Multi-Modality Multi-Attribute Contrastive Pre-Training for Image Aesthetics Computing.

In the Image Aesthetics Computing (IAC) field, most prior methods leveraged the off-the-shelf backbones pre-trained on the large-scale ImageNet database. While these pre-trained backbones have achieved notable success, they often overemphasize object-level semantics and fail to capture the high-level concepts of image aesthetics, which may only achieve suboptimal performances. To tackle this long-neglected problem, we propose a multi-modality multi-attribute contrastive pre-training framework, targeting at constructing an alternative to ImageNet-based pre-training for IAC.

Blind Video Quality Prediction by Uncovering Human Video Perceptual Representation.

Blind video quality assessment (VQA) has become an increasingly demanding problem in automatically assessing the quality of ever-growing in-the-wild videos. Although efforts have been made to measure temporal distortions, the core to distinguish between VQA and image quality assessment (IQA), the lack of modeling of how the human visual system (HVS) relates to the temporal quality of videos hinders the precise mapping of predicted temporal scores to the human perception. Inspired by the recent discovery of the temporal straightness law of natural videos in the HVS, this paper intends to model the complex temporal distortions of in-the-wild videos in a simple and uniform representation by describing the geometric properties of videos in the visual perceptual domain.

Q-BENCH: A Benchmark for Multi-modal Foundation Models on Low-level Vision from Single Images to Pairs.

The rapid development of Multi-modality Large Language Models (MLLMs) has navigated a paradigm shift in computer vision, moving towards versatile foundational models. However, evaluating MLLMs in low-level visual perception and understanding remains a yet-to-explore domain. To this end, we design benchmark settings to emulate human language responses related to low-level vision: the low-level visual perception (A1) via visual question answering related to low-level attributes (e.

TOPIQ: A Top-Down Approach From Semantics to Distortions for Image Quality Assessment.

Image Quality Assessment (IQA) is a fundamental task in computer vision that has witnessed remarkable progress with deep neural networks. Inspired by the characteristics of the human visual system, existing methods typically use a combination of global and local representations (i.e.

Towards Transparent Deep Image Aesthetics Assessment with Tag-based Content Descriptors.

Deep learning approaches for Image Aesthetics Assessment (IAA) have shown promising results in recent years, but the internal mechanisms of these models remain unclear. Previous studies have demonstrated that image aesthetics can be predicted using semantic features, such as pre-trained object classification features. However, these semantic features are learned implicitly, and therefore, previous works have not elucidated what the semantic features are representing.

Utilizing online stochastic optimization on scheduling of intensity-modulate radiotherapy therapy (IMRT).

According to Ministry of Health and Welfare of Taiwan, cancer has been one of the major causes of death in Taiwan since 1982. The Intensive-Modulated Radiation Therapy (IMRT) is one of the most important radiotherapies of cancers, especially for Nasopharyngeal cancers, Digestive system cancers and Cervical cancers. For patients, if they can receive the treatment at the earliest possibility while diagnosed with cancers, their survival rate increases.