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ENTITY ActivityNet: A large-scale video benchmark for human activity understanding

ActivityNet: A large-scale video benchmark for human activity understanding

PulseAugur coverage of ActivityNet: A large-scale video benchmark for human activity understanding — every cluster mentioning ActivityNet: A large-scale video benchmark for human activity understanding across labs, papers, and developer communities, ranked by signal.

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RECENT · PAGE 1/1 · 6 TOTAL
  1. TOOL · CL_298487 ·

    VEDJE system enables efficient video-text retrieval with compact caches

    Researchers have developed VEDJE, a novel system for scalable video-text retrieval that significantly reduces computational costs. VEDJE achieves this by compressing video features into a compact, reusable cache, allowi…

  2. TOOL · CL_289748 ·

    VEDJE encoder boosts video-text retrieval with compressed features

    Researchers have developed VEDJE, a novel Video-Efficient Discriminative Joint Encoder designed to enhance the scalability and efficiency of video-text retrieval. This method compresses video features into a reusable ca…

  3. RESEARCH · CL_233605 ·

    New datasets and models advance text-to-long-video retrieval capabilities

    Researchers have introduced MELON, a new large-scale dataset designed for multi-event text-to-long-video retrieval, addressing the limitations of existing datasets that primarily focus on short clips with single events.…

  4. TOOL · CL_181035 ·

    New Event ActivityNet benchmark advances untrimmed action understanding

    Researchers have introduced Event ActivityNet, a new benchmark designed to advance the field of untrimmed action understanding in videos. This dataset, derived from the existing ActivityNet videos, features over 3,200 v…

  5. TOOL · CL_180206 ·

    New PHA-Net improves text-video retrieval with prototype alignment

    Researchers have developed PHA-Net, a novel network for text-video retrieval that utilizes shared prototypes to align cross-modal representations efficiently. This approach addresses the semantic mismatch between text a…

  6. RESEARCH · CL_70411 ·

    MLLMs show positional bias in multi-video summarization

    Researchers have identified a positional bias in multimodal large language models (MLLMs) when summarizing multiple videos. This bias means the quality of a summary can depend on the order in which videos are presented …