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Works217 from public data
- 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.
- Towards Open World Object Detection654
This work proposes a novel computer vision problem called ORE: Open World Object Detector, which is based on contrastive clustering and energy based unknown identification, and finds that identifying and characterising unknown instances helps to reduce confusion in an incremental object detection setting.
- 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.
- A Guide to Convolutional Neural Networks for Computer Vision623
This self-contained guide will benefit those who seek to both understand the theory behind CNNs and to gain hands-on experience on the application of CNNs in computer vision, providing a comprehensive introduction to CNNs.
- 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.
- UNETR++: Delving Into Efficient and Accurate 3D Medical Image Segmentation496
This paper proposes a 3D medical image segmentation approach that offers both high-quality segmentation masks as well as efficiency in terms of parameters, compute cost, and inference speed and introduces a novel efficient paired attention block that efficiently learns spatial and channel-wise discriminative features using a pair of inter-dependent branches based on spatial and channel attention.
- 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.
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- 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.
- OW-DETR: Open-world Detection Transformer294
This work introduces a novel end-to-end transformer-based framework, OW-DETR, for open-world object detection that explicitly encodes multi-scale contextual information, possesses less inductive bias, enables knowledge transfer from known classes to the unknown class and can better discriminate between unknown objects and background.
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- Diverse Data Augmentation with Diffusions for Effective Test-time Prompt Tuning217
This paper proposes a novel TPT method, named DiffTPT, which leverages pre-trained diffusion models to generate diverse and informative new data, and incorporates augmented data by both conventional method and pre-trained stable diffusion to exploit their respective merits, improving the model’s ability to adapt to unknown new test data.
- PromptIR: Prompting for All-in-One Blind Image Restoration212
This work presents a prompt-based learning approach, PromptIR, for All-In-One image restoration that can effectively restore images from various types and levels of degradation, and uses prompts to encode degradation-specific information, which is then used to dynamically guide the restoration network.
- 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.
- Zero-Shot Object Detection: Learning to Simultaneously Recognize and Localize Novel Concepts169
This work introduces a new Zero-Shot Detection problem setting, which aims at simultaneously recognizing and locating object instances belonging to novel categories without any training examples, and designs an original loss function that achieves synergy between max-margin class separation and semantic space clustering.
- 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.
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- 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.
- Spatio-temporal Relation Modeling for Few-shot Action Recognition159
A novel few-shot action recognition framework, STRM, which enhances class-specific feature discrim-inability while simultaneously learning higher-order temporal representations and introduces a query-class similarity classifier on the patch-level enriched features to enhance class- specific feature dis-criminability.
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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