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ARXIV:2603.06228 · EVENT-BASED OBJECT DETECTION · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.06228EVENT-BASED OBJECT DETECTIONSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
Accelerate event-based object detection with spatially-sparse linear attention, achieving state-of-the-art accuracy and reduced per-event computation.
Opportunity summary
Pain Accelerate event-based object detection with spatially-sparse linear attention, achieving state-of-the-art accuracy and reduced per-event computation.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
Accelerate event-based object detection with spatially-sparse linear attention, achieving state-of-the-art accuracy and reduced per-event computation. Existing asynchronous event-based neural networks realize this low-latency advantage by updating predictions event-by-event, but still suffer from two bottlenecks:…
Event cameras provide sequential visual data with spatial sparsity and high temporal resolution, making them attractive for low-latency object detection. Existing asynchronous event-based neural networks realize this low-latency advantage by updating predictions event-by-event, but…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Linear attention is appealing in this setting because it supports parallel training and recurrent inference.
Event-Based Object Detection moved forward this cycle; last verified April 2026. Public score 7.0/10.
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mobile layout uses overflow-hidden min-w-0 break-wordsOpportunity summary
Score7.0Public score shown from the verified overall while the stale axis breakdown refreshesAnalysis summary
Accelerate event-based object detection with spatially-sparse linear attention, achieving state-of-the-art accuracy and reduced per-event computation.
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10.48550/arXiv.2603.06228Accelerate event-based object detection with spatially-sparse linear attention, achieving state-of-the-art accuracy and reduced per-event computation.
Abstract
Event cameras provide sequential visual data with spatial sparsity and high temporal resolution, making them attractive for low-latency object detection. Existing asynchronous event-based neural networks realize this low-latency advantage by updating predictions event-by-event, but still suffer from two bottlenecks: recurrent architectures are difficult to train efficiently on long sequences, and improving accuracy often increases per-event computation and latency. Linear attention is appealing in this setting because it supports parallel training and recurrent inference. However, standard linear attention updates a global state for every event, yielding a poor accuracy-efficiency trade-off, which is problematic for object detection, where fine-grained representations and thus states are preferred. The key challenge is therefore to introduce sparse state activation that exploits event sparsity while preserving efficient parallel training. We propose Spatially-Sparse Linear Attention (SSLA), which introduces a mixture-of-spaces state decomposition and a scatter-compute-gather training procedure, enabling state-level sparsity as well as training parallelism. Built on SSLA, we develop an end-to-end asynchronous linear attention model, SSLA-Det, for event-based object detection. On Gen1 and N-Caltech101, SSLA-Det achieves state-of-the-art accuracy among asynchronous methods, reaching 0.375 mAP and 0.515 mAP, respectively, while reducing per-event computation by more than 20 times compared to the strongest prior asynchronous baseline, demonstrating the potential of linear attention for low-latency event-based vision.
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Viability
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Dimensions overall score 7.0
PROBLEM
Accelerate event-based object detection with spatially-sparse linear attention, achieving state-of-the-art accuracy and reduced per-event computation. Existing asynchronous event-based neural networks realize this low-latency advantage by updating predictions event-by-event, but...
METHOD
Event cameras provide sequential visual data with spatial sparsity and high temporal resolution, making them attractive for low-latency object detection. Existing asynchronous event-based neural networks realize this low-latency advantage by updating predictions event-by-event,...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Linear attention is appealing in this setting because it supports parallel training and recurrent inference.
WHY NOW
Event-Based Object Detection moved forward this cycle; last verified April 2026. Public score 7.0/10.
Abstract-backed public claims while anchored extraction refreshes.
Accelerate event-based object detection with spatially-sparse linear attention, achieving state-of-the-art accuracy and reduced per-event computation. Existing asynchronous event-based neural networks realize this low-latency advantage by updating predictions event-by-event, but still suffer from two bottlenecks: recurrent architectures are difficult to train efficiently on long sequences, and improving accuracy often increases per-event computation and latency.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Event cameras provide sequential visual data with spatial sparsity and high temporal resolution, making them attractive for low-latency object detection. Existing asynchronous event-based neural networks realize this low-latency advantage by updating predictions event-by-event, but still suffer from two bottlenecks: recurrent architectures are difficult to train efficiently on long sequences, and improving accuracy often increases per-event computation and latency.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Linear attention is appealing in this setting because it supports parallel training and recurrent inference.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Event-Based Object Detection moved forward this cycle; last verified April 2026. Public score 7.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Accelerate event-based object detection with spatially-sparse linear attention, achieving state-of-the-art accuracy and reduced per-event computation.
Segment
Event-Based Object Detection
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7.0/10 public viability
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