Principle:Zai org CogVideo Caption Generation
| Attribute | Value |
|---|---|
| Principle Name | Caption Generation |
| Workflow | Video Captioning |
| Step | 4 of 5 |
| Type | Core Algorithm |
| Repository | zai-org/CogVideo |
| Paper | CogVLM2 |
| Last Updated | 2026-02-10 00:00 GMT |
Overview
Technique for generating natural language descriptions of video content using a vision-language model. Caption generation processes video frames and a text prompt through the CogVLM2 multimodal architecture, then produces a detailed text description via autoregressive decoding.
Description
Caption generation uses the CogVLM2 model to produce detailed text descriptions of video content. The process involves:
- Input preparation: Video frames are extracted using the
load_videofunction and the text prompt is tokenized. The model'sbuild_conversation_input_idsmethod constructs the multimodal input by interleaving visual tokens with text tokens. - Forward pass: The combined visual and textual inputs are processed through the vision encoder, bridge module, and language model. The vision encoder extracts features from each frame, the bridge aligns these with the language model's embedding space, and the language model processes the full multimodal sequence.
- Autoregressive generation: The model generates caption tokens one at a time using the
model.generate()method. Generation parameters control the quality and determinism of the output.
Usage
Use Caption Generation after model loading and video frame extraction. The generated captions can be saved to files for use as training data in the CogVideoX fine-tuning pipeline.
Theoretical Basis
Autoregressive generation computes the probability of the caption given the video and prompt:
P(caption | video, prompt) = Product(P(w_t | w_{<t}, video, prompt))
where each token w_t is generated conditioned on all previous tokens w_{<t}, the video frames, and the instruction prompt.
Generation parameters:
- temperature = 0.1: Low temperature sharpens the probability distribution, favoring high-confidence tokens. This produces more deterministic and factually accurate captions.
- top_k = 1: Greedy decoding selects only the most probable token at each step, ensuring fully deterministic output.
- do_sample = False: Disables stochastic sampling, further enforcing deterministic generation.
- top_p = 0.1: Nucleus sampling threshold (largely redundant with top_k=1 and do_sample=False).
- max_new_tokens = 2048: Allows long, detailed captions up to approximately 1500 words.
The model's vision encoder processes temporal frame features jointly with the language model, enabling it to describe not just static visual content but also temporal dynamics (actions, transitions, motion).
Related Pages
- Implementation:Zai_org_CogVideo_CogVLM2_Predict -- Implementation of the predict function
- Zai_org_CogVideo_Video_Frame_Extraction -- Previous step: extracting frames from video
- Zai_org_CogVideo_Caption_Output -- Next step: saving generated captions to files
- Zai_org_CogVideo_Caption_Model_Loading -- Model loading that provides the model and tokenizer