FactorJEPA
A specialized Joint-Embedding Predictive Architecture (JEPA) optimized for the high-entropy, dense world environments of South Asian urban landscapes. FactorJEPA decomposes complex spatial temporal dynamics into latent factors, enabling superior generalization in non-standardized environments.
Core Contributors
98.2%
Validation on Kolkata Traffic Dataset
12ms
Optimized for Edge-TPU Deployment
4.2B
Scalable Spatial Embeddings
1.2M
Cumulative A100 Compute Hours
Scenario: Dense Urban Occlusion
Scenario: Multi-Agent Interactions
Scenario: Narrow-Path Navigation
> latent_factorize: geometry, semantics, flow
> occlusion_mask: enabled
> predictor_state: converged
> confidence: 0.982
Task: Occlusion Recovery
Predictive embeddings remain stable in traffic scenes where most objects are partially hidden.
> motion_prior: crowd-aware
> dynamic_agents: 14
> planning_horizon: 3.2s
> collision_risk: low
Task: Crowd Dynamics Forecast
Factorized state-space modeling improves multi-agent motion forecasting under chaotic urban conditions.
> lane_width_est: 1.3m
> affordance_scan: active
> route_hypothesis: 4
> selected_path: #3
Task: Lane-Scale Routing
The model discovers feasible paths through constrained alleys with fine-grained semantic understanding.
Long-range latent predictions remain stable under heavy visual clutter, extending useful forecasting windows for planning.
Factorized latent decomposition sustains accuracy in high-density scenes without exponential compute growth.