Three Options to Enhance CogVLM2 Video Processing Capabilities
CogVLM2 supports 1-minute video comprehension by default, but processing power can be extended through technical optimization:
- Keyframe extraction optimization: switch to a dynamic sampling strategy, increasing the sampling density for segments with large changes in motion (OpenCV implementation recommended)
- distributed processing: Slicing long videos into 1-minute segments to process them in parallel and finally merging the results (requires about 20% additional video memory overhead)
- Model Lightweight: 4-bit quantized version of cogvlm2-video-4bit is used, with a 40% increase in processable time.
Code Example:
import cv2
from cogvlm2 import CogVLM2
model = CogVLM2.load('video_model')
cap = cv2.VideoCapture('long_video.mp4')
# Customized keyframe interval (default 2 sec/frame)
frame_interval = 1 # Adjusted to 1 second/frame
while True:
ret, frame = cap.read()
if not ret: break
if int(cap.get(1)) % frame_interval == 0:.
result = model.predict(frame)
print(result)
caveat: More than 3 minutes of video is recommended to use the cloud service API batch processing, local deployment needs to take into account the video memory limit.
This answer comes from the articleCogVLM2: Open Source Multimodal Modeling with Support for Video Comprehension and Multi-Round DialogueThe
