Is this you? Claim this profile to correct it, add a bio and choose the work people see first.
Claim this profileAcademic lineage
Possible advisorsa guess from early papers, not confirmed
- María VanrellSuggested from co-authorship
Is this you? Claim this profile to confirm or dismiss it.
Works378 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.
- ECO: Efficient Convolution Operators for Tracking2,708
This work revisit the core DCF formulation and introduces a factorized convolution operator, which drastically reduces the number of parameters in the model, and a compact generative model of the training sample distribution that significantly reduces memory and time complexity, while providing better diversity of samples.
- 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.
- Accurate Scale Estimation for Robust Visual Tracking2,087
This paper presents a novel approach to robust scale estimation that can handle large scale variations in complex image sequences and shows promising results in terms of accuracy and efficiency.
- Beyond Correlation Filters: Learning Continuous Convolution Operators for Visual Tracking1,770
Discriminative Correlation Filters have demonstrated excellent performance for visual object tracking and the key to their success is the ability to efficiently exploit available negative data.
- 1,512
- ATOM: Accurate Tracking by Overlap Maximization1,509
This work proposes a novel tracking architecture, consisting of dedicated target estimation and classification components, and introduces a classification component that is trained online to guarantee high discriminative power in the presence of distractors.
- 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.
- Discriminative Scale Space Tracking1,312
This paper proposes a novel scale adaptive tracking approach by learning separate discriminative correlation filters for translation and scale estimation in a tracking-by-detection framework that obtains the top rank in performance by outperforming 19 state-of-the-art trackers on OTB and 37 state of theart tracker on VOT2014.
- MaPLe: Multi-modal Prompt Learning1,242
The design promotes strong coupling between the vision-language prompts to ensure mutual synergy and discourages learning independent uni-modal solutions, and the effectiveness of the approach is evaluated on three representative tasks of generalization to novel classes, new target datasets and unseen domain shifts.
- 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.
- 975
- The Sixth Visual Object Tracking VOT2018 Challenge Results804
The Visual Object Tracking challenge VOT2018 is the sixth annual tracker benchmarking activity organized by the VOT initiative; results of over eighty trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years.
- The Visual Object Tracking VOT2016 Challenge Results776
The Visual Object Tracking challenge VOT2016 goes beyond its predecessors by introducing a new semi-automatic ground truth bounding box annotation methodology and extending the evaluation system with the no-reset experiment.
- 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.
- GLaMM: Pixel Grounding Large Multimodal Model594
This work presents Grounding LMM (GLaMM), the first model that can generate natural language responses seamlessly in-tertwined with corresponding object segmentation masks and is flexible enough to accept both textual and optional visual prompts (region of interest) as input.
- The Visual Object Tracking VOT2014 Challenge Results588
The Visual Object Tracking challenge 2014, VOT2014, aims at comparing short-term single-object visual trackers that do not apply pre-learned models of object appearance and introduces a new dataset with full annotation of targets by rotated bounding boxes and per-frame attribute.
- GeoChat:Grounded Large Vision-Language Model for Remote Sensing576
This work proposes GeoChat - the first versatile remote sensing VLM that offers multitask conversational capabilities with high-resolution RS images, and generates a novel RS multimodal instruction-following dataset by extending image-text pairs from existing diverse RS datasets.
- 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.
- 496
- Unveiling the Power of Deep Tracking484
This paper proposes a novel adaptive fusion approach that leverages the complementary properties of deep and shallow features to improve both robustness and accuracy in generic object tracking.
- Self-regulating Prompts: Foundational Model Adaptation without Forgetting460
This work introduces a self-regularization framework for prompting called PromptSRC (Prompting with Self-regulating Constraints), the first regularization framework for prompt learning that avoids overfitting by jointly attending to pre-trained model features, the training trajectory during prompting, and the textual diversity.
- 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.
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.
Report an error
Wrong papers, two people merged into one, or a profile that should not be here? Tell us and we will fix or hide it.