Accelerated Decoding of Centroid Positional Encoding for Instance Segmentation

• URL: http://arxiv.org/abs/2609.16874v1
• Date: Tue, 15 Sep 2026 09:05:17 GMT
• Title: Accelerated Decoding of Centroid Positional Encoding for Instance Segmentation
• Abstract summary: We investigate the decoding overhead associated with a centroid encoding for Instance, in which each pixel regresses a positional embedding of its instance centroid.<n>Our solution significantly reduces decoding overhead and improves Endto-End latency, outperforming both CPU-based approaches and naive GPU implementations.
• Score: 49.97373153077311
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Beyond model inference, the decoding stage, which converts raw network outputs into task-level representations, constitutes a significant portion of the execution cost. Despite its practical impact, prediction decoding has received comparatively little attention and is often implemented using generic CPU routines or inefficient GPU kernels, limiting the benefits of advances in model efficiency. In this work, we investigate the decoding overhead associated with a recent sinusoidal centroid encoding for Instance Segmentation, in which each pixel regresses a positional embedding of its instance centroid. This approach allows flexible segmentation without predefined proposals, but extracting instance masks from dense embeddings incurs a high computational cost. We present an optimized CUDA-based implementation of the decoding algorithm tailored to this encoding, explicitly addressing challenges related to parallelization, synchronization, and memory access on modern GPUs. Our solution significantly reduces decoding overhead and improves End-to-End inference latency, outperforming both CPU-based approaches and naive GPU implementations. The results demonstrate that efficient decoding is essential to fully exploit the advantages of advanced output representations and highlight the importance of jointly designing encoding schemes and their decoding algorithms for real-time computer vision systems.

Related papers

• Even More Efficient Soft-Output Decoding with Extra-Cluster Growth and Early Stopping [2.370310454459195]
  We introduce two types of novel soft-outputs: the bounded cluster gap and the extra-cluster gap.<n>The latter, the extra-cluster gap, quantifies decoder reliability by performing a small, additional growth of the clusters obtained by the decoder.<n>These techniques offer lower computational complexity and higher hardware compatibility.
  arXiv  Detail & Related papers  (2026-02-03T10:00:40Z)
• Fast SAM2 with Text-Driven Token Pruning [52.8350457627401]
  Segment Anything Model 2 (SAM2), a vision computation model has significantly advanced in prompt-driven video object segmentation.<n>SAM2 pipelines propagate all visual tokens produced by the image encoder through downstream temporal reasoning modules, regardless of their relevance to the target object.<n>We introduce a text-guided token pruning framework that improves inference efficiency by selectively reducing token density prior to temporal propagation.
  arXiv  Detail & Related papers  (2025-12-24T18:59:05Z)
• Fast correlated decoding of transversal logical algorithms [67.01652927671279]
  Quantum error correction (QEC) is required for large-scale computation, but incurs a significant resource overhead.<n>Recent advances have shown that by jointly decoding logical qubits in algorithms composed of logical gates, the number of syndrome extraction rounds can be reduced.<n>Here, we reform the problem of decoding circuits by directly decoding relevant logical operator products as they propagate through the circuit.
  arXiv  Detail & Related papers  (2025-05-19T18:00:00Z)
• Accelerating Error Correction Code Transformers [56.75773430667148]
  We introduce a novel acceleration method for transformer-based decoders. We achieve a 90% compression ratio and reduce arithmetic operation energy consumption by at least 224 times on modern hardware.
  arXiv  Detail & Related papers  (2024-10-08T11:07:55Z)
• Cascade Reward Sampling for Efficient Decoding-Time Alignment [17.278488115500615]
  We introduce Cascade Reward Sampling (CARDS) to resolve both efficiency in decoding-time alignment.<n>CARDS minimizes redundant computations of both large language models (LLMs) and reward models (RMs)
  arXiv  Detail & Related papers  (2024-06-24T04:08:35Z)
• Efficient Encoder-Decoder Transformer Decoding for Decomposable Tasks [53.550782959908524]
  We introduce a new configuration for encoder-decoder models that improves efficiency on structured output and decomposable tasks. Our method, prompt-in-decoder (PiD), encodes the input once and decodes the output in parallel, boosting both training and inference efficiency.
  arXiv  Detail & Related papers  (2024-03-19T19:27:23Z)
• Bioinspired Cortex-based Fast Codebook Generation [0.09449650062296822]
  We introduce a feature extraction method inspired by sensory cortical networks in the brain. Dubbed as bioinspired cortex, the algorithm provides convergence to features from streaming signals with superior computational efficiency. We show herein the superior performance of the cortex model in clustering and vector quantization.
  arXiv  Detail & Related papers  (2022-01-28T18:37:43Z)
• EfficientFCN: Holistically-guided Decoding for Semantic Segmentation [49.27021844132522]
  State-of-the-art semantic segmentation algorithms are mostly based on dilated Fully Convolutional Networks (dilatedFCN) We propose the EfficientFCN, whose backbone is a common ImageNet pre-trained network without any dilated convolution. Such a framework achieves comparable or even better performance than state-of-the-art methods with only 1/3 of the computational cost.
  arXiv  Detail & Related papers  (2020-08-24T14:48:23Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.