Video-Text-to-Text
Transformers
Safetensors
causalvlm
text-generation
dense-video-captioning
causal-reasoning
video-understanding
# Load model directly
from transformers import AutoModelForSeq2SeqLM
model = AutoModelForSeq2SeqLM.from_pretrained("causalvlm/CausalVLM", device_map="auto")Quick Links
CausalVLM
Dense Causal Captioning (DCC): joint dense event captioning + causal-graph prediction for video.
CausalVLM produces timestamped dense event descriptions for a video and predicts a causal (prerequisite) graph over those events.
Usage
from huggingface_hub import snapshot_download
d = snapshot_download("causalvlm/CausalVLM")
import sys; sys.path.insert(0, d)
from modeling_causalvlm import CausalVLM
model = CausalVLM(d, device="cuda")
out = model.run("my_video.mp4")
for t, desc in out["events"]:
print(f"[{t:.1f}s] {desc}")
print("causal edges:", out["causal_edges"])
Example output (illustrative)
[t1s] <event 1 description>
[t2s] <event 2 description>
[t3s] <event 3 description>
causal edges: [(1, 2), (2, 3)]
Each (i, j) edge means event i is a prerequisite of event j.
License
CC BY-NC-ND 4.0.
Citation
(anonymous — under review)
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