LMC: Large Model Collaboration with Cross-assessment for Training-Free Open-Set Object Recognition

Open-set object recognition aims to identify if an object is from a class that has been encountered during training or not. To perform open-set object recognition accurately, a key challenge is how to reduce the reliance on spurious-discriminative features. In this paper, motivated by that different large models pre-trained through different paradigms can possess very rich while distinct implicit knowledge, we propose a novel framework named Large Model Collaboration (LMC) to tackle the above challenge via collaborating different off-the-shelf large models in a training-free manner. Moreover, we also incorporate the proposed framework with several novel designs to effectively extract implicit knowledge from large models. Extensive experiments demonstrate the efficacy of our proposed framework. Code is available \href{https://github.com/Harryqu123/LMC}{here}.Comment: NeurIPS 202

A physics-informed and attention-based graph learning approach for regional electric vehicle charging demand prediction

Along with the proliferation of electric vehicles (EVs), optimizing the use of EV charging space can significantly alleviate the growing load on intelligent transportation systems. As the foundation to achieve such an optimization, a spatiotemporal method for EV charging demand prediction in urban areas is required. Although several solutions have been proposed by using data-driven deep learning methods, it can be found that these performance-oriented methods may suffer from misinterpretations to correctly handle the reverse relationship between charging demands and prices. To tackle the emerging challenges of training an accurate and interpretable prediction model, this paper proposes a novel approach that enables the integration of graph and temporal attention mechanisms for feature extraction and the usage of physic-informed meta-learning in the model pre-training step for knowledge transfer. Evaluation results on a dataset of 18,013 EV charging piles in Shenzhen, China, show that the proposed approach, named PAG, can achieve state-of-the-art forecasting performance and the ability in understanding the adaptive changes in charging demands caused by price fluctuations.Comment: Preprint. This work has been submitted to the IEEE Transactions on ITS for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Recent Advances of Continual Learning in Computer Vision: An Overview

In contrast to batch learning where all training data is available at once, continual learning represents a family of methods that accumulate knowledge and learn continuously with data available in sequential order. Similar to the human learning process with the ability of learning, fusing, and accumulating new knowledge coming at different time steps, continual learning is considered to have high practical significance. Hence, continual learning has been studied in various artificial intelligence tasks. In this paper, we present a comprehensive review of the recent progress of continual learning in computer vision. In particular, the works are grouped by their representative techniques, including regularization, knowledge distillation, memory, generative replay, parameter isolation, and a combination of the above techniques. For each category of these techniques, both its characteristics and applications in computer vision are presented. At the end of this overview, several subareas, where continuous knowledge accumulation is potentially helpful while continual learning has not been well studied, are discussed

Heatmap Distribution Matching for Human Pose Estimation

For tackling the task of 2D human pose estimation, the great majority of the recent methods regard this task as a heatmap estimation problem, and optimize the heatmap prediction using the Gaussian-smoothed heatmap as the optimization objective and using the pixel-wise loss (e.g. MSE) as the loss function. In this paper, we show that optimizing the heatmap prediction in such a way, the model performance of body joint localization, which is the intrinsic objective of this task, may not be consistently improved during the optimization process of the heatmap prediction. To address this problem, from a novel perspective, we propose to formulate the optimization of the heatmap prediction as a distribution matching problem between the predicted heatmap and the dot annotation of the body joint directly. By doing so, our proposed method does not need to construct the Gaussian-smoothed heatmap and can achieve a more consistent model performance improvement during the optimization of the heatmap prediction. We show the effectiveness of our proposed method through extensive experiments on the COCO dataset and the MPII dataset.Comment: NeurIPS 202

Research on the identification method of key parts of ship target based on contour matching

A template matching method based on the contour fitting heading angle is proposed for the problem of identifying key parts of maritime ships. First, unmanned boats are used as hypothetical enemy targets to extract outer contours, build a matching template library, and establish relevant kinematic models. Based on the requirements of timeliness and relativism, the judgment conditions for fitting angles and heading angles are given. A traversal fitting framework is established based on the structural similarity index algorithm, and the target matching template is matched based on precise matching results. A motion space that combines position correlation, electro-optical detection distance, and target pitch decoupling is designed to obtain real heading angles. Finally, based on the relative position information of the template’s key parts, the key parts of the target are matched, and the normalized output matching image is obtained. The experiment shows that this method can achieve recognition instructions in real seaways where key parts of the target cannot be extracted due to the large amount of water mist, and it has advantages in timeliness, accuracy, and applicability compared with other algorithms. This method has strong robustness and provides a reference for the identification of key parts of various types of ship targets

Towards More Reliable Confidence Estimation

As a task that aims to assess the trustworthiness of the model's prediction output during deployment, confidence estimation has received much research attention recently, due to its importance for the safe deployment of deep models. Previous works have outlined two important characteristics that a reliable confidence estimation model should possess, i.e., the ability to perform well under label imbalance and the ability to handle various out-of-distribution data inputs. In this work, we propose a meta-learning framework that can simultaneously improve upon both characteristics in a confidence estimation model. Specifically, we first construct virtual training and testing sets with some intentionally designed distribution differences between them. Our framework then uses the constructed sets to train the confidence estimation model through a virtual training and testing scheme leading it to learn knowledge that generalizes to diverse distributions. Besides, we also incorporate our framework with a modified meta optimization rule, which converges the confidence estimator to flat meta minima. We show the effectiveness of our framework through extensive experiments on various tasks including monocular depth estimation, image classification, and semantic segmentation