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Works54 from public data
- RescueNet: An unpaired GAN for brain tumor segmentation182
A network architecture named as residual cyclic unpaired encoder-decoder network (RescueNet) is designed using residual and mirroring principles to segment the whole tumor followed by core and enhance regions in a brain MRI scan.
- Burst Image Restoration and Enhancement129
The central idea is to create a set of pseudo-burst features that combine complimentary information from all the input burst frames to seamlessly exchange information, and the approach initially extracts pre-processed features from each burst frame and matches them using an edge-boosting burst alignment module.
- EarthDial: Turning Multi-sensory Earth Observations to Interactive Dialogues110
EarthDial is introduced, a conversational assistant specifically designed for Earth Observation (EO) data, transforming complex, multi-sensory Earth observations into interactive, natural language dialogues, and outperforms existing generic and domain-specific models, achieving better generalization across various EO tasks.
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- Burstormer: Burst Image Restoration and Enhancement Transformer79
This work proposes Burstormer: a novel transformer-based architecture for burst image restoration and enhancement that outperforms state-of-the-art methods on burst super-resolution, burst denoising and burst low-light enhance-ment.
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- C^2MSNet: A Novel Approach for Single Image Haze Removal60
Performance analysis shows that the proposed cardinal (red, green and blue) color fusion network for single image haze removal outperforms the existing state-of-the-art methods forsingle image dehazing.
- ThinkGeo: Evaluating Tool-Augmented Agents for Remote Sensing Tasks49
ThinkGeo, an agentic benchmark designed to evaluate LLM-driven agents on remote sensing tasks via structured tool use and multi-step planning, provides the first extensive testbed for evaluating how tool-enabled LLMs handle spatial reasoning in remote sensing.
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- Multi‐frame based adversarial learning approach for video surveillance45
A novel multi-frame-based adversarial learning network is proposed with multi-scale inception and residual module for FBS to learn prominent features of the foreground object(s) to exhibit more temporal consistency.
- An Unified Recurrent Video Object Segmentation Framework for Various Surveillance Environments45
It is demonstrated that the proposed method outperforms the state-of-the-art methods for MOS without any pre-trained module, fine-tuning on the test video frame(s) or complicated training.
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- The Tenth NTIRE 2025 Efficient Super-Resolution Challenge Report39
This paper presents a comprehensive review of the NTIRE 2025 Challenge on Single-Image Efficient Super-Resolution, emphasizing groundbreaking advancements in state-of-the-art single-image ESR techniques.
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- AVisT: A Benchmark for Visual Object Tracking in Adverse Visibility36
AVisT is introduced, a dedicated benchmark for visual tracking in diverse scenarios with adverse visibility and can greatly benefit the tracking community by complementing the existing benchmarks, in developing new creative tracking solutions in order to continue pushing the boundaries of the state-of-the-art.
- The Tenth NTIRE 2025 Image Denoising Challenge Report34
An overview of the NTIRE 2025 Image Denoising Challenge is presented, highlighting the proposed methodologies and corresponding results, providing insights into the current state-of-the-art in image denoising.
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- NTIRE 2026 Challenge on Robust AI-Generated Image Detection in the Wild29
This report provides a comprehensive overview of the NTIRE 2026 Challenge on Robust AI-Generated Image Detection in the Wild, describes the proposed solutions, and can be used as a valuable reference for researchers and practitioners in increasing the robustness of the detection models to real-world transformations.
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 2026-10-14. 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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