Micro-Action Recognition (MAR) has gained increasing attention due to its crucial role as a form of non-verbal communication in social interactions, with promising potential for applications in human communication and emotion analysis. However, current approaches often overlook the inherent ambiguity in micro-actions, which arises from the wide category range and subtle visual differences between categories. This oversight hampers the accuracy of micro-action recognition. In this paper, we propose a novel Prototypical Calibrating Ambiguous Network (PCAN) to unleash and mitigate the ambiguity of MAR. Firstly, we employ a hierarchical action-tree to identify the ambiguous sample, categorizing them into distinct sets of ambiguous samples of false negatives and false positives, considering both body- and action-level categories. Secondly, we implement an ambiguous contrastive refinement module to calibrate these ambiguous samples by regulating the distance between ambiguous samples and their corresponding prototypes. This calibration process aims to pull false negative (FN) samples closer to their respective prototypes and push false positive (FP) samples apart from their affiliated prototypes. In addition, we propose a new prototypical diversity amplification loss to strengthen the model's capacity by amplifying the differences between different prototypes. Finally, we propose a prototype-guided rectification to rectify prediction by incorporating the representability of prototypes. Extensive experiments conducted on the benchmark dataset demonstrate the superior performance of our method compared to existing approaches.

Quantization is one of the efficient model compression methods, which represents the network with fixed-point or low-bit numbers. Existing quantization methods address the network quantization by treating it as a single-objective optimization that pursues high accuracy (performance optimization) while keeping the quantization constraint. However, owing to the non-differentiability of the quantization operation, it is challenging to integrate the quantization operation into the network training and achieve optimal parameters. In this paper, a novel multi-objective convex quantization for efficient model compression is proposed. Specifically, the network training is modeled as a multi-objective optimization to find the network with both high precision and low quantization error (actually, these two goals are somewhat contradictory and affect each other). To achieve effective multi-objective optimization, this paper designs a quantization error function that is differentiable and ensures the computation convexity in each period, so as to avoid the non-differentiable back-propagation of the quantization operation. Then, we perform a time-series self-distillation training scheme on the multi-objective optimization framework, which distills its past softened labels and combines the hard targets to guarantee controllable and stable performance convergence during training. At last and more importantly, a new dynamic Lagrangian coefficient adaption is designed to adjust the gradient magnitude of quantization loss and performance loss and balance the two losses during training processing. The proposed method is evaluated on well-known benchmarks: MNIST, CIFAR-10/100, ImageNet, Penn Treebank and Microsoft COCO, and experimental results show that the proposed method achieves outstanding performance compared to existing methods.

Quantization for model compression can efficiently re-duce the network complexity and storage requirement, but the original training data is necessary to remedy the performance loss caused by quantization. The Data-Free Quan-tization (DFQ) methods have been proposed to handle the absence of original training data with synthetic data. How-ever, there are differences between the synthetic and orig-inal training data, which affects the performance of the quantized network, but none of the existing methods con-siders the differences. In this paper, we propose an efficient data-free quantization via pseudo-label filtering, which is the first to evaluate the synthetic data before quantization. We design a new metric for evaluating synthetic data using self-entropy, which indicates the reliability of synthetic data. The synthetic data can be categorized with the met-ric into high- and low-reliable datasets for the following training process. Besides, the multiple pseudo-labels are designed to label the synthetic data with different reliabil-ity, which can provide valuable supervision information and avoid misleading training by low-reliable samples. Exten-sive experiments are implemented on several datasets, in-cluding CIFAR-10, CIFAR-100, and ImageNet with various models. The experimental results show that our method can perform excellently and outperform existing methods in ac-curacy.