Temporal Dynamics and Semantic Consistency: A Novel Framework for Unsupervised Video Summarization
We use the F-score as the evaluation metric, comparing the machine-generated summaries against human-annotated ground truth.
If you are instead referring to the (IEEE International Conference on Integrated Circuits, Design, and Verification) and need to prepare a technical paper, the submission requirements are as follows: ICDV 2026 Paper Submission Guidelines
I cannot browse the internet in real-time to find the specific contents of a document labeled "icdv-30037," as this appears to be a specific accession number (likely from a video or audio dataset). Without the source material, I cannot "make a deep paper" analyzing that specific file.
The "ICDV" prefix is associated with specific production labels or series. In the context of modern media distribution, these files are often processed using specific codecs to maintain high visual quality while reducing file size. Some listings for this code mention its availability in format, which is a standard for high-efficiency video coding.
The primary contribution of this work is the demonstration that adversarial training provides a robust signal for frame selection in the absence of labels. The discriminator forces the Selector to pick frames that are not just distinct but semantically "central" to the video's content.
Generative Adversarial Networks (GANs) have achieved remarkable success in image synthesis. Recently, adversarial loss has been applied to video tasks, such as video generation and future frame prediction. We draw inspiration from these works, applying adversarial training to the selector mechanism in summarization, forcing the model to select frames that "fool" a discriminator trained on global video semantics.
Visual inspection reveals that TSAN effectively avoids selecting redundant frames (e.g., static backgrounds) and focuses on dynamic actions. Unlike clustering methods that may select a diverse but semantically irrelevant set of frames, TSAN prioritizes frames that tell a coherent story, driven by the reconstruction objective.
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The proposed TSAN consists of three main components: an Encoder, a Selector, and a Discriminator.
Let a video $V$ be represented as a sequence of $N$ frames $x_1, x_2, ..., x_N$. The goal is to learn a selector $S$ that outputs a binary mask $s = s_1, s_2, ..., s_N$, where $s_i \in [0, 1]$, indicating the probability of the $i$-th frame being selected.