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- TOPIQ: A Top-Down Approach From Semantics to Distortions for Image Quality Assessment423
This work proposes a top-down approach that uses high-level semantics to guide the IQA network to focus on semantically important local distortion regions, named as TOPIQ, and designs a heuristic coarse-to-fine network that leverages multi-scale features and progressively propagates multi-level semantic information to low-level representations in a top-down manner.
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- Q-Instruct: Improving Low-Level Visual Abilities for Multi-Modality Foundation Models218
The first dataset consisting of human natural language feedback on low-level vision, and a GPT-participated transformation to convert these feedbacks into a rich set of 200K instruction-response pairs, termed Q-Instruct are collected.
- An engineered CRISPR-Cas12a variant and DNA-RNA hybrid guides enable robust and rapid COVID-19 testing216
It is shown that the use of an engineered AsCas12a enzyme enables detection of wildtype and mutated SARS-CoV-2 and allows us to perform the detection step with loop-mediated isothermal amplification (LAMP) at 60-65 °C.
- Neighbourhood Representative Sampling for Efficient End-to-End Video Quality Assessment99
This work proposes a unified scheme, spatial-temporal grid mini-cube sampling (St-GMS), and the resultant samples are named fragments, a network architecture tailored specifically for fragments that achieves up to 1612× efficiency than the existing state-of-the-art VQA benchmarks.
- DisCoVQA: Temporal Distortion-Content Transformers for Video Quality Assessment86
The proposed Temporal Distortion-Content Transformers for Video Quality Assessment (DisCoVQA) reaches state-of-the-art performance on several VQA benchmarks without any extra pre-training datasets and up to 10% better generalization ability than existing methods.
- Towards Explainable In-the-Wild Video Quality Assessment: A Database and a Language-Prompted Approach80
The MaxVQA is proposed, a language-prompted VQA approach that modifies vision-language foundation model CLIP to better capture important quality issues as observed in the authors' analyses.
- Online teaching quality evaluation model based on support vector machine and decision tree78
The model proposed in this research is effective and advantageous in evaluating teaching quality in universities and processing large-scale data sets and is compared with other shallow models.
- Distilling Knowledge From Object Classification to Aesthetics Assessment48
This work proposes to distill knowledge on semantic patterns for a vast variety of image contents from multiple pre-trained object classification (POC) models to an IAA model, expecting the combination of multiple POC models can provide sufficient knowledge on various image contents to easier learn to relate various distinct contents to a limited number of aesthetic labels.
- Object-level Attention for Aesthetic Rating Distribution Prediction33
To the best of the knowledge, this is the first work modeling object-level attention for IAA and experimental results confirm the superiority of the framework over previous relevant methods.
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- Diffusion-Based Facial Aesthetics Enhancement With 3D Structure Guidance8
The Nearest Neighbor Structure Guidance based on Diffusion (NNSG-Diffusion), a diffusion-based FAE method that beautifies a 2D facial image with 3D structure guidance, is proposed, which is superior to previous relevant methods in enhancing facial aesthetics while preserving facial identity.
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- Integrating Large Pre-trained Models into Multimodal Named Entity Recognition with Evidential Fusion6
This work models the distribution of each modality as a Normal-inverse Gamma distribution, and fuses them into a unified distribution with an evidential fusion mechanism, enabling hierarchical characterization of uncertainties and promotion of prediction accuracy and trustworthiness in MNER.
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- Attention-driven Unsupervised Image Retrieval for Beauty Products with Visual and Textual Clues4
This work proposes a search method utilizing both images and product descriptions and experimentally proved the effectiveness of the proposed method on the validation set of the Perfect-500K dataset.
- Viewport-Unaware Blind Omnidirectional Image Quality Assessment: A Unified and Generalized Approach3
This paper experimentally finds that BOIQA can be formulated as a blind (2D planar) image quality assessment (BIQA) problem, and presents a new BOIQA approach, which has three merits: viewport-unaware, unified, and generalized, which shows better generalizability against other competitors.
- Enhancing Incomplete Multi-modal Brain Tumor Segmentation with Intra-modal Asymmetry and Inter-modal Dependency2
A novel approach that enhances the BTS model from two perspectives is proposed, introducing a pre-training stage that generates a diverse pre-training dataset covering a wide range of different combinations of tumor shapes and brain anatomy and a post-training stage that enables the model to reconstruct missing modalities in the prediction results when only partial modalities are available.
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Publication data from OpenAlex; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-13. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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