Existing Image Quality Assessment (IQA) models are limited to either full reference or no reference evaluation tasks, while humans can seamlessly switch between these assessment types. This motivates us to explore resolving these two tasks using a versatile model. In this work, we propose a novel framework that unifies full reference and no reference IQA. Our approach utilizes an encoder to extract multi-level features from images and introduces a Hierarchical Attention module to adaptively handle spatial distortions for both full reference and no reference inputs. Additionally, we develop a Semantic Distortion Aware module to analyze feature correlations between shallow and deep layers of the encoder, thereby accounting for the varying effects of different distortions on these layers. Our proposed framework achieves state-of-the-art performance for both full-reference and no-reference IQA tasks when trained separately. Furthermore, when the model is trained jointly on both types of tasks, it not only enhances performance in no-reference IQA but also maintains competitive results in full-reference IQA. This integrated approach facilitates a single training process that efficiently addresses both IQA tasks, representing a significant advancement in model versatility and performance.

Salient Object Detection (SOD) is dominated by Encoder-Decoder networks which involve multi-scale feature fusion and multi-resolution dense supervision. It is prevalent yet problematic to interpolate feature maps or pool ground truth (GT) to fit the size of decoder stages in SOD. Structural properties are unavoidably damaged since pixels are discarded or changed during scaling, resulting in restoration difficulties and poor predictions. Second, it is intuitive and suboptimal to posit the last layer of an encoder as global context, even though it has been widely accepted that high-level encoder features contain global information that contributes to the overall shape of a SOD. To this end, this paper aims to enhance the abovementioned techniques for richer details and a more complete shape. First, we developed a Global Context Branch (GCB) which is a patch-wise supervised SOD on top of the encoder for better global context modeling. Second, we developed a Context Refinement Module (CRM) to improve high/low-level feature fusion and enhance detail reconstruction. Lastly, we adopt Pixel Shuffle (PS) when scaling features and GT maps to preserve structural information. Experiments demonstrated that our proposed framework achieved state-of-the-art performance among all five benchmark datasets against six related existing evaluation metrics.

Contour information plays a vital role in salient object detection. However, excessive false positives remain in predictions from existing contour-based models due to insufficient contour-saliency fusion. In this work, we designed a network for better edge quality in salient object detection. We proposed a contour-saliency blending module to exchange information between contour and saliency. We adopted recursive CNN to increase contour-saliency fusion while keeping the total trainable parameters the same. Furthermore, we designed a stage-wise feature extraction module to help the model pick up the most helpful features from previous intermediate saliency predictions. Besides, we proposed two new loss functions, namely Dual Confinement Loss and Confidence Loss, for our model to generate better boundary predictions. Evaluation results on five common benchmark datasets reveal that our model achieves competitive state-of-the-art performance.