Opinion-unaware no-reference image quality assessment (NR-IQA) methods have received many interests recently because they do not require images with subjective scores for training. Unfortunately, it is a challenging task, and thus far no opinion-unaware methods have shown consistently better performance than the opinion-aware ones. In this paper, we propose an effective opinion-unaware NR-IQA method based on reinforcement recursive list-wise ranking. We formulate the NR-IQA as a recursive list-wise ranking problem which aims to optimize the whole quality ordering directly. During training, the recursive ranking process can be modeled as a Markov decision process (MDP). The ranking list of images can be constructed by taking a sequence of actions, and each of them refers to selecting an image for a specific position of the ranking list. Reinforcement learning is adopted to train the model parameters, in which no ground-truth quality scores or ranking lists are necessary for learning. Experimental results demonstrate the superior performance of our approach compared with existing opinion-unaware NR-IQA methods. Furthermore, our approach can compete with the most effective opinion-aware methods. It improves the state-of-the-art by over 2% on the CSIQ benchmark and outperforms most compared opinion-aware models on TID2013.

Transferring image-based object detectors to the domain of videos remains a challenging problem. Previous efforts mostly exploit optical flow to propagate features across frames, aiming to achieve a good trade-off between accuracy and efficiency. However, introducing an extra model to estimate optical flow can significantly increase the overall model size. The gap between optical flow and high-level features can also hinder it from establishing spatial correspondence accurately. Instead of relying on optical flow, this paper proposes a novel module called Progressive Sparse Local Attention (PSLA), which establishes the spatial correspondence between features across frames in a local region with progressively sparser stride and uses the correspondence to propagate features. Based on PSLA, Recursive Feature Updating (RFU) and Dense Feature Transforming (DenseFT) are proposed to model temporal appearance and enrich feature representation respectively in a novel video object detection framework. Experiments on ImageNet VID show that our method achieves the best accuracy compared to existing methods with smaller model size and acceptable runtime speed.

Convolutional neural networks (CNNs) are inherently subject to invariable filters that can only aggregate local inputs with the same topological structures. It causes that CNNs are allowed to manage data with Euclidean or grid-like structures (e.g., images), not ones with non-Euclidean or graph structures (e.g., traffic networks). To broaden the reach of CNNs, we develop structure-aware convolution to eliminate the invariance, yielding a unified mechanism of dealing with both Euclidean and non-Euclidean structured data. Technically, filters in the structure-aware convolution are generalized to univariate functions, which are capable of aggregating local inputs with diverse topological structures. Since infinite parameters are required to determine a univariate function, we parameterize these filters with numbered learnable parameters in the context of the function approximation theory. By replacing the classical convolution in CNNs with the structure-aware convolution, Structure-Aware Convolutional Neural Networks (SACNNs) are readily established. Extensive experiments on eleven datasets strongly evidence that SACNNs outperform current models on various machine learning tasks, including image classification and clustering, text categorization, skeleton-based action recognition, molecular activity detection, and taxi flow prediction.

This paper presents an effective method based on vector regression and object oriented pooling for blind image quality assessment. Unlike previous models that map the extracted features directly to a quality score, the proposed vector regression framework yields a vector of belief scores for the input image. We explore the uncertainty factors in quality assessment and design the belief scores to measure the confidences of an image to be assigned to the corresponding quality grades. Moreover, we propose an object oriented pooling strategy to further improve the performance by incorporating semantic information of image contents. According to this strategy, regions occupied by objects will be assigned more weights in the pooling phase, leading to a more accurate quality assessment. Extensive experiments on benchmark datasets demonstrate that our approach achieves state-of-the-art performance and shows a great generalization ability.

The goal of no-reference image quality assessment (NR-IQA) is to estimate human perceived image quality without access to either reference image or prior knowledge about distortion type. Previous approaches for this problem are typically based on a regression framework that maps the image features directly to a quality score. In contrast, psychological evidence shows that humans prefer to evaluate visual quality with qualitative descriptions, e.g., using a five-grade ordinal scale: “excellent”, “good”, “fair”, “poor” and “bad”. Based on this observation, we propose a vector regression model that predicts five belief scores rather than a single quality score. The belief scores are designed to indicate the confidences of the test image being assigned with these five quality grades. In addition, with the purpose of more extensive applications, a saliency-based pooling strategy is presented to convert the predicted confidences into objective quality scores. Extensive experiments performed on two benchmark datasets demonstrate that our approach achieves state-of-the-art performance and shows great generalization ability.