القائمة
Integrating and reasoning across multiple data modalities including text, images, audio, and video
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
Building autonomous AI agents capable of reasoning, planning, and executing multi-step tasks
Learning from a small number of examples per class using metric learning and meta-learning
Techniques and frameworks for generating new data instances that match the distribution of training data
Advanced techniques for training and fine-tuning transformer-based language models at scale
Foundational understanding and practical implementation of transformer-based language models
Learning representations from unlabeled data using pretext tasks and contrastive methods
| name | Multi-Modal Learning |
| category | ai |
| description | Integrating and reasoning across multiple data modalities including text, images, audio, and video |
I enable AI systems to process, understand, and generate content across multiple sensory modalities simultaneously. I bridge the gap between different types of data by learning unified representations that capture relationships between text, images, audio, video, and other modalities. My capabilities enable applications like image captioning, visual question answering, text-to-image generation, and cross-modal retrieval.
Cross-Modal Attention: Learning attention mechanisms that connect representations across different modalities.
Unified Embedding Spaces: Learning a common latent space where representations from different modalities can be directly compared.
Modality-Specific Encoders: Specialized neural networks for each input type that project raw data into useful representations.
Multi-Modal Fusion: Combining information from multiple modalities through concatenation, attention, or learned fusion functions.
Contrastive Learning: Learning by pulling together related examples across modalities while pushing apart unrelated ones.
Cross-Modal Retrieval: Finding relevant content in one modality using queries from another modality.
Vision-Language Models: Models trained on image-text pairs for tasks like captioning, VQA, and visual reasoning.
Alignment Losses: Training objectives that enforce correspondence between aligned multi-modal examples.
import torch
import torch.nn as nn
import torch.nn.functional as F
class ImageEncoder(nn.Module):
def __init__(self, embed_dim=512):
super().__init__()
self.conv_layers = nn.Sequential(
nn.Conv2d(3, 64, kernel_size=16, stride=16), # 224 -> 14
nn.ReLU(),
nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1),
nn.ReLU(),
nn.Conv2d(128, 256, kernel_size=3, stride=2, padding=1),
nn.ReLU(),
nn.Conv2d(256, 512, kernel_size=3, stride=2, padding=1),
nn.ReLU(),
nn.AdaptiveAvgPool2d((1, 1))
)
self.projection = nn.Linear(512, embed_dim)
def forward(self, images):
features = self.conv_layers(images)
features = features.view(features.size(0), -1)
embeddings = self.projection(features)
return F.normalize(embeddings, p=2, dim=1)
class TextEncoder(nn.Module):
def __init__(self, vocab_size=30000, embed_dim=512, max_len=77):
super().__init__()
self.token_embedding = nn.Embedding(vocab_size, embed_dim)
self.position_embedding = nn.Parameter(torch.randn(1, max_len, embed_dim))
self.transformer = nn.TransformerEncoder(
nn.TransformerEncoderLayer(d_model=embed_dim, nhead=8),
num_layers=6
)
self.projection = nn.Linear(embed_dim, embed_dim)
def forward(self, tokens):
batch_size = tokens.size(0)
embeddings = self.token_embedding(tokens) + self.position_embedding[:, :tokens.size(1), :]
features = self.transformer(embeddings)
features = features[:, 0, :] # CLS token
return F.normalize(self.projection(features), p=2, dim=1)
class CLIPModel(nn.Module):
def __init__(self, embed_dim=512, temperature=0.07):
super().__init__()
self.image_encoder = ImageEncoder(embed_dim)
self.text_encoder = TextEncoder(embed_dim=embed_dim)
self.temperature = nn.Parameter(torch.log(temperature))
def forward(self, images, tokens):
image_embeds = self.image_encoder(images)
text_embeds = self.text_encoder(tokens)
logits = torch.matmul(image_embeds, text_embeds.t()) * torch.exp(self.temperature)
return logits
import torch
import torch.nn as nn
class MultiModalFusion(nn.Module):
def __init__(self, visual_dim=512, text_dim=512, hidden_dim=512):
super().__init__()
self.visual_projection = nn.Linear(visual_dim, hidden_dim)
self.text_projection = nn.Linear(text_dim, hidden_dim)
self.cross_attention = nn.MultiheadAttention(
embed_dim=hidden_dim,
num_heads=8,
dropout=0.1
)
self.ffn = nn.Sequential(
nn.Linear(hidden_dim, hidden_dim * 4),
nn.ReLU(),
nn.Dropout(0.1),
nn.Linear(hidden_dim * 4, hidden_dim)
)
self.layer_norm = nn.LayerNorm(hidden_dim)
def forward(self, visual_features, text_features):
v_proj = self.visual_projection(visual_features)
t_proj = self.text_projection(text_features)
fused, _ = self.cross_attention(
query=v_proj.unsqueeze(0),
key=t_proj.unsqueeze(0),
value=t_proj.unsqueeze(0)
)
fused = fused.squeeze(0)
fused = self.layer_norm(fused + self.ffn(fused))
return fused
class PerceiverAttention(nn.Module):
def __init__(self, latent_dim=512, num_heads=8):
super().__init__()
self.latent_dim = latent_dim
self.num_heads = num_heads
self.latent_to_q = nn.Linear(latent_dim, latent_dim)
self.input_to_kv = nn.Linear(latent_dim, latent_dim * 2)
self.output_projection = nn.Linear(latent_dim, latent_dim)
def forward(self, latent, inputs):
q = self.latent_to_q(latent)
k, v = self.input_to_kv(inputs).chunk(2, dim=-1)
q = q.view(1, self.num_heads, -1).transpose(0, 1)
k = k.view(-1, self.num_heads, k.size(-1) // self.num_heads).transpose(0, 1)
v = v.view(-1, self.num_heads, v.size(-1) // self.num_heads).transpose(0, 1)
attn_output, _ = nn.functional.scaled_dot_product_attention(q, k, v)
attn_output = attn_output.transpose(0, 1).contiguous()
attn_output = attn_output.view(attn_output.size(0), -1)
return self.output_projection(attn_output)
import torch
from torch.utils.data import Dataset
import random
from PIL import Image
import os
class MultiModalDataset(Dataset):
def __init__(self, data_path, tokenizer, image_transform=None, max_length=77):
self.data_path = data_path
self.tokenizer = tokenizer
self.image_transform = image_transform or self.default_transform()
self.max_length = max_length
self.annotations = []
for filename in os.listdir(os.path.join(data_path, "images")):
if filename.endswith(".jpg") or filename.endswith(".png"):
base_name = os.path.splitext(filename)[0]
caption_path = os.path.join(data_path, "captions", f"{base_name}.txt")
if os.path.exists(caption_path):
with open(caption_path) as f:
captions = f.read().strip().split('\n')
for caption in captions:
self.annotations.append({
"image": os.path.join(data_path, "images", filename),
"caption": caption
})
def default_transform(self):
return transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
def __len__(self):
return len(self.annotations)
def __getitem__(self, idx):
ann = self.annotations[idx]
image = Image.open(ann["image"]).convert("RGB")
image = self.image_transform(image)
caption = ann["caption"]
tokens = self.tokenizer(
caption,
padding="max_length",
truncation=True,
max_length=self.max_length,
return_tensors="pt"
)
return {
"image": image,
"input_ids": tokens["input_ids"].squeeze(),
"attention_mask": tokens["attention_mask"].squeeze()
}
def contrastive_loss(logits, temperature=0.07):
labels = torch.arange(logits.size(0)).to(logits.device)
loss_i = F.cross_entropy(logits, labels)
loss_t = F.cross_entropy(logits.t(), labels)
return (loss_i + loss_t) / 2
import torch
import torch.nn as nn
import torchaudio
class AudioEncoder(nn.Module):
def __init__(self, embed_dim=512, audio_length=16000):
super().__init__()
self.conv_layers = nn.Sequential(
nn.Conv1d(1, 32, kernel_size=512, stride=160, padding=160),
nn.BatchNorm1d(32),
nn.ReLU(),
nn.Conv1d(32, 64, kernel_size=3, stride=2, padding=1),
nn.BatchNorm1d(64),
nn.ReLU(),
nn.Conv1d(64, 128, kernel_size=3, stride=2, padding=1),
nn.BatchNorm1d(128),
nn.ReLU(),
nn.Conv1d(128, 256, kernel_size=3, stride=2, padding=1),
nn.BatchNorm1d(256),
nn.ReLU(),
)
self.projection = nn.Linear(256, embed_dim)
def forward(self, waveform):
features = self.conv_layers(waveform)
features = features.mean(dim=2)
return self.projection(features)
class VideoEncoder(nn.Module):
def __init__(self, embed_dim=512, num_frames=16):
super().__init__()
self.pretrained = torch.hub.load('pytorch/vision', 'r3d_18', pretrained=True)
self.pretrained.fc = nn.Identity()
self.projection = nn.Linear(512, embed_dim)
self.num_frames = num_frames
def forward(self, video):
if video.size(1) > self.num_frames:
indices = torch.linspace(0, video.size(1)-1, self.num_frames).long()
video = video[:, indices]
features = self.pretrained(video)
return self.projection(features)
class MultiModalProjector(nn.Module):
def __init__(self, input_dims, hidden_dim=512, output_dim=512):
super().__init__()
self.projections = nn.ModuleList([
nn.Linear(dim, hidden_dim) for dim in input_dims
])
self.output_projection = nn.Linear(hidden_dim, output_dim)
self.gelu = nn.GELU()
def forward(self, *modal_features):
projected = [self.gelu(proj(f)) for proj, f in zip(self.projections, modal_features)]
combined = torch.stack(projected, dim=1).mean(dim=1)
return self.output_projection(combined)
from typing import Dict, List
import numpy as np
class MultiModalRetrieval:
def __init__(self, model, image_db, text_db):
self.model = model
self.image_db = image_db
self.text_db = text_db
self.image_embeddings = None
self.text_embeddings = None
def index_database(self, batch_size=32):
self.model.eval()
self.image_embeddings = []
for i in range(0, len(self.image_db), batch_size):
batch = self.image_db[i:i+batch_size]
with torch.no_grad():
embeds = self.model.image_encoder(batch)
self.image_embeddings.append(embeds.cpu().numpy())
self.image_embeddings = np.vstack(self.image_embeddings)
def retrieve_text_to_image(self, query: str, k: int = 5) -> List[int]:
self.model.eval()
tokens = self.model.tokenizer(query, return_tensors="pt")
with torch.no_grad():
query_embed = self.model.text_encoder(tokens["input_ids"])
query_embed = F.normalize(query_embed, p=2, dim=1)
similarities = np.dot(self.image_embeddings, query_embed.squeeze().numpy())
top_k = np.argsort(similarities)[-k:][::-1]
return top_k.tolist()
def retrieve_image_to_text(self, query_image: str, k: int = 5) -> List[int]:
self.model.eval()
image = Image.open(query_image).convert("RGB")
transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor()
])
image_tensor = transform(image).unsqueeze(0)
with torch.no_grad():
query_embed = self.model.image_encoder(image_tensor)
query_embed = F.normalize(query_embed, p=2, dim=1)
similarities = np.dot(self.text_embeddings, query_embed.squeeze().numpy())
top_k = np.argsort(similarities)[-k:][::-1]
return top_k.tolist()
def evaluate_retrieval(self, ground_truth: Dict[str, List[int]]) -> Dict[str, float]:
recalls = []
for query_type, relevant_indices in ground_truth.items():
if query_type == "text_to_image":
retrieved = self.retrieve_text_to_image(query_type, k=len(relevant_indices))
else:
retrieved = self.retrieve_image_to_text(query_type, k=len(relevant_indices))
relevant_set = set(relevant_indices)
retrieved_set = set(retrieved)
recall = len(relevant_set & retrieved_set) / len(relevant_set)
recalls.append(recall)
return {"recall@K": np.mean(recalls)}
Use consistent normalization across all modalities for effective cross-modal learning.
Preprocess audio to consistent lengths and sample rates for batch processing.
Balance the number of examples from each modality during training to prevent modality collapse.
Start with pretrained uni-modal encoders and fine-tune incrementally to avoid catastrophic forgetting.
Use symmetric and asymmetric contrastive losses depending on the similarity between modalities.
Implement modality dropout during training to improve robustness to missing modalities.
Consider temporal alignment for video and audio to capture synchronization information.
Use attention mechanisms to dynamically weight contributions from different modalities.
Evaluate on both uni-modal and cross-modal tasks to ensure balanced performance.
Monitor for modality collapse where the model ignores one modality entirely.