No Hard Negatives Required: Concept Centric Learning Leads to Compositionality without Degrading Zero-shot Capabilities of Contrastive Models

1. 1
   Identify Compositional Limitations

   Review your existing Vision-Language (V&L) models to pinpoint areas where they struggle with complex compositional tasks, such as understanding object attributes or relationships within a scene.
2. 2
   Investigate Concept Centric Learning (CCL) Implementations

   Research and identify available frameworks, libraries, or research papers that provide practical guidance or code for integrating Concept Centric Learning into V&L model training pipelines. Focus on methods that avoid hard negative mining.
3. 3
   Train or Fine-tune with CCL

   Apply a Concept Centric Learning-based training approach to your V&L models. This involves modifying the training objective or data sampling to emphasize concept-level understanding over simple pair-wise contrast.
4. 4
   Evaluate Compositional Performance

   Test the fine-tuned model on benchmarks specifically designed to assess compositional understanding, such as attribute binding, relation extraction, or complex visual question answering tasks. Measure improvement in these specific areas.
5. 5
   Verify Zero-Shot Generalization

   Crucially, evaluate the model's zero-shot performance on unseen datasets to confirm that the CCL approach has preserved or enhanced its ability to generalize without degradation, a key benefit of this method.

from transformers import CLIPProcessor, CLIPModel
from PIL import Image
import requests

# Load pre-trained CLIP model and processor
model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")

# Example image (a cat image)
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)

# Example text descriptions for zero-shot classification
texts = ["a photo of a cat", "a photo of a dog", "a photo of a couch"]

# Process inputs
inputs = processor(text=texts, images=image, return_tensors="pt", padding=True)

# Get model outputs
outputs = model(**inputs)
logits_per_image = outputs.logits_per_image # Image-text similarity scores
probs = logits_per_image.softmax(dim=1) # Convert to probabilities

print("Image-Text Similarity Probabilities:")
for i, text in enumerate(texts):
    print(f"  '{text}': {probs[0, i].item():.4f}")

# The output should show the highest probability for 'a photo of a cat', demonstrating
# zero-shot capability – a feature Concept Centric Learning aims to preserve.

• Paperarxiv.org