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- A Comprehensive Overview of Large Language Models2,059
This review article is intended to provide a quick, comprehensive reference for the researchers and practitioners to draw insights from extensive, informative summaries of the existing works to advance the LLM research.
- Real Image Denoising With Feature Attention719
A novel single-stage blind real image denoising network (RIDNet) is proposed by employing a modular architecture that uses residual on the residual structure to ease the flow of low-frequency information and apply feature attention to exploit the channel dependencies.
- Simultaneously Localize, Segment and Rank the Camouflaged Objects599
It is argued that explicitly modeling the conspicuousness of camouflaged objects against their particular backgrounds can not only lead to a better understanding about camouflage and evolution of animals, but also provide guidance to design more sophisticated camouflage techniques.
- A Deep Journey into Super-resolution503
A taxonomy for deep learning–based super-resolution networks is introduced that groups existing methods into nine categories including linear, residual, multi-branch, recursive, progressive, attention-based, and adversarial designs.
- UC-Net: Uncertainty Inspired RGB-D Saliency Detection via Conditional Variational Autoencoders398
Inspired by the saliency data labeling process, a probabilistic RGB-D saliency detection network via conditional variational autoencoders to model human annotation uncertainty and generate multiple saliency maps for each input image by sampling in the latent space is proposed.
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- First-in-Human Trial of a Novel Suprachoroidal Retinal Prosthesis330
The first-in-human Phase 1 trial to investigate the use of retinal implants in the suprachoroidal space in three human subjects with end-stage retinitis pigmentosa holds promise for the field of vision restoration.
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- Densely Residual Laplacian Super-Resolution288
This work presents a compact and accurate super-resolution algorithm, namely, densely residual laplacian network (DRLN), which employs cascading residual on the residual structure to allow the flow of low-frequency information to focus on learning high and mid-level features.
- Semantic Segmentation for Real Point Cloud Scenes via Bilateral Augmentation and Adaptive Fusion275
This work comprehensively interpret the distinctness of the points from multiple resolutions and represent the feature map following an adaptive fusion method at point-level for accurate semantic segmentation for large-scale point cloud data collected in reality.
- Reducing the Sim-to-Real Gap for Event Cameras262
This work presents a new High Quality Frames (HQF) dataset, containing events and ground truth frames from a DAVIS240C that are well-exposed and minimally motion-blurred, and presents strategies for improving training data for event based CNNs that result in 20-40% boost in performance of existing state-of-the-art (SOTA) video reconstruction networks retrained with this method.
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- Continuous-Time Intensity Estimation Using Event Cameras254
A computationally efficient, asynchronous filter that continuously fuses image frames and events into a single high-temporal-resolution, high-dynamic-range image state is proposed that outperforms existing state-of-the-art methods.
- An update on retinal prostheses221
Over 40 individual research groups who have built devices, developed stimulation strategies, or investigated the basic physiology underpinning retinal prostheses are encompassed, along with future challenges that face the field.
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- Uncertainty Inspired RGB-D Saliency Detection168
Qualitative and quantitative results on six challengingRGB-D benchmark datasets show the first stochastic framework to employ uncertainty for RGB-D saliency detection by learning from the data labeling process superior performance in learning the distribution of saliency maps.
- RGB-D Saliency Detection via Cascaded Mutual Information Minimization131
This paper introduces a novel multistage cascaded learning framework via mutual information minimization to explicitly model the multi-modal information between RGB image and depth data.
- Improved Visual-Semantic Alignment for Zero-Shot Object Detection125
An end-to-end deep learning framework underpinned by a novel loss function that handles class-imbalance and seeks to properly align the visual and semantic cues for improved zero-shot learning is proposed.
- Dense-Resolution Network for Point Cloud Classification and Segmentation122
A novel network named Dense-Resolution Network (DRNet) is proposed for point cloud analysis designed to learn local point features from the point cloud in different resolutions and presents a novel grouping method for local neighborhood searching and an error-minimizing module for capturing local features.
- Toward Deeper Understanding of Camouflaged Object Detection115
This paper argues that the binary segmentation setting fails to fully understand the concept of camouflage, and presents the first triple-task learning framework to simultaneously localize, segment, and rank camouflaged objects, indicating the conspicuousness level of camouflage.
- CED: Color Event Camera Dataset111
This work presents and releases the first Color Event Camera Dataset (CED), containing 50 minutes of footage with both color frames and events, and presents an extension of the event camera simulator ESIM that enables simulation of color events.
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- Correlating driver gaze with the road scene for driver assistance systems107
This paper presents a complete system that reads speed signs in real-time, compares the driver's gaze, and provides immediate feedback if it appears the sign has been missed by the driver.
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 2026-10-15. 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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