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- CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing1,013
A framework called CRITIC is introduced that allows LLMs, which are essentially"black boxes" to validate and progressively amend their own outputs in a manner similar to human interaction with tools.
- MANIQA: Multi-dimension Attention Network for No-Reference Image Quality Assessment864
Multi-dimension Attention Network for no-reference Image Quality Assessment (MANIQA) is proposed to improve the performance on GAN-based distortion images and outperforms state-of-the-art methods on four standard datasets by a large margin.
- GAN Inversion: A Survey653
This paper provides a survey of GAN inversion with a focus on its representative algorithms and its applications in image restoration and image manipulation, and discusses the trends and challenges for future research.
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- ToRA: A Tool-Integrated Reasoning Agent for Mathematical Problem Solving331
This paper proposes ToRA a series of Tool-integrated Reasoning Agents designed to solve challenging mathematical problems by seamlessly integrating natural language reasoning with the utilization of external tools, thereby amalgamating the analytical prowess of language and the computational efficiency of tools.
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- Self-supervised Feature Learning for 3D Medical Images by Playing a Rubik’s Cube173
A novel proxy task, i.e., Rubik's cube recovery, is formulated to pre-train 3D neural networks and it is shown that the self-supervised learning approach can substantially boost the accuracy of 3D deep learning networks on the volumetric medical datasets without using extra data.
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- Attentions Help CNNs See Better: Attention-based Hybrid Image Quality Assessment Network137
An Attention-based Hybrid Image Quality Assessment Network (AHIQ) is proposed to deal with the challenge and get better performance on the GAN-based IQA task and outperforms the state-of-the-art methods on four standard IQA datasets.
- Exploring Human-Like Translation Strategy with Large Language Models118
This work proposes the MAPS framework, which stands for Multi-Aspect Prompting and Selection, and enables LLMs first to analyze the given source sentence and induce three aspects of translation-related knowledge to guide the final translation process.
- Face recognition by sparse discriminant analysis via joint L2,1-norm minimization114
Experiments on three standard face databases illustrate FLDA and SDA via L 2,1 -norm penalty term can significantly improve their recognition performance, and obtain inspiring results with low computation cost and for low-dimension feature.
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- Sparse Adversarial Attack via Perturbation Factorization101
An e-cient algorithm is developed by equivalently reformulating the MIP problem as a continuous optimization problem to jointly optimize the binary selection factors and continuous perturbation magnitudes of all pixels, with a cardinality constraint on selection factors to explicitly control the degree of sparsity.
- CoSeR: Bridging Image and Language for Cognitive Super-Resolution93
The Cognitive Super-Resolution (CoSeR) framework is introduced, empowering SR models with the capacity to comprehend low-resolution images and a novel condition injection scheme called “Ali-in-Attention ”, consolidating all conditional information into a single module.
- MAP: Multimodal Uncertainty-Aware Vision-Language Pre-training Model86
This paper project the representations of all modalities as probabilistic distributions via a Probability Distribution Encoder (PDE) by utilizing sequence-level interactions and proposes suitable pretraining tasks that convey richer multimodal semantic information and more complex relationships.
- Interactive Story Visualization with Multiple Characters84
A system for generic interactive story visualization, capable of handling multiple novel characters and supporting the editing of layout and local structure is proposed, developed by leveraging the prior knowledge of large language and T2I models, trained on massive corpora.
- NTIRE 2022 Challenge on Perceptual Image Quality Assessment84
This paper reports on the NTIRE 2022 challenge on perceptual image quality assessment (IQA), held in conjunction with the New Trends in Image Restoration and Enhancement workshop (NTIRE) workshop at CVPR 2022 to address the emerging challenge of IQA by perceptual image processing algorithms.
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- Probabilistic Modeling of Semantic Ambiguity for Scene Graph Generation77
This work proposes a novel plug-and-play Probabilistic Uncertainty Modeling (PUM) module, which models each union region as a Gaussian distribution, whose variance measures the uncertainty of the corresponding visual content.
- IDOL: Instant Photorealistic 3D Human Creation from a Single Image71
A scalable feed-forward transformer model is developed to predict a 3D human Gaussian representation in a uniform space from a given human image, which demonstrates the ability to efficiently reconstruct photorealistic humans at 1K resolution from a single input image using a single GPU instantly.
- Convolutional neural network for cell classification using microscope images of intracellular actin networks69
The difference in the actin cytoskeleton structures between breast normal and cancer cells is revealed, which may provide new information regarding malignant changes and be used as additional diagnostic marker.
- VDTR: Video Deblurring With Transformer68
VDTR is an effective Transformer-based model that makes the first attempt to adapt pure Transformer for video deblurring, and achieves highly competitive results on both synthetic and real-world videodeblurring benchmarks, including DVD, GOPRO, REDS and BSD.
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- Generalizable Black-Box Adversarial Attack With Meta Learning66
A meta-learning framework is developed by training a meta generator to produce perturbations conditioned on benign examples by utilizing model-level adversarial transferability to train the meta generator on a white-box surrogate model, then transfer it to help the attack against the target model.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; 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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