القائمة
Ensemble predictions from heterogeneous vision-language models by blending their output embeddings with fixed scalar weights in embedding space
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استنادا إلى تصنيف SOC المهني
Memory-efficient Keras generator that streams sharded CSV files in chunks, renders strokes to images on-the-fly, and yields batches for training on datasets too large for memory
Stack 1D convolutions for local feature extraction before bidirectional LSTMs to classify variable-length stroke sequences into hundreds of doodle categories
Partition a large dataset into N balanced shards using integer key modulo arithmetic for reproducible, class-interleaved splits across CSV files
Normalize raw stroke coordinates to 0-255 range, resample at uniform arc-length spacing, then apply Ramer-Douglas-Peucker simplification
Render stroke sequences to grayscale images with temporal intensity encoding where earlier strokes are brighter and later strokes fade to encode drawing order
Score each face in a video as anomalous by computing L2 distance from the embedding centroid of all faces, then convert to probability via logistic function
| name | cv-weighted-embedding-blending |
| description | Ensemble predictions from heterogeneous vision-language models by blending their output embeddings with fixed scalar weights in embedding space |
When multiple models produce embeddings for the same input (e.g., CLIP k-NN, BLIP interrogator, fine-tuned ViT), blending their output embeddings with scalar weights is a simple yet effective ensemble. Unlike logit averaging, embedding blending works in the target space directly — each model's contribution is weighted and summed before submission.
import numpy as np
# Embeddings from three different models (shape: [n_samples, embed_dim])
emb_knn = predict_knn(images) # CLIP k-NN regression
emb_blip = predict_blip(images) # BLIP/CLIP interrogator + SentenceTransformer
emb_vit = predict_vit(images) # Fine-tuned ViT regression head
w_knn, w_blip, w_vit = 0.60, 0.15, 0.25
blended = w_knn * emb_knn + w_blip * emb_blip + w_vit * emb_vit