Lane detection (LD) plays a crucial role in enhancing the L2+ capabilities of
autonomous driving, capturing widespread attention. The Post-Processing
Quantization (PTQ) could facilitate the practical application of LD models,
enabling fast speeds and limited memories without labeled data. However, prior
PTQ methods do not consider the complex LD outputs that contain physical
semantics, such as offsets, locations, etc., and thus cannot be directly
applied to LD models. In this paper, we pioneeringly investigate semantic
sensitivity to post-processing for lane detection with a novel Lane Distortion
Score. Moreover, we identify two main factors impacting the LD performance
after quantization, namely intra-head sensitivity and inter-head sensitivity,
where a small quantization error in specific semantics can cause significant
lane distortion. Thus, we propose a Selective Focus framework deployed with
Semantic Guided Focus and Sensitivity Aware Selection modules, to incorporate
post-processing information into PTQ reconstruction. Based on the observed
intra-head sensitivity, Semantic Guided Focus is introduced to prioritize
foreground-related semantics using a practical proxy. For inter-head
sensitivity, we present Sensitivity Aware Selection, efficiently recognizing
influential prediction heads and refining the optimization objectives at
runtime. Extensive experiments have been done on a wide variety of models
including keypoint-, anchor-, curve-, and segmentation-based ones. Our method
produces quantized models in minutes on a single GPU and can achieve 6.4% F1
Score improvement on the CULane dataset.

通过引入感知失真评分以及选择性聚焦框架和敏感性感知选择模块，本文探究了车道检测中后处理的语义敏感性，并通过精简优化目标和优化重要预测结果的方式，结合后处理信息进行深度神经网络的量化重建，实现在短短几分钟内在单个 GPU 上生成压缩模型，并在 CULane 数据集上取得了 6.4% 的 F1 评分提升。

选择性聚焦：探究车道检测后训练量化中的语义敏感性

Selective Focus: Investigating Semantics Sensitivity in Post-training  Quantization for Lane Detection

Recent studies of the emergent capabilities of transformer-based Natural
Language Understanding (NLU) models have indicated that they have an
understanding of lexical and compositional semantics. We provide evidence that
suggests these claims should be taken with a grain of salt: we find that
state-of-the-art Natural Language Inference (NLI) models are sensitive towards
minor semantics preserving surface-form variations, which lead to sizable
inconsistent model decisions during inference. Notably, this behaviour differs
from valid and in-depth comprehension of compositional semantics, however does
neither emerge when evaluating model accuracy on standard benchmarks nor when
probing for syntactic, monotonic, and logically robust reasoning. We propose a
novel framework to measure the extent of semantic sensitivity. To this end, we
evaluate NLI models on adversarially generated examples containing minor
semantics-preserving surface-form input noise. This is achieved using
conditional text generation, with the explicit condition that the NLI model
predicts the relationship between the original and adversarial inputs as a
symmetric equivalence entailment. We systematically study the effects of the
phenomenon across NLI models for \emph{in-} and \emph{out-of} domain settings.
Our experiments show that semantic sensitivity causes performance degradations
of $12.92\%$ and $23.71\%$ average over \emph{in-} and \emph{out-of-} domain
settings, respectively. We further perform ablation studies, analysing this
phenomenon across models, datasets, and variations in inference and show that
semantic sensitivity can lead to major inconsistency within model predictions.

通过评估自然语言推理模型对含有微小语义保留表面形式噪声的对抗生成样本的效果，我们提供了证据表明，最新基于转换器的自然语言理解模型对于细微的语义保留有敏感性，这导致了推理过程中明显的不一致性。这种语义敏感性会导致在模型预测中的性能降低 12.92% 和 23.71%。