In this paper, we tackle the problem of crowd counting in indoor videos, where people often stay almost static for a long time. The label distribution, which covers a certain number of crowd counting labels, representing the degree to which each label describes the video frame, is previously adopted to model the label ambiguity of the crowd number. However, since the label ambiguity is significantly affected by the crowd number of the scene, we initialize the label distribution of each frame by the discretized Gaussian distribution with adaptive variance instead of the original single static Gaussian distribution. Moreover, considering the gradual change of crowd numbers in the adjacent frames, a mixture of Gaussian models is proposed to generate the final label distribution representation for each frame. The weights of the Gaussian models rely on the frame and feature distances between the current frame and the adjacent frames. The mixed l _2,1 -norm is adopted to restrict the weights of predicting the adjacent crowd numbers to be locally correlated. We collect three new indoor video datasets with frame number annotation for further research. The proposed approach achieves state-of-the-art performance on seven challenging indoor videos and cross-scene experiments.

No-Reference Image Quality Assessment aims to evaluate the perceptual quality of an image, according to human perception. Many recent studies use Transformers to assign different self-attention mechanisms to distinguish regions of an image, simulating the perception of the human visual system (HVS). However, the quadratic computational complexity caused by the self-attention mechanism is time-consuming and expensive. Meanwhile, the image resizing in the feature extraction stage loses the full-size image quality. To address these issues, we propose a lightweight attention mechanism using decomposed large-kernel convolutions to extract multiscale features, and a novel feature enhancement module to simulate HVS. We also propose to compensate the information loss caused by image resizing, with supplementary features from natural scene statistics. Experimental results on five standard datasets show that the proposed method surpasses the SOTA, while significantly reducing the computational costs.

Label Distribution Learning (LDL) has attracted increasing research attentions due to its potential to address the label ambiguity problem in machine learning and success in many real-world applications. In LDL, it is usually expensive to obtain the ground-truth label distributions of data, but it is relatively easy to obtain the logical labels of data. How to use training instances only with logical labels to learn an effective LDL model is a challenging problem. In this paper, we propose a two-step framework to address this problem. Specifically, we firstly design an efficient recovery model to recover the latent label distributions of training instances, named Fast Label Enhancement (FLE). Our idea is to use non-negative matrix factorization (NMF) to mine the label distribution information from the feature space. Moreover, we take the instance-class similarities into consideration to discover the importance of each label to training instances, which is useful for learning precise label distributions. Then, we train a predictive model for testing instances based on generated label distributions of training instances and an existing LDL method (e.g., SA-BFGS). Experimental results on fifteen benchmark datasets show the effectiveness of the proposed two-step framework and verify the superiority of FLE over several state-of-the-art approaches.

Label Distribution Learning (LDL) has been successfully implemented in numerous practical applications. However, the imbalance in label distributions presents a significant challenge due to the substantial variation in annotation information. To tackle this issue, we introduce Decoupled Imbalance Label Distribution Learning (DILDL), which decomposes the imbalanced label distribution into a dominant label distribution and a non-dominant label distribution. Our empirical findings reveal that an excessively high description degree of dominant labels can result in substantial gradient information attenuation for non-dominant labels during the learning process. Therefore, we employ the decoupling approach to balance the description degrees of both dominant and non-dominant labels independently. Furthermore, we align the feature representations with the representations of dominant and non-dominant labels separately, aiming to effectively mitigate the distribution shift problem. Experimental results demonstrate that our proposed DILDL outperforms other state-of-the-art methods for imbalance label distribution learning.