多模态处理最佳实践
本文档整理多模态（文本、图像、音频、视频）AI 处理的最佳实践。
多模态概述
txt
┌─────────────────────────────────────────────────────┐
│ 多模态处理架构 │
├─────────────────────────────────────────────────────┤
│ │
│ 输入层 │
│ ┌─────┐ ┌─────┐ ┌─────┐ ┌─────┐ │
│ │文本 │ │图像 │ │音频 │ │视频 │ │
│ └──┬──┘ └──┬──┘ └──┬──┘ └──┬──┘ │
│ │ │ │ │ │
│ ↓ ↓ ↓ ↓ │
│ 编码层 │
│ ┌─────┐ ┌─────┐ ┌─────┐ ┌─────┐ │
│ │文本 │ │图像 │ │音频 │ │视频 │ │
│ │编码 │ │编码 │ │编码 │ │编码 │ │
│ └──┬──┘ └──┬──┘ └──┬──┘ └──┬──┘ │
│ │ │ │ │ │
│ └───────┴───────┴───────┘ │
│ │ │
│ ↓ │
│ 融合层 │
│ ┌─────────────────────────────┐ │
│ │ 多模态特征融合 │ │
│ │ Attention │ Cross-Modal │ │
│ └──────────────┬──────────────┘ │
│ │ │
│ ↓ │
│ 输出层 │
│ ┌─────┐ ┌─────┐ ┌─────┐ ┌─────┐ │
│ │文本 │ │图像 │ │音频 │ │视频 │ │
│ │生成 │ │生成 │ │生成 │ │生成 │ │
│ └─────┘ └─────┘ └─────┘ └─────┘ │
│ │
└─────────────────────────────────────────────────────┘图像处理
图像输入处理
python
from dataclasses import dataclass
from typing import List, Optional, Tuple
from enum import Enum
import base64
from io import BytesIO
class ImageFormat(Enum):
JPEG = "jpeg"
PNG = "png"
WEBP = "webp"
GIF = "gif"
@dataclass
class ImageInput:
"""图像输入"""
data: bytes
format: ImageFormat
width: int
height: int
caption: Optional[str] = None
def to_base64(self) -> str:
"""转换为 Base64"""
return base64.b64encode(self.data).decode('utf-8')
def to_data_url(self) -> str:
"""转换为 Data URL"""
return f"data:image/{self.format.value};base64,{self.to_base64()}"
class ImageProcessor:
"""图像处理器"""
def __init__(
self,
max_width: int = 2048,
max_height: int = 2048,
max_size_mb: int = 20,
preferred_format: ImageFormat = ImageFormat.JPEG
):
self.max_width = max_width
self.max_height = max_height
self.max_size_mb = max_size_mb
self.preferred_format = preferred_format
def process(self, image: ImageInput) -> ImageInput:
"""处理图像"""
# 检查尺寸
if image.width > self.max_width or image.height > self.max_height:
image = self._resize(image)
# 检查大小
if len(image.data) > self.max_size_mb * 1024 * 1024:
image = self._compress(image)
return image
def _resize(self, image: ImageInput) -> ImageInput:
"""调整尺寸"""
from PIL import Image
img = Image.open(BytesIO(image.data))
# 计算缩放比例
ratio = min(self.max_width / image.width, self.max_height / image.height)
new_width = int(image.width * ratio)
new_height = int(image.height * ratio)
# 调整尺寸
img_resized = img.resize((new_width, new_height), Image.LANCZOS)
# 转换为字节
buffer = BytesIO()
img_resized.save(buffer, format=image.format.value.upper())
return ImageInput(
data=buffer.getvalue(),
format=image.format,
width=new_width,
height=new_height,
caption=image.caption
)
def _compress(self, image: ImageInput) -> ImageInput:
"""压缩图像"""
from PIL import Image
img = Image.open(BytesIO(image.data))
# 逐步降低质量直到满足大小要求
quality = 95
while quality > 50:
buffer = BytesIO()
img.save(buffer, format="JPEG", quality=quality)
if len(buffer.getvalue()) <= self.max_size_mb * 1024 * 1024:
return ImageInput(
data=buffer.getvalue(),
format=ImageFormat.JPEG,
width=image.width,
height=image.height,
caption=image.caption
)
quality -= 5
return image
def create_grid(
self,
images: List[ImageInput],
grid_size: Tuple[int, int] = None
) -> ImageInput:
"""创建图像网格"""
from PIL import Image
if not images:
raise ValueError("No images provided")
# 自动计算网格大小
if grid_size is None:
import math
n = len(images)
cols = math.ceil(math.sqrt(n))
rows = math.ceil(n / cols)
grid_size = (rows, cols)
rows, cols = grid_size
# 计算单元格大小
cell_width = max(img.width for img in images)
cell_height = max(img.height for img in images)
# 创建网格
grid = Image.new('RGB', (cols * cell_width, rows * cell_height))
for i, image in enumerate(images):
row = i // cols
col = i % cols
img = Image.open(BytesIO(image.data))
# 居中放置
x = col * cell_width + (cell_width - img.width) // 2
y = row * cell_height + (cell_height - img.height) // 2
grid.paste(img, (x, y))
# 转换为字节
buffer = BytesIO()
grid.save(buffer, format="JPEG", quality=90)
return ImageInput(
data=buffer.getvalue(),
format=ImageFormat.JPEG,
width=cols * cell_width,
height=rows * cell_height
)
# 使用示例
processor = ImageProcessor(max_width=1024, max_height=1024)
# 处理单张图像
with open("image.jpg", "rb") as f:
image_data = f.read()
image = ImageInput(
data=image_data,
format=ImageFormat.JPEG,
width=4000,
height=3000
)
processed = processor.process(image)
print(f"处理后的尺寸: {processed.width}x{processed.height}")图像理解
python
from typing import List, Dict, Any, Optional
from dataclasses import dataclass
@dataclass
class BoundingBox:
"""边界框"""
x1: float
y1: float
x2: float
y2: float
label: str
confidence: float
@dataclass
class ImageAnalysis:
"""图像分析结果"""
description: str
objects: List[BoundingBox]
text_content: List[str]
colors: List[str]
tags: List[str]
confidence: float
class ImageUnderstanding:
"""图像理解"""
def __init__(self, model_client):
self.model_client = model_client
async def describe(
self,
image: ImageInput,
detail_level: str = "medium"
) -> str:
"""描述图像"""
prompts = {
"brief": "用一句话描述这张图片。",
"medium": "详细描述这张图片的内容，包括主要对象、场景和氛围。",
"detailed": "非常详细地描述这张图片，包括：1) 主要对象及其位置、颜色、大小；2) 背景和环境；3) 图像的整体风格和氛围；4) 任何有趣的细节。"
}
prompt = prompts.get(detail_level, prompts["medium"])
# 调用多模态模型
response = await self.model_client.generate(
messages=[
{
"role": "user",
"content": [
{"type": "text", "text": prompt},
{"type": "image_url", "image_url": {"url": image.to_data_url()}}
]
}
]
)
return response.content
async def extract_text(self, image: ImageInput) -> List[str]:
"""提取图像中的文字"""
response = await self.model_client.generate(
messages=[
{
"role": "user",
"content": [
{"type": "text", "text": "提取图像中的所有文字，按阅读顺序列出。如果没有文字，返回空列表。"},
{"type": "image_url", "image_url": {"url": image.to_data_url()}}
]
}
]
)
# 解析文字列表
text_content = response.content.strip().split('\n')
return [t.strip() for t in text_content if t.strip()]
async def detect_objects(
self,
image: ImageInput,
object_types: List[str] = None
) -> List[BoundingBox]:
"""检测物体"""
object_prompt = ""
if object_types:
object_prompt = f"特别关注以下类型的物体：{', '.join(object_types)}。"
response = await self.model_client.generate(
messages=[
{
"role": "user",
"content": [
{
"type": "text",
"text": f"""检测图像中的所有物体。{object_prompt}
对于每个物体，提供：
1. 物体名称
2. 边界框坐标（x1, y1, x2, y2，范围 0-1000）
3. 置信度（0-1）
输出格式：
[物体名称] [x1] [y1] [x2] [y2] [置信度]"""
},
{"type": "image_url", "image_url": {"url": image.to_data_url()}}
]
}
]
)
# 解析边界框
boxes = []
for line in response.content.strip().split('\n'):
parts = line.strip().split()
if len(parts) >= 6:
try:
box = BoundingBox(
label=parts[0],
x1=float(parts[1]),
y1=float(parts[2]),
x2=float(parts[3]),
y2=float(parts[4]),
confidence=float(parts[5])
)
boxes.append(box)
except ValueError:
continue
return boxes
async def analyze(
self,
image: ImageInput,
tasks: List[str] = None
) -> ImageAnalysis:
"""综合分析"""
if tasks is None:
tasks = ["describe", "objects", "text", "colors", "tags"]
results = {}
if "describe" in tasks:
results["description"] = await self.describe(image, "detailed")
if "objects" in tasks:
results["objects"] = await self.detect_objects(image)
if "text" in tasks:
results["text"] = await self.extract_text(image)
return ImageAnalysis(
description=results.get("description", ""),
objects=results.get("objects", []),
text_content=results.get("text", []),
colors=[],
tags=[],
confidence=0.0
)
# 使用示例
async def analyze_image():
processor = ImageProcessor()
understanding = ImageUnderstanding(model_client)
# 加载和处理图像
with open("photo.jpg", "rb") as f:
image = ImageInput(
data=f.read(),
format=ImageFormat.JPEG,
width=2000,
height=1500
)
processed = processor.process(image)
# 综合分析
analysis = await understanding.analyze(processed)
print(f"描述: {analysis.description}")
print(f"物体: {[obj.label for obj in analysis.objects]}")
print(f"文字: {analysis.text_content}")音频处理
音频输入处理
python
from dataclasses import dataclass
from typing import List, Optional
from enum import Enum
import wave
import struct
class AudioFormat(Enum):
WAV = "wav"
MP3 = "mp3"
FLAC = "flac"
OGG = "ogg"
@dataclass
class AudioInput:
"""音频输入"""
data: bytes
format: AudioFormat
sample_rate: int
channels: int
duration_seconds: float
def to_base64(self) -> str:
"""转换为 Base64"""
import base64
return base64.b64encode(self.data).decode('utf-8')
class AudioProcessor:
"""音频处理器"""
def __init__(
self,
max_duration_seconds: int = 300,
target_sample_rate: int = 16000,
target_channels: int = 1
):
self.max_duration_seconds = max_duration_seconds
self.target_sample_rate = target_sample_rate
self.target_channels = target_channels
def process(self, audio: AudioInput) -> AudioInput:
"""处理音频"""
# 检查时长
if audio.duration_seconds > self.max_duration_seconds:
audio = self._trim(audio, 0, self.max_duration_seconds)
# 重采样
if audio.sample_rate != self.target_sample_rate:
audio = self._resample(audio, self.target_sample_rate)
# 转换声道
if audio.channels != self.target_channels:
audio = self._convert_channels(audio, self.target_channels)
return audio
def _trim(
self,
audio: AudioInput,
start: float,
end: float
) -> AudioInput:
"""裁剪音频"""
# 简化实现
return audio
def _resample(
self,
audio: AudioInput,
target_sample_rate: int
) -> AudioInput:
"""重采样"""
# 简化实现
return audio
def _convert_channels(
self,
audio: AudioInput,
target_channels: int
) -> AudioInput:
"""转换声道"""
# 简化实现
return audio
def split_by_silence(
self,
audio: AudioInput,
min_silence_duration: float = 0.5,
silence_threshold_db: float = -40
) -> List[AudioInput]:
"""按静音分割"""
# 简化实现
return [audio]
# 使用示例
processor = AudioProcessor(max_duration_seconds=60)
# 处理音频
audio = AudioInput(
data=b"...",
format=AudioFormat.WAV,
sample_rate=44100,
channels=2,
duration_seconds=120
)
processed = processor.process(audio)
print(f"处理后: {processed.duration_seconds}秒, {processed.sample_rate}Hz, {processed.channels}声道")语音识别与合成
python
from dataclasses import dataclass
from typing import List, Optional
from enum import Enum
class LanguageCode(Enum):
ZH_CN = "zh-CN"
EN_US = "en-US"
JA_JP = "ja-JP"
KO_KR = "ko-KR"
@dataclass
class TranscriptionResult:
"""转录结果"""
text: str
segments: List[dict]
language: str
confidence: float
@dataclass
class SynthesisResult:
"""合成结果"""
audio_data: bytes
format: AudioFormat
sample_rate: int
duration_seconds: float
class SpeechProcessor:
"""语音处理器"""
def __init__(self, asr_client, tts_client):
self.asr_client = asr_client # 语音识别客户端
self.tts_client = tts_client # 语音合成客户端
async def transcribe(
self,
audio: AudioInput,
language: LanguageCode = LanguageCode.ZH_CN,
enable_diarization: bool = False
) -> TranscriptionResult:
"""语音转文字"""
response = await self.asr_client.transcribe(
audio=audio.data,
language=language.value,
enable_diarization=enable_diarization
)
return TranscriptionResult(
text=response.text,
segments=response.segments,
language=response.language,
confidence=response.confidence
)
async def synthesize(
self,
text: str,
voice: str = "default",
language: LanguageCode = LanguageCode.ZH_CN,
speed: float = 1.0
) -> SynthesisResult:
"""文字转语音"""
response = await self.tts_client.synthesize(
text=text,
voice=voice,
language=language.value,
speed=speed
)
return SynthesisResult(
audio_data=response.audio,
format=AudioFormat.MP3,
sample_rate=24000,
duration_seconds=len(response.audio) / 24000 / 2 # 估算
)
async def translate_speech(
self,
audio: AudioInput,
source_language: LanguageCode,
target_language: LanguageCode
) -> TranscriptionResult:
"""语音翻译"""
# 先转录
transcription = await self.transcribe(audio, source_language)
# 翻译
translated_text = await self._translate(
transcription.text,
source_language.value,
target_language.value
)
return TranscriptionResult(
text=translated_text,
segments=transcription.segments,
language=target_language.value,
confidence=transcription.confidence
)
async def _translate(
self,
text: str,
source_lang: str,
target_lang: str
) -> str:
"""翻译文本"""
# 简化实现
return text
# 使用示例
async def process_speech():
processor = SpeechProcessor(asr_client, tts_client)
# 语音转文字
transcription = await processor.transcribe(
audio=processed_audio,
language=LanguageCode.ZH_CN
)
print(f"转录: {transcription.text}")
# 文字转语音
synthesis = await processor.synthesize(
text="你好，世界",
voice="female-1",
language=LanguageCode.ZH_CN
)
print(f"合成长度: {synthesis.duration_seconds}秒")视频处理
视频分析
python
from dataclasses import dataclass
from typing import List, Optional
import subprocess
import json
@dataclass
class VideoInfo:
"""视频信息"""
duration_seconds: float
width: int
height: int
fps: float
codec: str
bitrate: int
has_audio: bool
audio_codec: Optional[str]
@dataclass
class VideoFrame:
"""视频帧"""
timestamp: float
image: ImageInput
description: Optional[str] = None
@dataclass
class VideoAnalysis:
"""视频分析结果"""
info: VideoInfo
key_frames: List[VideoFrame]
transcript: Optional[str]
summary: str
tags: List[str]
class VideoProcessor:
"""视频处理器"""
def __init__(
self,
max_duration_seconds: int = 600,
max_width: int = 1920,
max_height: int = 1080,
frame_sample_interval: float = 1.0
):
self.max_duration_seconds = max_duration_seconds
self.max_width = max_width
self.max_height = max_height
self.frame_sample_interval = frame_sample_interval
def get_info(self, video_path: str) -> VideoInfo:
"""获取视频信息"""
cmd = [
"ffprobe",
"-v", "quiet",
"-print_format", "json",
"-show_format",
"-show_streams",
video_path
]
result = subprocess.run(cmd, capture_output=True, text=True)
data = json.loads(result.stdout)
video_stream = None
audio_stream = None
for stream in data.get("streams", []):
if stream.get("codec_type") == "video" and not video_stream:
video_stream = stream
elif stream.get("codec_type") == "audio" and not audio_stream:
audio_stream = stream
duration = float(data.get("format", {}).get("duration", 0))
return VideoInfo(
duration_seconds=duration,
width=int(video_stream.get("width", 0)),
height=int(video_stream.get("height", 0)),
fps=eval(video_stream.get("r_frame_rate", "0/1")),
codec=video_stream.get("codec_name", ""),
bitrate=int(data.get("format", {}).get("bit_rate", 0)),
has_audio=audio_stream is not None,
audio_codec=audio_stream.get("codec_name") if audio_stream else None
)
def extract_frames(
self,
video_path: str,
interval: float = None,
max_frames: int = 30
) -> List[VideoFrame]:
"""提取关键帧"""
if interval is None:
interval = self.frame_sample_interval
info = self.get_info(video_path)
frames = []
# 计算需要提取的帧数
num_frames = min(int(info.duration_seconds / interval), max_frames)
for i in range(num_frames):
timestamp = i * interval
# 使用 ffmpeg 提取帧
output_path = f"/tmp/frame_{i}.jpg"
cmd = [
"ffmpeg",
"-ss", str(timestamp),
"-i", video_path,
"-vframes", "1",
"-q:v", "2",
output_path
]
subprocess.run(cmd, capture_output=True)
# 读取帧图像
with open(output_path, "rb") as f:
frame_data = f.read()
frames.append(VideoFrame(
timestamp=timestamp,
image=ImageInput(
data=frame_data,
format=ImageFormat.JPEG,
width=info.width,
height=info.height
)
))
return frames
def extract_audio(
self,
video_path: str,
output_path: str
) -> AudioInput:
"""提取音频"""
cmd = [
"ffmpeg",
"-i", video_path,
"-vn",
"-acodec", "pcm_s16le",
"-ar", "16000",
"-ac", "1",
output_path
]
subprocess.run(cmd, capture_output=True)
with open(output_path, "rb") as f:
audio_data = f.read()
return AudioInput(
data=audio_data,
format=AudioFormat.WAV,
sample_rate=16000,
channels=1,
duration_seconds=self.get_info(video_path).duration_seconds
)
class VideoUnderstanding:
"""视频理解"""
def __init__(self, video_processor: VideoProcessor, image_understanding: ImageUnderstanding, speech_processor: SpeechProcessor):
self.video_processor = video_processor
self.image_understanding = image_understanding
self.speech_processor = speech_processor
async def analyze(
self,
video_path: str,
extract_audio: bool = True,
analyze_frames: bool = True,
max_frames: int = 10
) -> VideoAnalysis:
"""综合分析视频"""
info = self.video_processor.get_info(video_path)
key_frames = []
transcript = None
summaries = []
# 提取和分析关键帧
if analyze_frames:
frames = self.video_processor.extract_frames(
video_path,
max_frames=max_frames
)
for frame in frames:
description = await self.image_understanding.describe(
frame.image,
detail_level="brief"
)
frame.description = description
key_frames.append(frame)
summaries.append(f"[{frame.timestamp:.1f}s] {description}")
# 提取和转录音频
if extract_audio and info.has_audio:
audio = self.video_processor.extract_audio(
video_path,
"/tmp/audio.wav"
)
transcription = await self.speech_processor.transcribe(audio)
transcript = transcription.text
# 生成总结
if summaries:
summary_prompt = f"""根据以下关键帧描述，总结这个视频的主要内容：
{chr(10).join(summaries)}
音频转录（如果有）：
{transcript or '无'}
请提供一个简洁的总结："""
# 调用模型生成总结
summary = summary_prompt # 简化实现
return VideoAnalysis(
info=info,
key_frames=key_frames,
transcript=transcript,
summary=summary if 'summary' in dir() else "",
tags=[]
)
# 使用示例
async def analyze_video():
video_processor = VideoProcessor()
understanding = VideoUnderstanding(
video_processor,
image_understanding,
speech_processor
)
analysis = await understanding.analyze(
"video.mp4",
extract_audio=True,
analyze_frames=True,
max_frames=10
)
print(f"视频时长: {analysis.info.duration_seconds}秒")
print(f"关键帧: {len(analysis.key_frames)}个")
print(f"转录: {analysis.transcript[:100] if analysis.transcript else '无'}...")
print(f"总结: {analysis.summary}")多模态融合
跨模态检索
python
from typing import List, Dict, Any, Tuple
from dataclasses import dataclass
import numpy as np
@dataclass
class MultiModalItem:
"""多模态项目"""
id: str
text: Optional[str] = None
image: Optional[ImageInput] = None
audio: Optional[AudioInput] = None
video_path: Optional[str] = None
metadata: Dict[str, Any] = None
@dataclass
class SearchResult:
"""搜索结果"""
item: MultiModalItem
score: float
matched_modalities: List[str]
class MultiModalRetriever:
"""多模态检索器"""
def __init__(self, text_encoder, image_encoder, audio_encoder):
self.text_encoder = text_encoder
self.image_encoder = image_encoder
self.audio_encoder = audio_encoder
self.items: List[MultiModalItem] = []
self.text_embeddings: Dict[str, np.ndarray] = {}
self.image_embeddings: Dict[str, np.ndarray] = {}
self.audio_embeddings: Dict[str, np.ndarray] = {}
def index(self, items: List[MultiModalItem]):
"""索引项目"""
for item in items:
self.items.append(item)
# 编码文本
if item.text:
self.text_embeddings[item.id] = self.text_encoder.encode(item.text)
# 编码图像
if item.image:
self.image_embeddings[item.id] = self.image_encoder.encode(item.image.data)
# 编码音频
if item.audio:
self.audio_embeddings[item.id] = self.audio_encoder.encode(item.audio.data)
def search(
self,
query: MultiModalItem,
top_k: int = 10,
weights: Dict[str, float] = None
) -> List[SearchResult]:
"""搜索"""
if weights is None:
weights = {"text": 0.4, "image": 0.4, "audio": 0.2}
# 编码查询
query_text_embedding = None
query_image_embedding = None
query_audio_embedding = None
if query.text:
query_text_embedding = self.text_encoder.encode(query.text)
if query.image:
query_image_embedding = self.image_encoder.encode(query.image.data)
if query.audio:
query_audio_embedding = self.audio_encoder.encode(query.audio.data)
# 计算相似度
scores: Dict[str, Tuple[float, List[str]]] = {}
for item in self.items:
total_score = 0.0
matched_modalities = []
# 文本相似度
if query_text_embedding is not None and item.id in self.text_embeddings:
similarity = self._cosine_similarity(
query_text_embedding,
self.text_embeddings[item.id]
)
total_score += similarity * weights["text"]
matched_modalities.append("text")
# 图像相似度
if query_image_embedding is not None and item.id in self.image_embeddings:
similarity = self._cosine_similarity(
query_image_embedding,
self.image_embeddings[item.id]
)
total_score += similarity * weights["image"]
matched_modalities.append("image")
# 音频相似度
if query_audio_embedding is not None and item.id in self.audio_embeddings:
similarity = self._cosine_similarity(
query_audio_embedding,
self.audio_embeddings[item.id]
)
total_score += similarity * weights["audio"]
matched_modalities.append("audio")
scores[item.id] = (total_score, matched_modalities)
# 排序
sorted_scores = sorted(
scores.items(),
key=lambda x: x[1][0],
reverse=True
)
# 构建结果
results = []
for item_id, (score, modalities) in sorted_scores[:top_k]:
item = next(i for i in self.items if i.id == item_id)
results.append(SearchResult(
item=item,
score=score,
matched_modalities=modalities
))
return results
def _cosine_similarity(self, a: np.ndarray, b: np.ndarray) -> float:
"""计算余弦相似度"""
return float(np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b)))
# 使用示例
async def multi_modal_search():
retriever = MultiModalRetriever(text_encoder, image_encoder, audio_encoder)
# 索引项目
items = [
MultiModalItem(
id="item1",
text="一只猫在草地上",
image=cat_image,
metadata={"source": "user1"}
),
MultiModalItem(
id="item2",
text="狗在海边玩耍",
image=dog_image,
metadata={"source": "user2"}
),
]
retriever.index(items)
# 搜索
query = MultiModalItem(
text="可爱的动物",
image=query_image
)
results = retriever.search(query, top_k=5)
for result in results:
print(f"ID: {result.item.id}, Score: {result.score:.2f}")
print(f"匹配模态: {result.matched_modalities}")最佳实践总结
多模态处理检查清单
markdown
## 多模态处理检查清单
### 图像处理
- [ ] 图像尺寸限制
- [ ] 图像大小压缩
- [ ] 格式转换
- [ ] 图像质量检查
- [ ] 敏感内容过滤
### 音频处理
- [ ] 音频时长限制
- [ ] 采样率标准化
- [ ] 声道转换
- [ ] 音频质量检查
- [ ] 静音检测
### 视频处理
- [ ] 视频时长限制
- [ ] 关键帧提取
- [ ] 音频分离
- [ ] 视频压缩
- [ ] 格式兼容性
### 融合策略
- [ ] 模态权重设置
- [ ] 特征对齐
- [ ] 时序同步
- [ ] 缺失模态处理
- [ ] 结果融合
### 性能优化
- [ ] 批量处理
- [ ] 异步编码
- [ ] 缓存策略
- [ ] 模型量化
- [ ] GPU 加速