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- Medical Transformer: Gated Axial-Attention for Medical Image Segmentation1,495
A Gated Axial-Attention model is proposed which extends the existing architectures by introducing an additional control mechanism in the self-attention module and achieves better performance than the convolutional and other related transformer-based architectures.
- HyperFace: A Deep Multi-Task Learning Framework for Face Detection, Landmark Localization, Pose Estimation, and Gender Recognition1,314
The proposed method, HyperFace, fuses the intermediate layers of a deep CNN using a separate CNN followed by a multi-task learning algorithm that operates on the fused features to exploit the synergy among the tasks which boosts up their individual performances.
- Densely Connected Pyramid Dehazing Network1,223
A new end-to-end single image dehazing method, called Densely Connected Pyramid Dehazing Network (DCPDN), which can jointly learn the transmission map, atmospheric light and dehazed all together by directly embedding the atmospheric scattering model into the network.
- Image De-Raining Using a Conditional Generative Adversarial Network1,218
This work attempts to leverage powerful generative modeling capabilities of the recently introduced conditional generative adversarial networks (CGAN) by enforcing an additional constraint that the de-rained image must be indistinguishable from its corresponding ground truth clean image.
- A Transformer-Based Siamese Network for Change Detection1,174
Experiments show that the proposed end-to-end trainable ChangeFormer architecture achieves better CD performance than previous counterparts.
- Density-Aware Single Image De-raining Using a Multi-stream Dense Network1,080
A novel density-aware multi-stream densely connected convolutional neural network-based algorithm, called DID-MDN, for joint rain density estimation and de-raining, which achieves significant improvements over the recent state-of-the-art methods.
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- Generating High-Quality Crowd Density Maps Using Contextual Pyramid CNNs700
A novel method called Contextual Pyramid CNN (CP-CNN) for generating high-quality crowd density and count estimation by explicitly incorporating global and local contextual information of crowd images is presented.
- A survey of recent advances in CNN-based single image crowd counting and density estimation634
A comprehensive survey of recent Convolutional Neural Network (CNN) based approaches that have demonstrated significant improvements over earlier methods that rely largely on hand-crafted representations is provided.
- CNN-Based cascaded multi-task learning of high-level prior and density estimation for crowd counting543
A novel end-to-end cascaded network of CNNs to jointly learn crowd count classification and density map estimation achieves lower count error and better quality density maps as compared to the recent state-of-the-art methods.
- Deep Multitask Learning for Railway Track Inspection441
It is shown that detection performance can be improved by combining multiple detectors within a multitask learning framework, and this approach results in improved accuracy for detecting defects on railway ties and fasteners.
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- C2AE: Class Conditioned Auto-Encoder for Open-Set Recognition428
An open-set recognition algorithm using class conditioned auto-encoders with novel training and testing methodologies is proposed and experiments show that the proposed method performs significantly better than the state of the art methods.
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- Unsupervised Domain Adaptation of Object Detectors: A Survey351
This work describes in detail the domain adaptation problem for detection and presents an extensive survey of the various methods, and identifies multiple aspects of the problem that are most promising for future research.
- Uncertainty Guided Multi-Scale Residual Learning-Using a Cycle Spinning CNN for Single Image De-Raining349
The proposed Uncertainty guided Multi-scale Residual Learning (UMRL) network attempts to address the single image de-raining problem by learning the rain content at different scales and using them to estimate the final de-rained output.
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- Unconstrained face verification using deep CNN features303
An algorithm for unconstrained face verification based on deep convolutional features and evaluate it on the newly released IARPA Janus Benchmark A (IJB-A) dataset as well as on the traditional Labeled Face in the Wild (LFW) dataset.
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- KiU-Net: Overcomplete Convolutional Architectures for Biomedical Image and Volumetric Segmentation255
A new architecture for im- age segmentation- KiU-Net is designed which has two branches: an overcomplete convolutional network Kite-Net which learns to capture fine details and accurate edges of the input, and (2) U- net which learns high level features.
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- MeGA-CDA: Memory Guided Attention for Category-Aware Unsupervised Domain Adaptive Object Detection242
This work attempts to incorporate category information into the domain adaptation process by proposing Memory Guided Attention for Category-Aware Domain Adaptation (MeGA-CDA), which consists of employing category-wise discriminators to ensure category-aware feature alignment for learning domain-invariant discriminative features.
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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