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- MOSEv2: A More Challenging Dataset for Video Object Segmentation in Complex Scenes74
This work presents MOSEv2, a significantly more challenging dataset designed to further advance VOS methods under real-world conditions, and benchmark 20 representative VOS methods under 5 different settings, highlighting that current VOS methods still fall short under real-world complexities.
- MeViS: A Multi-Modal Dataset for Referring Motion Expression Video Segmentation69
This paper proposes a large-scale multi-modal dataset for referring motion expression video segmentation, focusing on segmenting and tracking target objects in videos based on language description of objects’ motions, and introduces MeViS, a dataset containing 33,072 human-annotated motion expressions in both text and audio.
- GREx: Generalized Referring Expression Segmentation, Comprehension, and Generation8
Three new benchmarks called Generalized Referring Expression Segmentation (GRES), Comprehension (GREC), and Generation (GREG), collectively denoted as GREx, which extend the classic REx to allow expressions to identify an arbitrary number of objects are introduced.
- Open-Set Anomaly Segmentation in Complex Scenarios1
This paper introduces the ComSAmy, a Complex Scenarios Anomaly segmentation benchmark, and proposes a novel energy-entropy learning (EEL) strategy that integrates the complementary information from energy and entropy to bolster the robustness of anomaly segmentation under complex open-world environments.
- GCA-SUNet: A Gated Context-Aware Swin-UNet for Exemplar-Free Counting1
A Gated Context-Aware Swin-UNet to directly map an input image to the density map of countable objects and demonstrates that GCA-SUNet significantly and consistently outperforms state-of-the-art methods.
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Publication data from OpenAlex; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-11. 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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