In the Blind Image Quality Assessment (BIQA) field, accurately assessing the quality of authentically distorted images presents a substantial challenge due to the diverse distortion types in natural settings. Existing state-of-the-art IQA methods mix a sequence of distortions into entire images to establish global distortion priors, but are inadequate for authentic images with spatially varied distortions. To address this, we introduce a novel IQA framework that employs knowledge distillation tailored to perceive spatially heterogeneous distortions, enhancing quality-distortion awareness. Specifically, we introduce a novel Block-wise Degradation Modelling approach that applies distinct distortions to different spatial blocks of an image, thereby expanding local distortion priors. Following this, we present a Block-wise Aggregation and Filtering module that enables fine-grained attention to the quality information within different distortion areas of the image. Furthermore, to effectively capture the complex relationships between distortions across different regions while preserving overall quality perception, we introduce Contrastive Knowledge Distillation to enhance the model’s ability to discriminate between different types of distortions and Affinity Knowledge Distillation to model the correlation among distortions in different regions. Extensive experiments on standard BIQA datasets demonstrate the effectiveness and competitiveness of the proposed method.

N:M sparsity stands as a progressively important tool for DNN compression, achieving practical speedups by stipulating at most N non-zero components within M sequential weights. Unfortunately, most existing works identify the N:M sparse mask through dense backward propagation to update all weights, which incurs exorbitant training costs. In this paper, we introduce BAME, a method that maintains consistent sparsity throughout the N:M sparse training process. BAME perpetually keeps both sparse forward and backward propagation, while iteratively performing weight pruning-and-regrowing within designated weight blocks to tailor the N:M mask. These blocks are selected through a joint assessment based on accumulated mask oscillation frequency and expected loss reduction of mask adaptation, thereby ensuring stable and efficient identification of the optimal N:M mask. Our empirical results substantiate the effectiveness of BAME, illustrating it performs comparably to or better than previous works that fully maintaining dense backward propagation during training. For instance, BAME attains a 72.0% top-1 accuracy while training a 1:16 sparse ResNet-50 on ImageNet, eclipsing SR-STE by 0.5%, despite achieving 2.37 training FLOPs reduction. Code is released at https://github.com/BAME-xmu/BAME