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Claim this profileWorks153 from public data
- 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.
- 1,137
- 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).
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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