event2vec-neuromorphic-representation

name	event2vec-neuromorphic-representation
description	Event2Vec: Processing neuromorphic events directly via vector representations for efficient event camera data processing compatible with Transformer architectures. Activation triggers: event camera, neuromorphic vision, event2vec, DVS, asynchronous events, sparse events, event-based vision.

Event2Vec: Processing Neuromorphic Events Directly by Representations in Vector Space

Novel representation enabling direct processing of asynchronous, sparse neuromorphic event data in vector space, fully compatible with Transformer architectures while maintaining event camera advantages of temporal resolution, power efficiency, and dynamic range.

Metadata

• Source: arXiv:2504.15371 [cs.CV]
• Authors: Wei Fang, Priyadarshini Panda
• Published: 2025-04-21 (v5: 2026-02-05)
• Categories: cs.CV (Computer Vision), cs.NE (Neural and Evolutionary Computing)
• Code: Available at https URL

Core Methodology

Key Innovation

Neuromorphic event cameras produce asynchronous, sparse event streams that are incompatible with standard deep learning pipelines. Event2Vec addresses this by:

• Word-to-Event Analogy: Drawing inspiration from word embeddings (Word2Vec)
• Vector Representation: Direct encoding of events in continuous vector space
• Transformer Compatibility: Seamless integration with standard Transformer architectures
• Preserved Sparsity: Maintains event advantages without conversion to dense frames

Technical Framework

1. Event Representation

Event cameras output asynchronous events: $$e = (x, y, t, p)$$

Where:

• $(x, y)$: Pixel location
• $t$: Timestamp
• $p$: Polarity (+1 for ON event, -1 for OFF event)

2. Event2Vec Embedding

Inspired by Word2Vec, events are mapped to a vector space:

• Event Embeddings: Each event type encoded as vector
• Positional Embeddings: Spatial and temporal location information
• Contextual Embeddings: Event relationships via attention

3. Event Stream Encoding

Raw Events → Event2Vec Embedding → Transformer → Task Output
    ↓              ↓                  ↓            ↓
  Sparse      Continuous Vectors    Attention      Classification/
Asynchronous   Maintains           Processing     Regression

4. Key Advantages

• Parameter Efficiency: Dramatically fewer parameters than frame-based
• High Throughput: Parallel processing of sparse events
• Low Latency: Direct event processing, no accumulation window
• Scalability: Effective at ultra-low spatial resolutions

Implementation Guide

Prerequisites

• Python 3.8+
• PyTorch 2.0+
• Event data library (e.g., tonic, Tonic)
• NumPy, Matplotlib
• Optional: CUDA for GPU acceleration

Step-by-Step Implementation

import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import List, Tuple, Optional
import numpy as np

class Event2Vec(nn.Module):
    """
    Event2Vec: Direct event representation in vector space
    """
    def __init__(
        self,
        embedding_dim: int = 128,
        num_polarities: int = 2,  # ON and OFF events
        spatial_bins: Tuple[int, int] = (128, 128),  # H, W
        temporal_resolution: float = 1e-3,  # 1ms
        max_time_window: float = 0.1,  # 100ms
        use_positional_encoding: bool = True,
        num_heads: int = 8,
        num_layers: int = 6,
        dropout: float = 0.1
    ):
        super().__init__()
        
        self.embedding_dim = embedding_dim
        self.num_polarities = num_polarities
        self.spatial_bins = spatial_bins
        self.temporal_resolution = temporal_resolution
        .max_time_window = max_time_window
        
        
        .polarity_embedding = nn.Embedding(num_polarities, embedding_dim)
        
        
        .spatial_embedding = nn.Parameter(
            torch.randn(spatial_bins[], spatial_bins[], embedding_dim // )
        )
        
        
         use_positional_encoding:
            .temporal_embedding = SinusoidalPositionalEncoding(
                embedding_dim // ,
                max_len=(max_time_window / temporal_resolution)
            )
        :
            .temporal_embedding = nn.Embedding(
                (max_time_window / temporal_resolution),
                embedding_dim // 
            )
        
        
        .event_proj = nn.Linear(embedding_dim * , embedding_dim)
        
        
        encoder_layer = nn.TransformerEncoderLayer(
            d_model=embedding_dim,
            nhead=num_heads,
            dim_feedforward=embedding_dim * ,
            dropout=dropout,
            batch_first=
        )
        .transformer = nn.TransformerEncoder(encoder_layer, num_layers)
        
        
        .output_head = nn.Identity()  
        
     () -> torch.Tensor:
        
         spatial_size  :
            spatial_size = .spatial_bins
        
        x, y, t, p = events[:, ], events[:, ], events[:, ], events[:, ]
        
        
        x_idx = (x * .spatial_bins[] / spatial_size[]).long().clamp(, .spatial_bins[] - )
        y_idx = (y * .spatial_bins[] / spatial_size[]).long().clamp(, .spatial_bins[] - )
        
        
        p_idx = ((p + ) / ).long()
        
        
        t_idx = (t / .temporal_resolution).long().clamp(
            , (.max_time_window / .temporal_resolution) - 
        )
        
        
        polarity_emb = .polarity_embedding(p_idx)  
        
        spatial_emb = .spatial_embedding[y_idx, x_idx]  
        
         (.temporal_embedding, ):
            temporal_emb = .temporal_embedding(t_idx)  
        :
            temporal_emb = .temporal_embedding(t_idx)
        
        
        spatiotemporal_emb = torch.cat([spatial_emb, temporal_emb], dim=-)
        
        
        combined = torch.cat([polarity_emb, spatiotemporal_emb], dim=-)
        
        
        embeddings = .event_proj(combined)
        
         embeddings
    
     () -> torch.Tensor:
        
        
        event_embeddings = .encode_events(events, spatial_size)
        
        
        
        embeddings = event_embeddings.unsqueeze()
        
        
        encoded = .transformer(embeddings)
        
        
        output = .output_head(encoded)
        
         output


 (nn.Module):
    
     ():
        ().__init__()
        
        pe = torch.zeros(max_len, d_model)
        position = torch.arange(, max_len, dtype=torch.).unsqueeze()
        
        div_term = torch.exp(
            torch.arange(, d_model, ).() *
            (-np.log() / d_model)
        )
        
        pe[:, ::] = torch.sin(position * div_term)
        pe[:, ::] = torch.cos(position * div_term)
        
        .register_buffer(, pe)
    
     () -> torch.Tensor:
        
         .pe[positions]


 (nn.Module):
    
     ():
        ().__init__()
        
        .event2vec = Event2Vec(
            embedding_dim=embedding_dim,
            **event2vec_kwargs
        )
        
        
        .classifier = nn.Sequential(
            nn.LayerNorm(embedding_dim),
            nn.Linear(embedding_dim, embedding_dim // ),
            nn.GELU(),
            nn.Dropout(),
            nn.Linear(embedding_dim // , num_classes)
        )
    
     () -> torch.Tensor:
        
        
        encoded = .event2vec(events, spatial_size)
        
        
        pooled = encoded.mean(dim=)  
        
        
        logits = .classifier(pooled.squeeze())
        
         logits

Handling Event Streams Efficiently

def preprocess_event_stream(
    events: np.ndarray,
    time_window: float = 0.1,  # 100ms
    spatial_crop: Optional[Tuple[int, int, int, int]] = None
) -> torch.Tensor:
    """
    Preprocess raw event stream for Event2Vec
    
    Args:
        events: [N, 4] raw events (x, y, t, p)
        time_window: Time window for processing
        spatial_crop: (x_min, x_max, y_min, y_max) crop region
    
    Returns:
        processed: [M, 4] processed events
    """
    # Filter by time
    t_start = events[:, 2].min()
    mask = (events[:, 2] - t_start) < time_window
    events = events[mask]
    
    # Spatial crop if specified
    if spatial_crop:
        x_min, x_max, y_min, y_max = spatial_crop
        mask = (
            (events[:, 0] >= x_min) & (events[:, 0] < x_max) &
            (events[:, 1] >= y_min) & (events[:, 1] < y_max)
        )
        events = events[mask]
        
        # Normalize coordinates
        events[:, 0] -= x_min
        events[:, 1] -= y_min
    
    # Normalize timestamps
    events[:, 2] -= events[:, 2].min()
    
    return torch.from_numpy(events).float()


class EventBatcher:
    """
    Batch sparse events for efficient processing
    """
    def __init__(self, max_events_per_sample: int = ):
        .max_events = max_events_per_sample
    
     () -> [torch.Tensor, torch.Tensor]:
        
        batched = []
        batch_indices = []
        
         batch_idx, events  (event_list):
            
             (events) > .max_events:
                events = events[:.max_events]
            
            batched.append(events)
            batch_indices.extend([batch_idx] * (events))
        
        batched_events = torch.cat(batched, dim=)
        batch_indices = torch.tensor(batch_indices, dtype=torch.long)
        
         batched_events, batch_indices

Training on Event Datasets

import tonic
import tonic.transforms as transforms
from torch.utils.data import DataLoader

def get_dvs_gesture_loader(
    batch_size: int = 32,
    data_path: str = './data'
):
    """
    Create DataLoader for DVS Gesture dataset
    """
    # Event2Vec transform
    transform = transforms.Compose([
        transforms.ToTensor(),
        transforms.DropEvent(p=0.1),  # Random dropout for robustness
    ])
    
    train_dataset = tonic.datasets.DVSGesture(
        save_to=data_path,
        train=True,
        transform=transform
    )
    
    test_dataset = tonic.datasets.DVSGesture(
        save_to=data_path,
        train=False,
        transform=transforms.ToTensor()
    )
    
    # Custom collate for variable-length events
    def collate_fn(batch):
        events_list = [item[0] for item in batch]
        labels = torch.tensor([item[1] for item in batch])
        
        # Batch events
        batcher = EventBatcher()
        batched_events, batch_indices = batcher.collate_events(events_list)
        
        return batched_events, batch_indices, labels
    
    train_loader = DataLoader(
        train_dataset,
        batch_size=batch_size,
        shuffle=True,
        collate_fn=collate_fn,
        num_workers=4
    )
    
    return train_loader, test_dataset


def train_event2vec(
    model: Event2VecClassifier,
    train_loader: DataLoader,
    num_epochs: int = ,
    device:  = 
):
    
    model = model.to(device)
    optimizer = torch.optim.AdamW(model.parameters(), lr=)
    scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, num_epochs)
    criterion = nn.CrossEntropyLoss()
    
     epoch  (num_epochs):
        model.train()
        total_loss = 
        correct = 
        total = 
        
         batched_events, batch_indices, labels  train_loader:
            batched_events = batched_events.to(device)
            labels = labels.to(device)
            
            optimizer.zero_grad()
            
            
            logits_list = []
             i  ((labels)):
                mask = batch_indices == i
                sample_events = batched_events[mask]
                logits = model(sample_events)
                logits_list.append(logits)
            
            logits = torch.stack(logits_list)
            loss = criterion(logits, labels)
            
            loss.backward()
            optimizer.step()
            
            total_loss += loss.item()
            correct += (logits.argmax(dim=) == labels).().item()
            total += (labels)
        
        scheduler.step()
        
        acc =  * correct / total
        ()

Applications

1. Gesture Recognition

• DVS Gesture: 11-class hand gesture dataset
• ASL-DVS: American Sign Language alphabet
• DVS Lip: Lip reading from events
• Advantages: Low latency, motion blur immunity

2. Autonomous Navigation

• Obstacle Detection: Fast response to motion
• High Dynamic Range: Works in extreme lighting
• Low Power: Suitable for drones, robots
• Event Cameras: DAVIS, ATIS, Prophesee

3. Surveillance

• Motion Detection: Event-triggered recording
• Privacy-Preserving: No full-frame capture
• 24/7 Operation: Low power always-on
• Anomaly Detection: Sparse event analysis

4. Robotics

• SLAM: Event-based simultaneous localization
• Visual Servoing: High-speed tracking
• Collision Avoidance: Sub-millisecond latency
• Industrial Inspection: High-speed quality control

Pitfalls

1. Spatial Quantization: Grid-based encoding loses sub-pixel precision

   • Mitigation: Learned continuous spatial embeddings, attention mechanisms

2. Temporal Alignment: Events from different sources may need synchronization

   • Mitigation: Temporal normalization, learned time warping

3. Variable Event Count: Different samples have different numbers of events

   • Mitigation: Set-based processing, attention pooling, truncation

4. Static Scenes: No events in static regions

   • Mitigation: Periodic frame integration, hybrid approaches

5. Dataset Specificity: Hyperparameters tuned per dataset

   • Mitigation: Meta-learning, domain adaptation

Related Skills

• snn-event-processing: Spiking neural networks for events
• neuromorphic-vision: General neuromorphic vision methods
• event-camera-denoising: Noise removal from event streams
• sparse-transformer: Efficient attention for sparse data

References

@article{fang2025event2vec,
  title={Event2Vec: Processing Neuromorphic Events Directly by Representations in Vector Space},
  author={Fang, Wei and Panda, Priyadarshini},
  journal={arXiv preprint arXiv:2504.15371},
  year={2025}
}

Further Reading

• Event Cameras: Gallego et al., "Event-based Vision: A Survey"
• Word2Vec: Mikolov et al., "Efficient Estimation of Word Representations"
• Transformer: Vaswani et al., "Attention is All You Need"
• Neuromorphic Vision: Davies et al., "Advancing Neuromorphic Computing"