Foundation Models, World Models, and the Future of Vision

Module: Computer Vision | Difficulty: Premium

Scaling Laws for Vision

where is data, is compute.

World Model

where is state, is action, is reward.

Multimodal Chain-of-Thought

import torch
import torch.nn as nn

class VisionLanguageModel(nn.Module):
    def __init__(self, vision_encoder, language_model,
                 projection_dim=4096):
        super().__init__()
        self.vision_encoder = vision_encoder
        self.language_model = language_model
        vision_dim = vision_encoder.embed_dim
        language_dim = language_model.embed_dim
        self.projection = nn.Sequential(
            nn.Linear(vision_dim, projection_dim),
            nn.GELU(),
            nn.Linear(projection_dim, language_dim),
        )

    def encode_image(self, images):
        vision_features = self.vision_encoder(images)
        projected = self.projection(vision_features)
        return projected

    def forward(self, images, text_tokens):
        image_embeds = self.encode_image(images)
        text_embeds = self.language_model.embed_tokens(text_tokens)
        combined = torch.cat([image_embeds, text_embeds], dim=1)
        return self.language_model(inputs_embeds=combined)

class WorldModel(nn.Module):
    def __init__(self, state_dim, action_dim, hidden_dim=512):
        super().__init__()
        self.dynamics = nn.Sequential(
            nn.Linear(state_dim + action_dim, hidden_dim),
            nn.ReLU(inplace=True),
            nn.Linear(hidden_dim, hidden_dim),
            nn.ReLU(inplace=True),
            nn.Linear(hidden_dim, state_dim),
        )
        self.reward_head = nn.Sequential(
            nn.Linear(state_dim + action_dim, hidden_dim),
            nn.ReLU(inplace=True),
            nn.Linear(hidden_dim, 1),
        )

    def forward(self, state, action):
        sa = torch.cat([state, action], dim=-1)
        next_state = self.dynamics(sa)
        reward = self.reward_head(sa)
        return next_state, reward

    def imagine_trajectory(self, initial_state, policy, horizon):
        states = [initial_state]
        rewards = []
        state = initial_state
        for _ in range(horizon):
            action = policy(state)
            next_state, reward = self.forward(state, action)
            states.append(next_state)
            rewards.append(reward)
            state = next_state
        return states, rewards

Research Insight: The future of computer vision is being shaped by three converging trends: (1) Foundation models that unify vision, language, and reasoning into single systems (GPT-4V, Gemini); (2) World models that learn predictive models of the physical world for planning and simulation; (3) Embodied AI that connects perception to action through robotic interaction. The key open questions are: How do we efficiently ground language models in visual experience? Can world models scale to handle the full complexity of the real world? How do we build agents that learn continuously from interaction? The answer likely involves combining large-scale pretraining with interactive learning, creating systems that can both perceive and act in the world.