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- Track Anything: Segment Anything Meets Videos363
The Track Anything Model (TAM) is proposed, which achieves high-performance interactive tracking and segmentation in videos with very little human participation, and achieves satisfactory results in one-pass inference.
- Human-like Summarization Evaluation with ChatGPT195
ChatGPT was able to complete annotations relatively smoothly using Likert scale scoring, pairwise comparison, Pyramid, and binary factuality evaluation, and it outperformed commonly used automatic evaluation metrics on some datasets.
- Deep learning for video object segmentation: a review109
A systematic review of the deep learning-based video segmentation literature, highlighting the pros and cons of each category of approaches and summarising the datasets for training and testing a video object segmentation algorithm.
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- Multi-scale Location-Aware Kernel Representation for Object Detection83
This paper proposes a novel Multi-scale Location-aware Kernel Representation (MLKP) to capture high-order statistics of deep features in proposals, which achieves very competitive performance with state-of-the-art methods, and improves Faster R-CNN by 4.9%, 4.7% and 5.0% respectively.
- Summarization is (Almost) Dead77
Most conventional works in the field of text summarization are no longer necessary in the era of LLMs, but there are still some directions worth exploring, such as the creation of novel datasets with higher quality and more reliable evaluation methods.
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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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