Datasets:

kfkas
/

streaming-gebd-causal

license: cc-by-nc-4.0
task_categories:
  - video-classification
tags:
  - event-boundary-detection
  - online-video
  - kinetics-400
  - causal-inference
size_categories:
  - 10K<n<100K
pretty_name: Audited Kinetics-GEBD Causal Metadata

Audited Kinetics-GEBD Causal Metadata

This metadata-only release converts the publicly released Kinetics-GEBD annotations into an auditable 24 FPS causal training representation. It does not redistribute Kinetics or YouTube video bytes.

Splits

Hub split	Official source file	Records	Meaning
`train`	`k400_train_raw_annotation.pkl`	18,808	Public GEBD training annotations
`validation`	`k400_val_raw_annotation.pkl`	18,815	Public Kinetics-GEBD validation annotations

The ICCV 2021 benchmark describes a separate challenge test subset sampled from Kinetics-400 train. Its labels are withheld and are not included here. The ICCV 2025 On-GEBD paper explicitly evaluates Kinetics-GEBD on the public validation split because test annotations are unavailable.

Annotation processing

The release reproduces the official prepare_k400_release.ipynb behavior, including its input-order and list-mutation quirks:

discard boundaries in the first or last 0.3 seconds;
merge overlapping gradual-shot ranges;
suppress timestamp boundaries around gradual-shot ranges;
suppress nearby same-type timestamps and prefer shots over events;
convert cleaned timestamps to source-frame indices with floor(timestamp * source_fps), capped at num_frames - 1.

The implementation was differentially checked against the official notebook over all 185,518 released rater annotations with zero timestamp-sequence differences. This is intentionally the code behavior: some effective suppression distances are 0.6 seconds even though the paper summarizes a 0.1-second merge rule.

Training uses the first maximum of f1_consis, matching numpy.argmax in the official loader. Evaluation must compare predictions independently with every retained rater and select the best rater-specific F1. Records with f1_consis_avg < 0.3 remain present but have evaluation_eligible=false.

Causal 24 FPS labels

The benchmark does not publish a canonical 24 FPS binary-label file. This release derives one for causal training:

sample_count = ceil(duration_seconds * 24);
sample i represents timestamp i / 24;
a sample is positive when it falls inclusively within 0.15 seconds of a cleaned boundary from the highest-consistency training rater.

boundary_source_frame_indices and training_boundary_source_frame_indices preserve official source-FPS frame coordinates. training_boundary_sample_indices and binary_labels are the derived 24 FPS projection. These fields must not be conflated.

Evaluation

The official metric is relative temporal distance, not the 0.15-second training window. The paper reports F1 at thresholds 0.05 through 0.50; the challenge ranking metric is F1@0.05. Matching is one-to-one and performed separately for each rater, retaining the rater yielding the best F1.

Videos

The local acquisition audit resolved and ffprobe-validated 37,608 of 37,623 referenced clips:

Source split	Available	Missing
train	18,794	14
val	18,814	1

The remaining 15 references were absent from all inspected public mirrors, the official CVDF Kinetics-400 archives, and accessible original-video paths. See VIDEO_ACQUISITION.md and manifests/ for exact availability and missing-reference records.

uv run scripts/build_official_on_gebd_dataset.py
uv run scripts/acquire_official_gebd_videos.py --workers 4

Rights

The GEBD annotation release is CC BY-NC 4.0 and the official repository code is MIT-licensed. Neither license grants redistribution rights for the underlying Kinetics/YouTube videos. A gated or private Hub repository does not change those rights, so this release contains metadata and manifests only.