Test-time adaptation updates pretrained models on the fly to handle distribution shifts in test data. While existing research has focused on stable optimization during adaptation, less attention has been given to enhancing model representations for adaptation capability. To address this gap, we propose Test-Time Ensemble (TTE) grounded in the intriguing property of linear mode connectivity. TTE leverages ensemble strategies during adaptation: 1) adaptively averaging the parameter weights of assorted test-time adapted models and 2) incorporating dropout to further promote representation diversity. These strategies encapsulate model diversity into a single model, avoiding computational burden associated with managing multiple models. Besides, we propose a robust knowledge distillation scheme to prevent model collapse, ensuring stable optimization and preserving the ensemble benefits during adaptation. Notably, TTE integrates seamlessly with existing TTA approaches, advancing their adaptation capabilities. In extensive experiments, integration with TTE consistently outperformed baseline models across various challenging scenarios, demonstrating its effectiveness and general applicability.

Deep learning algorithms require large amounts of labeled data for effective performance, but the presence of noisy labels often significantly degrade their performance. Although recent studies on designing a robust objective function to label noise, known as the robust loss method, have shown promising results for learning with noisy labels, they suffer from the issue of underfitting not only noisy samples but also clean ones, leading to suboptimal model performance. To address this issue, we propose a novel learning framework that selectively suppresses noisy samples while avoiding underfitting clean data. Our framework incorporates label confidence as a measure of label noise, enabling the network model to prioritize the training of samples deemed to be noise-free. The label confidence is based on the robust loss methods, and we provide theoretical evidence that our method can reach the optimal point of the robust loss, subject to certain conditions. Furthermore, the proposed method is generalizable and can be combined with existing robust loss methods, making it suitable for a wide range of applications of learning with noisy labels. We evaluate our approach on both synthetic and real-world datasets, and the experimental results demonstrate its effectiveness in achieving outstanding classification performance compared to state-of-the-art methods.

This paper proposes a novel unified framework to solve the 3-D localization and tracking problem that occurs multiple camera settings with overlapping views. The main challenge is to overcome the uncertainty of the back projection arising from the challenges of ground point detection in an environment that includes severe occlusions and the unknown heights of people. To tackle this challenge, we establish a Bayesian learning framework that maximizes a posterior over the trajectory assignments and 3-D positions for given detections from multiple cameras. To solve the Bayesian learning problem in a tractable form, we develop an expectation-maximization scheme based on the variation inference approximation, where the probability distributions are designed to follow Boltzmann distributions of seven terms that are induced from multicamera tracking settings. The experimental results show that the proposed method outperforms the state-of-the-art methods on the challenging multicamera data sets.

In this paper, we propose a novel deep-learning architecture for accurate 3-D localization and tracking of a pedestrian using multiple cameras. The deep-learning network is composed of two networks: detection network and localization network. The detection network yields the pedestrian detections and the localization network estimates the ground position of a pedestrian within its detection box. In addition, an attentional pass filter is introduced to effectively connect the two networks. Using the detection proposals and their 2-D grounding positions obtained from the two networks, multi-camera multi-target 3-D localization and tracking algorithm is developed through min-cost network flow approach. In the experiments, it is shown that the proposed method improves the performance of 3-D localization and tracking.

We propose an online tracking algorithm for multiple target tracking with multiple cameras. In this paper, we suggest a multiple hypothesis tracking (MHT) framework to find an unknown number of multiple tracks through the spatio-temporal association between tracklets generated from multiple cameras. In this framework, the MHT is realized online by solving the maximum weighted clique problem (MWCP) at every frame to estimate the 3D trajectories of the targets. To handle the NP-hard issue of the MWCP, we propose a novel online scheme that formulates the MWCP using feedback information from the previous frame's result to find optimal tracks at every frame. This scheme enables the MWCP to be formulated by multiple subproblems and will significantly reduce the computation. The experiments show that the proposed algorithm performs comparably with the state-of-the-art batch algorithms, even though it adopts an online scheme.

This paper proposes a novel multi-target tracking method which jointly solves a data association problem using images from multiple cameras. In this work, the spatiotemporal data association problem is formulated as a multidimensional assignment problem (MDA). To achieve a fast, efficient, and easily implementable approximation algorithm, we solve the MDA problem approximately by solving a sequence of bipartite matching problems using random splitting and merging operations. In this formulation, we design a new cost function, considering the accuracy in 3D reconstruction, motion smoothness, visibility from cameras, starting/ending at entrance and exit zone, and false positive. Our approach reconstructs 3D trajectories that represent people’s movement as 3D cylinders whose locations are estimated considering all adjacent frames. The experiments illustrate the proposed method shows the state-of-the-art performance in challenging multi-camera datasets and the computational efficiency with 8 times faster computation than the existing BIP approach.

This paper presents an online data association approach to handle new detection and missing detection problems of multiple people tracking in crowded scenes. The key contribution of our paper includes two aspects: one is automatic initiation of tracking models for newly appeared detections and the other is selective update of tracking models for missing detections by occlusions. For the automatic initiation, instead of the conventional matching algorithm, our data association is solved by a maximum a posteriori probability (MAP) formulation considering object's size, center distance, motion and appearance. The selective update scheme for tracking models is developed by considering the spatial information which prevents the tracking model from being corrupted with unreliable information. Even if the head detector is less discriminative due to low number of features than full body and only the recent tracking models are used for online association purpose, the proposed method shows improved performance compared to the state-of-art offline association approach with significantly low computational load.