Blind Image Quality Assessment (BIQA) models trained on one distortion distribution often degrade when exposed to new ones, making sequential adaptation without forgetting a fundamental challenge. While continual learning offers a natural solution, existing methods typically retrain the entire backbone per task, limiting scalability and parameter efficiency. We propose ContEditIQA, a parameter-efficient framework for continual BIQA that selectively edits a pre-trained Vision Transformer (ViT) rather than retraining it. Following a locate-then-edit strategy, a lightweight attention-guided hypernetwork identifies distortion-sensitive Feed-Forward Network (FFN) parameters for each incoming task and restricts updates to those regions, while attention layers remain frozen to preserve globally shared representations. This targeted editing enables robust sequential adaptation without model expansion or memory replay. Experiments across six BIQA benchmarks demonstrate superior knowledge retention and cross-dataset generalization while modifying fewer than 30% of backbone parameters, establishing selective model editing as an effective and scalable paradigm for continual BIQA.
Satish Maurya, Parimala Kancharla· International Conference on...· 0 citations
Diffusion models achieve strong performance in generative modeling but incur high memory and computational costs due to large model parameters and iterative denoising procedures, limiting deployment in resource-constrained environments. This paper presents a unified framework to improving inference latency and memory efficiency of diffusion models by jointly optimizing activation reuse and weight storage. We introduce a representation-aware caching strategy(ReCaT) which reduces redundant computation during inference by selectively reusing intermediate activations along the diffusion trajectory. ReCaT identifies effective caching points based on representational similarity measured using centered kernel alignment(CKA), enabling informed activation reuse without degrading generative quality. To complement activation reuse, we propose Adaptive Clusteringguided Quantization with Entropy Encoding (ACQ-E) for efficient weight storage. ACQ-E uses histogram-guided clustering to model weights, followed by adaptive mixed-precision quantization and entropy encoding to reduce storage and cost. The proposed framework integrates representation-aware caching and adaptive quantization into a unified optimization pipeline, leading to reduced effective bit-width, smaller model size, and lower computational cost while maintaining generative fidelity. Experiments on standard benchmarks, including LSUN Churches, LSUN Bedrooms, CelebA-HQ, MS-COCO, and PartiPrompt, demonstrate efficiency gains with minimal impact on image quality when compared to full-precision.
Deepanshi Sharma, Rudra Pratap, Shivam Jaiswal et al.· International Conference on...· 0 citations