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- InternImage: Exploring Large-Scale Vision Foundation Models with Deformable Convolutions1,217
This work presents a new large-scale CNN-based foundation model, termed InternImage, which can obtain the gain from increasing parameters and training data like ViTs, and reduces the strict inductive bias of traditional CNNs and makes it possible to learn stronger and more robust patterns with large- scale parameters from massive datalike ViTs.
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- GIT: A Generative Image-to-text Transformer for Vision and Language808
This paper design and train a Generative Image-to-text Transformer, GIT, to unify vision-language tasks such as image/video captioning and question answering and establishes new state of the arts on 12 challenging benchmarks with a large margin.
- An Empirical Study of GPT-3 for Few-Shot Knowledge-Based VQA559
This work proposes PICa, a simple yet effective method that Prompts GPT3 via the use of Image Captions, for knowledge-based VQA, and treats GPT-3 as an implicit and unstructured KB that can jointly acquire and process relevant knowledge.
- R³Net: Recurrent Residual Refinement Network for Saliency Detection522
A novel recurrent residual refinement network (R^3Net) equipped with residual refinement blocks (RRBs) to more accurately detect salient regions of an input image that outperforms competitors in all the benchmark datasets.
- CANet: Cross-Disease Attention Network for Joint Diabetic Retinopathy and Diabetic Macular Edema Grading449
A novel cross-disease attention network (CANet) is presented to jointly grade DR and DME by exploring the internal relationship between the diseases with only image-level supervision and achieves the best result on the ISBI 2018 IDRiD challenge dataset and outperforms other methods on the Messidor dataset.
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- Scaling Up Vision-Language Pretraining for Image Captioning342
LEMON O, a LargE-scale iMage captiONer, is presented, and the first empirical study on the scaling behavior of VLP for image captioning is provided, and it is shown LEMON can generate captions with long-tail vi-sual concepts when used in a zero-shot manner.
- SINet: A Scale-Insensitive Convolutional Neural Network for Fast Vehicle Detection341
A scale-insensitive convolutional neural network (SINet) for fast detecting vehicles with a large variance of scales is presented and a context-aware RoI pooling to maintain the contextual information and original structure of small scale objects is presented.
- Bidirectional Feature Pyramid Network with Recurrent Attention Residual Modules for Shadow Detection285
This paper presents a network to detect shadows by exploring and combining global context in deep layers and local context in shallow layers of a deep convolutional neural network (CNN) and develops a bidirectional feature pyramid network (BFPN) to aggregate shadow contexts spanned across different CNN layers.
- Selective Catalysis of the Aerobic Oxidation of Cyclohexane in the Liquid Phase by Carbon Nanotubes265
Catalytic performances of carbon materials, includingactivated carbon (AC), multiwalled CNTs, and typical metal catalysts are summarized in Table 1.
- Direction-Aware Spatial Context Features for Shadow Detection and Removal261
This paper designs a weighted cross entropy loss to make effective the training for shadow detection and further adopt the network for shadow removal by using a euclidean loss function and formulating a color transfer function to address the color and luminosity inconsistencies in the training pairs.
- Direction-Aware Spatial Context Features for Shadow Detection248
A novel network for shadow detection is presented by analyzing image context in a direction-aware manner by introducing attention weights when aggregating spatial context features in a spatial recurrent neural network (RNN).
- Global guidance network for breast lesion segmentation in ultrasound images244
This paper develops a deep convolutional neural network equipped with a global guidance block and breast lesion boundary detection modules for boosting the breast ultrasound lesion segmentation and shows the application of the network on the ultrasound prostate segmentation, in which it better identifies prostate regions than state-of-the-art networks.
- Learning Semantic Context from Normal Samples for Unsupervised Anomaly Detection241
This work presents a Semantic Context based Anomaly Detection Network, SCADN, for unsupervised anomaly detection by learning the semantic context from the normal samples by generating multi-scale striped masks to remove a part of regions from thenormal samples and training a generative adversarial network to reconstruct the unseen regions.
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- Deep Texture-Aware Features for Camouflaged Object Detection165
Experimental results show that the approach outperforms various state-of-the-art methods by a large margin, and the network is evaluated on the benchmark datasets for camouflaged object detection both qualitatively and quantitatively.
- Recurrently Aggregating Deep Features for Salient Object Detection164
This paper develops a novel deep saliency network equipped with recurrently aggregated deep features (RADF) to more accurately detect salient objects from an image by fully exploiting the complementary saliency information captured in different layers.
- Enhancing Pseudo Label Quality for Semi-supervised Domain-Generalized Medical Image Segmentation162
A novel confidence-aware cross pseudo supervision algorithm for semi-supervised domain generalized medical image segmentation to enhance the pseudo label quality for unlabeled images from unknown distributions.
- UniTAB: Unifying Text and Box Outputs for Grounded Vision-Language Modeling154
UniTAB’s unified multi-task network and the task-agnostic output sequence design make the model parameter efficient and generalizable to new tasks, and could provide a more comprehensive and interpretable image description.
- CATARACTS: Challenge on automatic tool annotation for cataRACT surgery149
Evaluating tool annotation algorithms based on deep learning for cataract surgery finds that the quality of their annotations are compared to that of human interpretations, and it is expected that they will guide the design of efficient surgery monitoring tools in the near future.
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- Injecting Semantic Concepts into End-to-End Image Captioning134
This paper proposes a pure vision transformer-based image captioning model, dubbed as ViTCAP, in which grid representations are used without extracting the regional features, and introduces a novel Concept Token Network (CTN) to predict the semantic concepts and then incorporate them into the end-to-end captioning.
- Compressing Visual-linguistic Model via Knowledge Distillation127
This paper uses the mean square error loss to mimic the attention distribution inside the transformer block, and presents a token-wise noise contrastive loss to align the hidden state by contrasting with negative representations stored in a sample queue.
- Deep Attentional Features for Prostate Segmentation in Ultrasound123
A novel deep neural network equipped with deep attentional feature (DAF) modules for better prostate segmentation in TRUS by fully exploiting the complementary information encoded in different layers of the convolutional neural network (CNN).
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- Revisiting Shadow Detection: A New Benchmark Dataset for Complex World106
This work collected shadow images for multiple scenarios and compiled a new dataset of 10,500 shadow images, each with labeled ground-truth mask, for supporting shadow detection in the complex world.
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- Instance Shadow Detection90
LISA is designed, named after Light-guided Instance Shadow-object Association, an end-to-end framework to automatically predict the shadow and object instances, together with the shadow-object associations and light direction, and demonstrates its applicability on light direction estimation and photo editing.
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- SAC-Net: Spatial Attenuation Context for Salient Object Detection81
A new deep neural network design for salient object detection by maximizing the integration of local and global image context within, around, and beyond the salient objects by embedding the spatial attenuation context module to recurrently translate and aggregate the context features.
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- Bandit Convex Optimization with Biased Noisy Gradient Oracles71
A novel framework is proposed that replaces the specific gradient estimation methods with an abstract oracle, and formally shows that to achieve the optimal root-n rate either the algorithms that use existing gradient estimators, or the proof techniques used to analyze them have to go beyond what exists today.
- Integrated method of thermosensitive triblock copolymer–salt aqueous two phase extraction and dialysis membrane separation for purification of lycium barbarum polysaccharide71
An ideal extraction and recycle result were achieved in deproteinization for lycium barbarum polysaccharide (LBP) using a polymer-salt aqueous two-phase system consisting of thermosensitive copolymer EOPOEO and NaH2PO4.
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- Video Dehazing via a Multi-Range Temporal Alignment Network with Physical Prior58
A memory-based physical prior guidance module to encode the prior-related features into long-range memory and a multi-range scene radiance recovery module to capture space-time dependencies in multiple space- time ranges, which helps to effectively aggregate temporal information from adjacent frames.
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- Large depth-of-field 3D shape measurement using an electrically tunable lens50
This paper captures always in-focus phase-shifted fringe patterns by precisely synchronizing the tunable lens attached to the camera with the image acquisition and the pattern projection, and develops a phase unwrapping framework that fully utilizes the geometric constraint from the camera focal length setting.
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- Determinants to parking mode alternatives: A model integrating technology acceptance model and satisfaction–loyalty model37
Results show that the re-patronage of shared parking is significantly affected by its technology acceptance and that the perceived risk is the key variable leading to change parking mode.
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- A Survey of Scientific Large Language Models: From Data Foundations to Agent Frontiers31
A comprehensive, data-centric synthesis that reframes the development of Sci-LLMs as a co-evolution between models and their underlying data substrate, and outlines a paradigm shift toward closed-loop systems where autonomous agents based on Sci-LLMs actively experiment, validate, and contribute to a living, evolving knowledge base.
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- Demystify Transformers & Convolutions in Modern Image Deep Networks13
This paper aims to identify the real gains of popular convolution and attention operators through a detailed study, and finds that the key difference among these feature transformation modules, such as attention or convolution, lies in their spatial feature aggregation approach, known as the “spatial token mixer” (STM).
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- Study on Reliability of Accuracy of Numerical Method Based on Seismic Response of Transmission Tower–
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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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