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Works121 from public data
- Restormer: Efficient Transformer for High-Resolution Image Restoration4,635
This work proposes an efficient Transformer model by making several key designs in the building blocks (multi-head attention and feed-forward network) such that it can capture long-range pixel interactions, while still remaining applicable to large images.
- Transformers in Vision: A Survey3,900
This survey aims to provide a comprehensive overview of the Transformer models in the computer vision discipline with an introduction to fundamental concepts behind the success of Transformers, i.e., self-attention, large-scale pre-training, and bidirectional feature encoding.
- Multi-Stage Progressive Image Restoration2,345
This paper proposes a novel synergistic design that can optimally balance these competing goals in image restoration tasks, that progressively learns restoration functions for the degraded inputs, thereby breaking down the overall recovery process into more manageable steps.
- Transformers in medical imaging: A survey1,338
This survey surveys the use of Transformers in medical image segmentation, detection, classification, restoration, synthesis, registration, clinical report generation, and other tasks and develops taxonomy for each application.
- Learning Enriched Features for Real Image Restoration and Enhancement1,052
This paper presents a novel architecture with the collective goals of maintaining spatially-precise high-resolution representations through the entire network and receiving strong contextual information from the low- resolution representations, named as MIRNet.
- Cost-Sensitive Learning of Deep Feature Representations From Imbalanced Data990
This paper proposes a cost-sensitive (CoSen) deep neural network, which can automatically learn robust feature representations for both the majority and minority classes, and shows that the proposed approach significantly outperforms the baseline algorithms.
- Learning Enriched Features for Fast Image Restoration and Enhancement653
The approach learns an enriched set of features that combines contextual information from multiple scales, while simultaneously preserving the high-resolution spatial details, and achieves state-of-the-art results for a variety of image processing tasks.
- CycleISP: Real Image Restoration via Improved Data Synthesis460
This paper presents a framework that models camera imaging pipeline in forward and reverse directions that allows any number of realistic image pairs for denoising both in RAW and sRGB spaces and achieves the state-of-the-art performance on real camera benchmark datasets.
- A Self-supervised Approach for Adversarial Robustness385
This paper proposes a self-supervised adversarial training mechanism in the input space that provides significant robustness against the unseen adversarial attacks and can be deployed as a plug-and-play solution to protect a variety of vision systems, as it demonstrates for the case of classification, segmentation and detection.
- 294
- Deep learning based synthesis of MRI, CT and PET: Review and analysis285
This survey comprehensively reviews deep learning-based medical imaging translation from 2018 to 2023 on pseudo-CT, synthetic MR, and synthetic PET, and provides an overview of synthetic contrasts in medical imaging and the most frequently employed deep learning networks for medical image synthesis.
- A Robust Volumetric Transformer for Accurate 3D Tumor Segmentation241
A Transformer architecture for volumetric segmentation, a challenging task that requires keeping a complex balance in encoding local and global spatial cues, and preserving information along all axes of the volume, is proposed.
- Striking the Right Balance With Uncertainty199
This paper demonstrates that the Bayesian uncertainty estimates directly correlate with the rarity of classes and the difficulty level of individual samples, and presents a novel framework for uncertainty based class imbalance learning that efficiently utilizes sample and class uncertainty information to learn robust features and more generalizable classifiers.
- iTAML: An Incremental Task-Agnostic Meta-learning Approach176
A novel meta-learning approach that seeks to maintain an equilibrium between all the encountered tasks, ensured by a new meta-update rule which avoids catastrophic forgetting and is task-agnostic.
- Semantic-Aware Domain Generalized Segmentation174
This paper addresses domain generalized semantic segmentation, where a segmentation model is trained to be domain-invariant without using any target domain data, and proposes a framework including two novel modules: Semantic-Aware Normalization (SAN) and Semantic -Aware Whitening (SAW).
- 172
- Random Path Selection for Continual Learning168
This paper proposes a random path selection algorithm, called RPS-Net, that progressively chooses optimal paths for the new tasks while encouraging parameter sharing and reuse and proposes a simple controller to dynamically balance the model plasticity.
- Adversarial Defense by Restricting the Hidden Space of Deep Neural Networks161
This work proposes to class-wise disentangle the intermediate feature representations of deep networks, and forces the features for each class to lie inside a convex polytope that is maximally separated from the polytopes of other classes.
- AV-Deepfake1M: A Large-Scale LLM-Driven Audio-Visual Deepfake Dataset140
The paper provides a thorough description of the proposed data generation pipeline accompanied by a rigorous analysis of the quality of the generated data and indicates a significant drop in performance compared to previous datasets.
- Regularization of deep neural networks with spectral dropout131
This work introduces a new approach called 'Spectral Dropout' to improve the generalization ability of deep neural networks by eliminating weak and 'noisy' Fourier domain coefficients of the neural network activations, leading to remarkably better results than the current regularization methods.
- An efficient 3D face recognition approach using local geometrical signatures128
This paper presents a computationally efficient 3D face recognition system based on a novel facial signature called Angular Radial Signature (ARS) which is extracted from the semi-rigid region of the face which can handle expression variations.
- Survey: Image mixing and deleting for data augmentation127
An in-depth survey of image mixing and deleting techniques is furnish and essentials, such as each category's strengths and limitations, describing their working mechanism, basic formulations, and applications are presented.
- Self-supervised Knowledge Distillation for Few-shot Learning127
This paper proposes a simple approach to improve the representation capacity of deep neural networks for few-shot learning tasks, and shows that, even in the first stage, self-supervision can outperform current state-of-the-art methods.
- Random Path Selection for Incremental Learning116
This paper proposes a random path selection algorithm, called RPSnet, that progressively chooses optimal paths for the new tasks while encouraging parameter sharing and reuse and proposes a simple controller to dynamically balance the model plasticity.
- 116
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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