Concor-2-Data
Collection
Curated VidSTG and VIPOSeg datasets with caption grounding, action labels, temporal intervals, and object masks. • 6 items • Updated
Datasets:
caption_id stringlengths 17 20 | video_id stringlengths 11 13 | sampled_frame_ids listlengths 2 113 | text stringlengths 31 353 | source dict | actions listlengths 0 9 |
|---|---|---|---|---|---|
-1H5IyO1MNY:E2:A1 | -1H5IyO1MNY | [
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22... | A bartender in a tuxedo holds a bottle of red liqueur and pours it into a small shot glass on the bar, filling it about halfway. | {
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22... | A close-up shot shows a finished layered drink in a shot glass. The bottom layer is red and the top layer is green. Two liquor bottles are visible in the background. | {
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-1H5IyO1MNY:E3:A1 | -1H5IyO1MNY | [
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22... | A person in a tuxedo pours a red liquid from a bottle with a white and red label into a small shot glass on a dark countertop. | {
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-1H5IyO1MNY:E3:A2 | -1H5IyO1MNY | [
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22... | The bartender sets down the red bottle, picks up a bottle of green liquid, and then picks up a long, thin bar spoon. | {
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-1H5IyO1MNY:E3:A3 | -1H5IyO1MNY | [
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22... | The bartender holds a bar spoon over the shot glass and carefully pours the green liquid from the bottle onto the back of the spoon, creating a separate green layer on top of the red liquid. | {
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22... | A bartender in a tuxedo places a small bottle of red liqueur on the bar counter next to two shot glasses. He then picks up a bottle of clear liquid from the back shelf. | {
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-1H5IyO1MNY:E4:A2 | -1H5IyO1MNY | [
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22... | The bartender holds a bar spoon over a shot glass containing red and green liquids. He slowly pours a clear liqueur from a bottle over the back of the spoon, creating a third layer in the glass. | {
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-1H5IyO1MNY:E4:A3 | -1H5IyO1MNY | [
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22... | After finishing the pour, the bartender places the bottle on the counter. The woman at the bar reaches for the completed shot glass as the bartender slides it towards her. | {
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EventLens Caption, Action and Temporal Annotations
11,763 original captions from 1,053 videos, with 25,792 localized actions and 27,954 temporal intervals. Source clips are 1–2 minutes long; source captions of 1 second or less are excluded.
| File | Contents |
|---|---|
captions/train.parquet |
One original caption per row, action character spans and temporal intervals |
videos/train.parquet |
One video per row, source metadata, exact frame/PTS mappings and image URLs |
dataset_info.json |
Counts, selection and field conventions |
Join the tables by video_id. Nested fields are native Parquet lists and structs.
from datasets import load_dataset
repo = "Viviannnyang/EventLens-caption-action-temporal-train"
captions = load_dataset(repo, split="train")
videos = load_dataset(repo, "videos", split="train")
All character spans and temporal ranges are zero-based and half-open. Action mentions use spans=[{"start": ..., "end": ...}] over text, with an optional separate particle span. Action segments contain start_native_frame, end_native_frame_exclusive, start_s, end_s, global 1 FPS indices and exact rational times. Explicit apparel and accessory wearing predicates use appearance_state. Every segment remains inside its original caption's source.window.
Frames are sampled at 1 FPS from source clips encoded at 3 FPS, anchored at original-video t0. Native frame IDs and rational PTS are preserved without rounding. sampled_frame_ids lists shared model-input native frames; source.allowed_native_frame_ids restricts evidence to each caption. videos.frames.global_sample_frame_index preserves the full-video index even where input frames have gaps. Image URLs point to a pinned frame-repository revision.
Original captions remain unchanged, including 1,414 rows with actions=[]; empty lists are not negative action labels. Annotations are model-generated from sampled images and are not individually human-verified.
Source: Lostgreen/EventLens-Train. Upstream attribution, access and licensing terms apply.
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