enhancement. The low-pass (LP) version of the fused bands are constrained to be the most similar to the original MS bands to preserve the spectral characteristics. We use the local correlation coefficients between the MS band and the LP version of the Pan image to adjust the two sources of information based on a simple observation, and it is further optimized by considering the overall quality index Q4. Experimental results demonstrate that the proposed method outperforms the state-of-the-art multiresolution analysis-based methods.

We concern the problem of learning a Mahalanobis distance metric for improving nearest neighbor classification. Our work is built upon the large margin nearest neighbor (LMNN) classification framework. Due to the semidefiniteness constraint in the optimization problem of LMNN, it is not scalable in terms of the dimensionality of the input data. The original LMNN solver partially alleviates this problem by adopting alternating projection methods instead of standard interior-point methods. Still, at each iteration, the computation complexity is at least O(D3) (D is the dimension of input data). In this work, we propose a column generation based algorithm to solve the LMNN optimization problem much more efficiently. Our algorithm is much more scalable in tha tat each iteration, it does not need full eigen-decomposition. Instead, we only need to find the leading eigen value and its corresponding eigen vector, which is of O(D2) complexity. Experiments show the efficiency and efficacy of our algorithms.

Boosting combines a set of moderately accurate weak classifiers to form a highly accurate predictor. Compared with binary boosting classification, multi-class boosting received less attention. We propose a novel multi-class boosting formulation here. Unlike most previous multi-class boosting algorithms which decompose a multi-boost problem into multiple independent binary boosting problems, we formulate a direct optimization method for training multi-class boosting. Moreover, by explicitly deriving the La-grange dual of the formulated primal optimization problem, we design totally-corrective boosting using the column generation technique in convex optimization. At each iteration, all weak classifiers' weights are updated. Our experiments on various data sets demonstrate that our direct multi-class boosting achieves competitive test accuracy compared with state-of-the-art multi-class boosting in the literature.