Interleaving Visual Objects for Better Alignment

# Illustrative sketch (not from the paper)
import torch
from torch import nn

# ----- placeholder modules -------------------------------------------------
def generate_caption(image):
    """Return a dummy caption string for the given image tensor."""
    return "a cat sitting on a sofa"

def extract_entities(caption):
    """Extract noun‑phrase entities from the caption (mock implementation)."""
    return ["cat", "sofa"]

def ground_entities(image, entities):
    """Use Grounding DINO to obtain region proposals for each entity (mock)."""
    # return list of (entity, region_tensor) tuples
    return [(e, torch.randn(1, 3, 224, 224)) for e in entities]

def filter_pairs(pairs):
    """Apply confidence, size, and SAM‑2.1 mask quality filters (mock)."""
    return pairs  # no actual filtering in this sketch

def get_visual_embedding(region):
    """Encode a visual region into a fixed‑dim embedding (mock)."""
    return torch.mean(region, dim=[1, 2, 3])  # placeholder

def interleave_sequence(tokens, pairs):
    """Replace entity tokens with their visual embeddings (mock)."""
    seq = []
    for tok in tokens:
        match = next((p for p in pairs if p[0] == tok), None)
        if match:
            seq.append(get_visual_embedding(match[1]))
        else:
            seq.append(tok)
    return seq

# ----- main workflow -------------------------------------------------------
image = torch.randn(3, 224, 224)               # dummy image tensor
caption = generate_caption(image)             # step 1
entities = extract_entities(caption)          # step 2
pairs = ground_entities(image, entities)      # step 3
pairs = filter_pairs(pairs)                   # step 4
tokens = caption.split()                     # simple tokenization
interleaved = interleave_sequence(tokens, pairs)  # step 5

# Simple language‑model head (placeholder dimensions)
lm_head = nn.Linear(768, len(interleaved))
lm_loss_fn = nn.CrossEntropyLoss()
recon_loss_fn = nn.MSELoss()

# Mock forward pass
logits = lm_head(torch.randn(len(interleaved), 768))
loss_lm = lm_loss_fn(logits, torch.randint(0, len(interleaved), (len(interleaved),)))
loss_recon = recon_loss_fn(torch.randn(len(pairs), 768), torch.randn(len(pairs), 768))

total_loss = loss_lm + loss_recon           # step 6
total_loss.backward()

// Illustrative sketch (not from the paper)
const torch = require('torch-js'); // placeholder import

// ----- placeholder functions ------------------------------------------------
function generateCaption(image) {
  // return a dummy caption string for the given image tensor
  return 'a dog playing with a ball';
}

function extractEntities(caption) {
  // mock noun‑phrase extraction
  return ['dog', 'ball'];
}

function groundEntities(image, entities) {
  // mock Grounding DINO output: array of {entity, regionTensor}
  return entities.map(e => ({ entity: e, region: torch.randn([1, 3, 224, 224]) }));
}

function filterPairs(pairs) {
  // mock filtering based on confidence/size/SAM‑2.1 masks
  return pairs;
}

function getVisualEmbedding(region) {
  // mock visual encoder: mean over spatial dims
  return torch.mean(region, [1, 2, 3]);
}

function interleaveSequence(tokens, pairs) {
  // replace matching entity tokens with visual embeddings
  return tokens.map(tok => {
    const match = pairs.find(p => p.entity === tok);
    return match ? getVisualEmbedding(match.region) : tok;
  });
}

// ----- main workflow -------------------------------------------------------
const image = torch.randn([3, 224, 224]); // dummy image tensor
const caption = generateCaption(image);   // step 1
const entities = extractEntities(caption); // step 2
let pairs = groundEntities(image, entities); // step 3
pairs = filterPairs(pairs);               // step 4
const tokens = caption.split(' ');
const interleaved = interleaveSequence(tokens, pairs); // step 5

// Simple language‑model head (mock dimensions)
const lmHead = new torch.nn.Linear(768, interleaved.length);
const lmLossFn = new torch.nn.CrossEntropyLoss();
const reconLossFn = new torch.nn.MSELoss();

// Mock forward pass
const logits = lmHead.forward(torch.randn([interleaved.length, 768]));
const target = torch.randint(0, interleaved.length, [interleaved.length]);
const lossLm = lmLossFn.forward(logits, target);
const lossRecon = reconLossFn.forward(
  torch.randn([pairs.length, 768]),
  torch.randn([pairs.length, 768])
);

const totalLoss = lossLm.add(lossRecon); // step 6
totalLoss.backward();