Segmenting Objectiveness and Task-awareness Unknown Region for Autonomous Driving

Guanglei Yang; Kevin Han; Mi Zheng; Wangmeng Zuo; Zhenhua Guo; Zitong Huang

REVIEW 3 major objections 5 minor 56 references

Segmenting Objectiveness and Task-awareness Unknown Region for Autonomous Driving

T0 review · 3 major / 5 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read A new framework, SOTA, fuses SAM features with scene-guided prompts to improve road-anomaly segmentation across base detectors.

desk verdict SOTA's empirical gains are real and the ablations are honest, but the 'task-awareness' story leans on a road-mask gate that the paper never isolates from the baseline. read the letter →

arxiv 2504.19183 v2 pith:Y6JRRW7B submitted 2025-04-27 cs.CV

Mi Zheng , Guanglei Yang , Zitong Huang , Zhenhua Guo , Kevin Han , Wangmeng Zuo This is my paper · ORCID

classification cs.CV

keywords anomalyobjectsegmentationroaddetectionout-of-distributionfeaturefusiontask-awarepromptlearningparameter-efficientfine-tuningSegmentAnythingModelautonomousdriving

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Road-anomaly segmentation for autonomous driving suffers from two failures today: anomalies that partly resemble known classes get under-segmented, and non-critical regions outside the drivable area trigger false alarms. The paper proposes SOTA, a framework that attacks both problems by injecting the base detector's out-of-distribution (OOD) confidence map into the feature space of SAM, the Segment Anything Model, and by building a task-aware prompt from the refined road mask via cross-attention, with Low-Rank Adaptation (LoRA) of SAM's mask decoder. On the RoadAnomaly benchmark SOTA raises the area under the precision-recall curve (AuPRC) from 85.42 to 92.46 and cuts the false-positive rate (FPR) from 6.92 to 4.03 over its RbA base, and on Fishyscapes Lost & Found it improves AuPRC from 70.81 to 76.10 and FPR from 6.30 to 3.53. The paper's claim is that the two modules contribute complementary gains, more complete masks for unknown objects and fewer off-road false alarms, and that the same gains transfer when the framework is wrapped around different detectors.

What carries the argument

The argument is carried by three mechanisms. The Semantic Fusion Block (SFB) takes the single-channel OOD confidence map $\tilde{y}$ from a pixel-wise segmentor such as RbA, projects and aligns it into SAM's latent space, concatenates it with the image embedding $F_I$, and fuses the two with a learned attention mask: $F_{\text{fused}} = (1+M)\odot F_I + (1-M)\odot T_a$. This is what objectiveness means here: anomaly cues become dense features that SAM's object-aware decoder can segment completely rather than as scattered uncertain pixels. The Scene-understanding Guided Prompt-Context Adapter (SG-PCA) implements task-awareness: it extracts the road class from the semantic logits, repairs the mask with erosion and dilation, hard-restricts the anomaly map to the road with a Hadamard product $\tilde{y}_t = \tilde{y} \odot y'_t$, and runs a multi-aware cross-attention ($Q=f_Q(\tilde{y}_t)$, $K=f_K(\tilde{y})$, $V=f_V(\tilde{y})$, $A=\text{softmax}(QK^T/\sqrt{d})$, $P_{\text{final}}=A\cdot V$) so the road-constrained prompt can still retrieve anomaly signal from the raw anomaly map. Finally, Low-Rank Adaptation (LoRA) fine-tunes SAM's mask decoder on the fused embedding and prompt, letting the pretrained decoder absorb the enriched inputs without threshold tuning or postprocessing. The cross-attention is the piece that keeps partially occluded on-road anomalies alive: the road mask acts as a query and the raw anomaly map as the key-value store, so objects that extend outside the dilated road are not immediately lost.

What would settle it

Build a test set of driving scenes with safety-critical objects deliberately placed off the drivable area, such as a pedestrian on the sidewalk or debris beyond the kerb, and compare per-region recall against on-road anomalies: if SOTA's off-road scores drop sharply relative to the base detector because of the road-restricted prompt in Eq. (7), the task-aware prior is suppressing off-road hazards rather than judging them. A companion check from the paper's Appendix C is to feed SOTA only anomaly maps that are exactly zero for small objects and verify that component-level F1* cannot exceed the base detector's recall on those instances.

Watch

Extended reading notes

Core claim

This paper establishes that fusing pixel-level OOD confidence into SAM's image embeddings and conditioning the mask-decoder prompt on the drivable-road prior repairs two defects of existing road-anomaly segmenters: incomplete masks for anomalies that share appearance with known classes, and task-agnostic overdetection outside the road. The Semantic Fusion Block concatenates a projected and aligned version of the base detector's OOD map $\tilde{y}$ with SAM's image features and reweights the composite with a sigmoid attention map, so anomaly cues are selectively emphasized while in-distribution context is preserved. The Scene-understanding Guided Prompt-Context Adapter derives a road mask from the semantic segmentation output, refines it with erosion and dilation, restricts the anomaly map to that region ($\tilde{y}_t = \tilde{y} \odot y'_t$), and then uses road-wise cross-attention between $\tilde{y}_t$ and $\tilde{y}$ to build a task-aware prompt for the mask decoder, fine-tuned with LoRA. With these pieces, SOTA raises the area under the precision-recall curve (AuPRC) from 85.42 to 92.46 and the false-positive rate (FPR) from 6.92 to 4.03 on RoadAnomaly, improves Fishyscapes Lost & Found AuPRC from 70.81 to 76.10 and FPR from 6.30 to 3.53, and lifts component-level F1* on RA-21 from 46.80 to 61.47; wrapping RPL and Mask2Anomaly in the same framework improves them as well.

Load-bearing premise

The load-bearing premise is that anomalies that matter for driving sit inside or near the drivable-road mask; everything outside that region is de-emphasized at the prompt level by Eq. (7), so an off-road hazard such as a pedestrian on the sidewalk is at risk of being silently suppressed.

Editorial extensions

If this is right

On the paper's numbers, wrapping RbA in SOTA raises RoadAnomaly AuPRC from 85.42 to 92.46 and cuts FPR from 6.92 to 4.03, and lifts Fishyscapes L&F AuPRC from 70.81 to 76.10 with FPR down from 6.30 to 3.53.
Component-level segmentation improves substantially: F1* on SMIYC RA-21 goes from 46.80 (RbA) to 61.47, sIoU from 55.70 to 61.28, and PPV from 52.10 to 60.41.
SOTA is detector-agnostic: plugged into RPL, RoadAnomaly AuPRC rises from 71.60 to 77.86 and FPR drops from 17.74 to 15.12; plugged into Mask2Anomaly, AuPRC rises from 79.53 to 87.86.
Even the smallest SAM backbone (SOTA-B) already beats RbA, and the largest (SOTA-H) adds under 0.2 seconds per image on an A100, so the accuracy gains do not require an impractical compute budget.
The improvement survives changes in training data: with Cityscapes plus Vistas as in-distribution data, SOTA posts AuPRC 92.77 and FPR 3.48 on Road Anomaly, and AuPRC 83.93 and FPR 2.23 on Fishyscapes L&F, ahead of comparison methods.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

Because Eq. (7) builds the task-aware prompt from a road-restricted anomaly map, off-road pixels are de-emphasized at the prompt level; the cross-attention could in principle recover them through the raw-map value stream, but the paper never measures how much off-road signal survives, so the effective behavior is an uncalibrated spatial prior rather than a learned judgment of task relevance.
The Appendix C failure case shows that objects invisible to the base detector are never recovered, so SOTA's ceiling is the recall of the underlying pixel-wise detector; a direct test would combine the adapter with multi-scale or zoom-in refinement on high-anomaly regions to see whether the fusion can create signal where the base map is exactly zero.
The ablation comparing SFB with naive feature addition (Table 7) suggests that most of the cross-dataset generalization gain comes from the learned attention fusion path rather than from prompt conditioning alone; ablating the projection and alignment subnetwork while keeping SG-PCA fixed would isolate that contribution.
The same SG-PCA machinery could be re-pointed at other scene priors, such as lane geometry, crosswalks, or the predicted ego trajectory, to define task-relevant regions per frame; this would turn task-awareness from a fixed road prior into a per-scene query, a forward-looking consequence the paper does not draw.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Here's my take on arXiv:2504.19183 (SOTA). The paper builds on RbA's anomaly scores, adds SAM feature fusion, a road-mask-guided prompt adapter, and LoRA fine-tuning of the mask decoder, and reports consistent gains on RoadAnomaly, Fishyscapes L&F, and SMIYC. The gains are real and the framework is modular: it also improves RPL and Mask2Anomaly when plugged in, which makes the story credible. The ablations are genuinely informative, and the appendix failure case (small objects lost because the base detector missed them) is the kind of honesty you like to see.

The main claim is 'task-awareness' — using a refined road mask to suppress off-road anomalies. The implementation is a hard Hadamard gate (Eq. 7), and every benchmark used puts the anomalies on the road, so the gate removes exactly the false positives that hurt baseline AuPRC/FPR. The stress-test worried this gate explains most of the gains. It doesn't, based on the paper's own ablation: config d (SG-PCA with raw prompt, no gate) vs f (adding the road-prompt fusion) moves RoadAnomaly AuPRC from 92.31 to 92.41 and Fishyscapes from 72.98 to 74.87. The big jumps come from SAM prompt fusion (b) and SFB+finetune (c). So the concern, while correct that the gate biases the method, is overstated as the load-bearing explanation. What is under-analyzed is the safety implication: anomalies off the road are zeroed out by construction, and the paper never evaluates or discusses that risk beyond saying the task doesn't need them. That's a real gap, though not a fatal one.

Minor issues: the 'real-time' claim is a stretch — 2.7 fps on an A100 is not real-time, though the overhead over RbA is only 0.18s. And the 'first to incorporate two things' novelty is marketing; UNO already used negative objectness. No code or full hyperparameters are provided, so numbers are not independently reproducible.

Overall: the work is sound, the empirical core holds, and the paper deserves a serious referee. I'd ask the authors to (1) add a masked-baseline ablation where the same road mask is applied directly to RbA's output, and (2) evaluate a few off-road but safety-relevant cases. Give it to a reviewer who knows the benchmark and will push on the attribution.

Referee Report

3 major / 5 minor

Summary. The paper proposes SOTA, a framework for road anomaly (out-of-distribution, OOD) segmentation that builds on the RbA baseline. It adds a Semantic Fusion Block (SFB) that fuses SAM image features with RbA's OOD confidence map, and a Scene-understanding Guided Prompt-Context Adapter (SG-PCA) that extracts a refined road mask, multiplies it with the anomaly score map (Eq. 7), and uses cross-attention to generate task-aware prompts. The SAM mask decoder is fine-tuned with LoRA. Experiments on RoadAnomaly, Fishyscapes Lost&Found, and SMIYC RA/RO report improvements over RbA on most metrics, with ablations attributing gains to individual components and to SAM scale.

Significance. If the reported results hold up, SOTA is a meaningful step for road anomaly segmentation because it explicitly addresses two real limitations of prior methods: incomplete segmentation of anomalous objects and over-detection outside the drivable area. The paper is extensive: it evaluates on standard benchmarks, provides component-level ablations, tests generalization to other base detectors (RPL, Mask2Anomaly), measures runtime, and includes a failure-case analysis. These are concrete strengths that make the work reproducible and useful. However, the main empirical claim is weakened by a confound between the task-aware gate in Eq. (7) and the benchmark design: all evaluated anomalies lie on the road, so a hard road-mask prior removes exactly the off-road false positives that inflate baseline metrics. The paper does not isolate this gate from the learned components, so the significance of the 'task-awareness' contribution is not yet established.

major comments (3)

[§3.3, Eq. (7); Table 3] This is the core issue: the paper's headline gains could plausibly come mostly from a hand-coded benchmark prior.
[§3.3, §4.3, §C] Without such an analysis, the general OOD detection performance of the method for driving safety is incompletely characterized.
[Table 1, SMIYC RO-21 row] This is a specific, load-bearing wording issue in the central claim.

minor comments (5)

[Table 5] This is a typo in the column header.
[§4.2] This is an incorrect table reference.
[Table 3] The table is hard to read and inconsistent with the text descriptions.
[§3.3, Eq. (8)–(10)] This is a clarity issue in the attention formulation.
[Appendix B.3, B.4] These are minor typos and a table-text alignment issue.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity found; SOTA is an end-to-end trained pipeline built on external anomaly scores, and no fitted input is renamed as a prediction.

full rationale

The paper's derivation chain is self-contained. The anomaly score is supplied by an external pixel-wise detector (RbA), SFB fuses that score with SAM image features, and SG-PCA multiplies the score by a morphologically refined road mask (Eq. 7). None of these operations is fitted to the reported test metrics, and no fitted parameter is renamed as a prediction. The road mask is an explicit input prior, not a quantity derived from the target output, so the masking step is not circular even though it encodes the same on-road assumption that the evaluation benchmarks happen to satisfy. LoRA adaptation is trained end-to-end with Dice and cross-entropy losses on outlier-exposure-composited training data and then evaluated on held-out benchmarks, which is external validation rather than self-reference. There are no load-bearing self-citations (the references to RbA, SAM, and LoRA are external prior work), no imported uniqueness theorems, and no ansatz smuggled in via citation. The only legitimate concern is an evaluation-confound risk: because the task-aware gate suppresses off-road responses by construction and the benchmark anomalies are predominantly located on the road, part of the reported improvement may reflect the prior rather than the learned modules. That concern belongs to external validity or correctness risk, not to circularity of the derivation, because the paper does not claim to derive the road prior from the anomaly output and does not present a benchmark-curve quantity as a first-principles prediction.

Assumptions & free parameters 2 free parameters · 4 assumptions · 0 invented entities

No new physical entities are introduced. The method's load-bearing assumptions are the road-prior, the dependency on the base detector's anomaly score, and the transferability of the outlier-exposure training distribution.

free parameters (2)

Morphological kernel size and iteration count for road mask refinement = 15x15 kernel, 15 iterations (dilation only in appendix; erosion+dilation in main text)
Hand-chosen hyperparameters in the Task-aware Aggregation module (Section 3.3 and Appendix A.1) that control how much the road prior is expanded; Table 9 shows a small sensitivity gain (0.16-0.79% AuPRC).
LoRA rank and alpha for mask decoder adaptation = not reported
LoRA (Section 3.4) is used to adapt SAM's mask decoder, but the rank and scaling are never specified, making the number of trainable parameters and the effective capacity unknown.

assumptions (4)

domain assumption Anomalies relevant to autonomous driving lie within or near the refined drivable road mask
SG-PCA computes ỹ_t = ỹ ⊙ y'_t (Eq. 7), zeroing anomaly scores outside the road; the method relies on this to reduce false positives, and it matches the benchmark distributions.
domain assumption The base pixel-wise OOD detector provides a usable anomaly confidence map
SOTA consumes the base detector's ỹ as the primary anomaly signal; Appendix C states that if the base detector misses small objects, SOTA cannot recover them.
domain assumption Outlier exposure by compositing COCO objects into Cityscapes scenes approximates the real-world OOD distribution
Training data is generated by pasting COCO instances onto Cityscapes backgrounds (Section 4.1, Appendix A.3), which assumes this synthetic distribution transfers to the test benchmarks.
ad hoc to paper SAM's image and mask-decoder features are complementary to OOD confidence maps for objectness
SFB's design assumes that fusing SAM features with ỹ improves object-level completeness; this is supported only by the paper's own ablations.

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Cite this review

Pith. "Pith review of Segmenting Objectiveness and Task-awareness Unknown Region for Autonomous Driving." pith.science (2026). https://pith.science/paper/Y6JRRW7B

@misc{pith2026250419183,
  author       = {Pith},
  title        = {Pith review of: Segmenting Objectiveness and Task-awareness Unknown Region for Autonomous Driving},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/Y6JRRW7B}},
  note         = {Machine review of arXiv:2504.19183}
}

read the original abstract

With the emergence of transformer-based architectures and large language models (LLMs), the accuracy of road scene perception has substantially advanced. Nonetheless, current road scene segmentation approaches are predominantly trained on closed-set data, resulting in insufficient detection capabilities for out-of-distribution (OOD) objects. To overcome this limitation, road anomaly detection methods have been proposed. However, existing methods primarily depend on image inpainting and OOD distribution detection techniques, facing two critical issues: (1) inadequate consideration of the objectiveness attributes of anomalous regions, causing incomplete segmentation when anomalous objects share similarities with known classes, and (2) insufficient attention to environmental constraints, leading to the detection of anomalies irrelevant to autonomous driving tasks. In this paper, we propose a novel framework termed Segmenting Objectiveness and Task-Awareness (SOTA) for autonomous driving scenes. Specifically, SOTA enhances the segmentation of objectiveness through a Semantic Fusion Block (SFB) and filters anomalies irrelevant to road navigation tasks using a Scene-understanding Guided Prompt-Context Adaptor (SG-PCA). Extensive empirical evaluations on multiple benchmark datasets, including Fishyscapes Lost and Found, Segment-Me-If-You-Can, and RoadAnomaly, demonstrate that the proposed SOTA consistently improves OOD detection performance across diverse detectors, achieving robust and accurate segmentation outcomes.

Figures

Figures reproduced from arXiv: 2504.19183 by the authors.

**Figure 1.** Visualisations for pixel-wise anomaly score maps. The top row shows real-world images where the anomalous objects [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗

**Figure 2.** Overview of our SOTA. Our approach comprises two principal modules: (a). The Semantic Fusion Block (SFB), [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** Qualitative Results. Visualization comparisons of RPL [ [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗

Figures from the paper (6 more)

**Figure 4.** Figure 4: SOTA Qualitative Ablation. Compared to the origi [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]

**Figure 5.** Figure 5: Runtime analysis shows SOTA maintains practical [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]

**Figure 6.** Figure 6: Qualitative Results on RoadAnomaly dataset. [PITH_FULL_IMAGE:figures/full_fig_p013_6.png]

**Figure 7.** Figure 7: Qualitative Results on Fishyscapes Lost&Found dataset. [PITH_FULL_IMAGE:figures/full_fig_p014_7.png]

**Figure 8.** Figure 8: Qualitative Results on SMIYC dataset [PITH_FULL_IMAGE:figures/full_fig_p015_8.png]

**Figure 9.** Figure 9: Failure cases [PITH_FULL_IMAGE:figures/full_fig_p015_9.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

56 extracted references · 36 canonical work pages

[1]

Vijay Badrinarayanan, Alex Kendall, and Roberto Cipolla. 2017. SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) (2017)

work page 2017
[2]

Serge Beucher. 1991. The watershed transformation applied to image segmenta- tion. Scanning microscopy 6, SUPPL (1991), 299–314

work page 1991
[3]

Hermann Blum, Paul-Edouard Sarlin, Juan Nieto, Roland Siegwart, and Cesar Cadena. 2021. The fishyscapes benchmark: Measuring blind spots in semantic segmentation. International Journal of Computer Vision 129, 11 (2021), 3119–3135

2021
[4]

Marius Zöllner

Daniel Bogdoll, Maximilian Nitsche, and J. Marius Zöllner. 2022. Anomaly Detection in Autonomous Driving: A Survey. CVPR Workshops (2022). https:// openaccess.thecvf.com/content/CVPR2022W/WAD/papers/Bogdoll_Anomaly_ Detection_in_Autonomous_Driving_A_Survey_CVPRW_2022_paper.pdf

work page 2022
[5]

Anirudh S Chakravarthy, Meghana Reddy Ganesina, Peiyun Hu, Laura Leal-Taixé, Shu Kong, Deva Ramanan, and Aljosa Osep. 2024. Lidar Panoptic Segmentation in an Open World. International Journal of Computer Vision (2024), 1–22

work page 2024
[6]

Robin Chan, Krzysztof Lis, Svenja Uhlemeyer, Hermann Blum, Sina Honari, Roland Siegwart, Pascal Fua, Mathieu Salzmann, and Matthias Rottmann. 2021. Segmentmeifyoucan: A benchmark for anomaly segmentation. arXiv preprint arXiv:2104.14812 (2021)

arXiv 2021
[7]

Robin Chan, Matthias Rottmann, and Hanno Gottschalk. 2021. Entropy maxi- mization and meta classification for out-of-distribution detection in semantic segmentation. In Proceedings of the ieee/cvf international conference on computer vision. 5128–5137

work page 2021
[8]

Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, and Alan L. Yuille. 2017. DeepLab: Semantic Image Segmentation with Deep Convo- lutional Nets, Atrous Convolution, and Fully Connected CRFs. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) (2017)

work page 2017

Show all 56 references

[9]

Liang-Chieh Chen, Yukun Zhu, George Papandreou, Florian Schroff, and Hartwig Adam. 2018. Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation. In European Conference on Computer Vision (ECCV)

2018
[10]

Bowen Cheng, Ishan Misra, Alexander G Schwing, Alexander Kirillov, and Rohit Girdhar. 2022. Masked-attention mask transformer for universal image segmen- tation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 1290–1299

2022
[11]

Bowen Cheng, Alex Schwing, and Alexander Kirillov. 2021. Per-Pixel Classifi- cation is Not All You Need for Semantic Segmentation. In Advances in Neural Information Processing Systems

2021
[12]

Marius Cordts, Mohamed Omran, Sebastian Ramos, Timo Rehfeld, Markus En- zweiler, Rodrigo Benenson, Uwe Franke, Stefan Roth, and Bernt Schiele. 2016. The cityscapes dataset for semantic urban scene understanding. In Proceedings of the IEEE conference on computer vision and patt...

2016
[13]

Anja Delić, Matej Grcic, and Siniša Šegvić. 2024. Outlier detection by ensem- bling uncertainty with negative objectness. BMVC 2024 British Machine Vision Conference

2024
[14]

Giancarlo Di Biase, Hermann Blum, Roland Siegwart, and Cesar Cadena. 2021. Pixel-Wise Anomaly Detection in Complex Driving Scenes. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . 16918–16927

2021
[15]

Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, et al
[16]

Matej Grcić, Petra Bevandić, and Siniša Šegvić. 2022. Densehybrid: Hybrid anomaly detection for dense open-set recognition. In European Conference on Computer Vision. Springer, 500–517

2022
[17]

Matej Grcic, Josip Šarić, and Siniša Šegvić. 2023. On Advantages of Mask-level Recognition for Outlier-aware Segmentation. CVPR 2023 Workshop Visual Anom- aly and Novelty Detection (V AND)

2023
[18]

Dan Hendrycks and Kevin Gimpel. 2016. A baseline for detecting misclassified and out-of-distribution examples in neural networks. arXiv preprint arXiv:1610.02136 (2016)

2016 arXiv
[19]

Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. 2022. LoRA: Low-Rank Adaptation of Large Language Models. In International Conference on Learning Representations . https: //openreview.net/forum?id=nZeVKeeFYf9

2022
[20]

Sanghun Jung, Jungsoo Lee, Daehoon Gwak, Sungha Choi, and Jaegul Choo
[21]

Berg, Wan-Yen Lo, Piotr Dollár, and Ross Girshick

Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C. Berg, Wan-Yen Lo, Piotr Dollár, and Ross Girshick. 2023. Segment Anything. arXiv preprint arXiv:2304.02643 (2023)

2023 arXiv
[22]

Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell. 2017. Simple and scalable predictive uncertainty estimation using deep ensembles. Advances in neural information processing systems 30 (2017)

2017
[23]

Kimin Lee, Kibok Lee, Honglak Lee, and Jinwoo Shin. 2018. A simple unified framework for detecting out-of-distribution samples and adversarial attacks. Advances in neural information processing systems 31 (2018)

2018
[24]

Chen Liang, Wenguan Wang, Jiaxu Miao, and Yi Yang. 2022. GMMSeg: Gauss- ian Mixture based Generative Semantic Segmentation Models. arXiv preprint arXiv:2210.02025 (2022)

2022 arXiv
[25]

Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick. 2014. Microsoft coco: Common objects in context. In Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proce...

2014
[26]

Krzysztof Lis, Sina Honari, Pascal Fua, and Mathieu Salzmann. 2023. Detecting Road Obstacles by Erasing Them. IEEE Transactions on Pattern Analysis and Machine Intelligence (2023). https://doi.org/10.1109/TPAMI.2023.3335152

2023
[27]

Krzysztof Lis, Krishna Nakka, Mathieu Salzmann, and Pascal Fua. 2019. Detecting the Unexpected via Image Resynthesis. In Proceedings of the IEEE International Conference on Computer Vision

2019
[28]

Yuyuan Liu, Choubo Ding, Yu Tian, Guansong Pang, Vasileios Belagiannis, Ian Reid, and Gustavo Carneiro. 2023. Residual Pattern Learning for Pixel-Wise Out-of-Distribution Detection in Semantic Segmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vi...

2023
[29]

Yuxing Liu, Ji Zhang, Zhou Xuchuan, Jingzhong Xiao, Huimin Yang, and Jiaxin Zhong. 2025. OoDDINO:A Multi-level Framework for Anomaly Segmentation on Complex Road Scenes. arXiv preprint arXiv:2507.01455 (2025)

2025 arXiv
[30]

Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo. 2021. Swin Transformer: Hierarchical Vision Transformer using Shifted Windows. In Proceedings of the IEEE/CVF International Conference on Computer Vision

2021
[31]

Jonathan Long, Evan Shelhamer, and Trevor Darrell. 2015. Fully Convolutional Networks for Semantic Segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

2015
[32]

Fausto Milletari, Nassir Navab, and Seyed-Ahmad Ahmadi. 2016. V-Net: Fully convolutional neural networks for volumetric medical image segmentation. 2016 Fourth International Conference on 3D Vision (3DV) (2016), 565–571

2016
[33]

Shervin Minaee, Yuri Boykov, Fatih Porikli, Antonio Plaza, Nasser Kehtarnavaz, and Demetri Terzopoulos. 2020. Image Segmentation Using Deep Learning: A Survey. arXiv preprint arXiv:2001.05566 (2020). https://arxiv.org/abs/2001.05566

2020 arXiv
[34]

Jishnu Mukhoti and Yarin Gal. 2018. Evaluating bayesian deep learning methods for semantic segmentation. arXiv preprint arXiv:1811.12709 (2018)

2018 arXiv
[35]

Henriques, and Fatma Güney

Nazir Nayal, Mısra Yavuz, João F. Henriques, and Fatma Güney. 2023. RbA: Segmenting Unknown Regions Rejected by All. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)

2023
[36]

Gerhard Neuhold, Tobias Ollmann, Samuel Rota Bulò, and Peter Kontschieder
[37]

Shyam Nandan Rai, Fabio Cermelli, Dario Fontanel, Carlo Masone, and Barbara Caputo. 2023. Unmasking Anomalies in Road-Scene Segmentation.arXiv preprint arXiv:2307.13316 (2023)

2023 arXiv
[38]

René Ranftl, Alexey Bochkovskiy, and Vladlen Koltun. 2021. Vision Transformers for Dense Prediction. In Proceedings of the IEEE/CVF International Conference on Computer Vision

2021
[39]

Olaf Ronneberger, Philipp Fischer, and Thomas Brox. 2015. U-Net: Convolutional Networks for Biomedical Image Segmentation. In Medical Image Computing and Computer-Assisted Intervention (MICCAI)

2015
[40]

Robin Strudel, Ricardo Garcia, Ivan Laptev, and Cordelia Schmid. 2021. Seg- menter: Transformer for Semantic Segmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision

2021
[41]

Hans Thisanke, Deshan Chamli, Kavindu Chamith, Sachith Seneviratne, Rajith Vidanaarachchi, and Damayanthi Herath. 2023. Semantic Segmentation using Vision Transformers: A Survey. arXiv preprint arXiv:2305.03273 (2023). https: //arxiv.org/abs/2305.03273

2023 arXiv
[42]

Yu Tian, Yuyuan Liu, Guansong Pang, Fengbei Liu, Yuanhong Chen, and Gustavo Carneiro. 2022. Pixel-wise energy-biased abstention learning for anomaly seg- mentation on complex urban driving scenes. InEuropean Conference on Computer Vision. Springer, 246–263. MM ’25, October 27–...

2022
[43]

Tomas Vojir, Tomáš Šipka, Rahaf Aljundi, Nikolay Chumerin, Daniel Olmeda Reino, and Jiri Matas. 2021. Road anomaly detection by partial image reconstruc- tion with segmentation coupling. In Proceedings of the IEEE/CVF International Conference on Computer Vision . 15651–15660

2021
[44]

Tomáš Vojíř, Jan Šochman, and Jiří Matas. 2024. PixOOD: Pixel-Level Out-of- Distribution Detection. In ECCV

2024
[45]

Wang, M.-Y

T.-C. Wang, M.-Y. Liu, J.-Y. Zhu, A. Tao, J. Kautz, and B. Catanzaro. 2018. High- Resolution Image Synthesis and Semantic Manipulation with Conditional GANs. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition

2018
[46]

Y. Xia, Y. Zhang, F. Liu, W. Shen, and A. Yuille. 2020. Synthesize Then Com- pare: Detecting Failures and Anomalies for Semantic Segmentation. In European Conference on Computer Vision

2020
[47]

Jingkang Yang, Kaiyang Zhou, Yixuan Li, and Ziwei Liu. 2021. Generalized Out-of-Distribution Detection: A Survey. arXiv preprint arXiv:2110.11334 (2021)

2021 arXiv
[48]

Zavrtanik, M

V. Zavrtanik, M. Kristan, and D. Skoaj. 2020. Reconstruction by Inpainting for Visual Anomaly Detection. Pattern Recognition (2020)

2020
[49]

Hengshuang Zhao, Jianping Shi, Xiaojuan Qi, Xiaogang Wang, and Jiaya Jia
[50]

Wenjie Zhao, Jia Li, Xin Dong, Yu Xiang, and Yunhui Guo. 2024. Segment Every Out-of-Distribution Object. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 3910–3920

2024
[51]

Torr, and Li Zhang

Sixiao Zheng, Jiachen Lu, Hengshuang Zhao, Xiatian Zhu, Zekun Luo, Yabiao Wang, Yanwei Fu, Jianfeng Feng, Tao Xiang, Philip H.S. Torr, and Li Zhang. 2021. Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers. In Proceedings of the IEEE/CVF...

2021
[52]

Bolei Zhou, Hang Zhao, Xavier Puig, Sanja Fidler, Adela Barriuso, and Antonio Torralba. 2017. Scene Parsing through ADE20K Dataset. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . Segmenting Objectiveness and Task-awareness Unknown Region for...

2017
[53]

In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Pyramid Scene Parsing Network. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
[2017]

In 2017 IEEE International Conference on Computer Vision (ICCV)

The Mapillary Vistas Dataset for Semantic Understanding of Street Scenes. In 2017 IEEE International Conference on Computer Vision (ICCV) . 5000–5009. https://doi.org/10.1109/ICCV.2017.534

2017 doi
[2020]

In International Conference on Learning Representations (ICLR)

An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. In International Conference on Learning Representations (ICLR)
[2021]

In Proceedings of the IEEE/CVF International Conference on Computer Vision

Standardized max logits: A simple yet effective approach for identifying unexpected road obstacles in urban-scene segmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision . 15425–15434

Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.

[1] [1]

Vijay Badrinarayanan, Alex Kendall, and Roberto Cipolla. 2017. SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) (2017)

work page 2017

[2] [2]

Serge Beucher. 1991. The watershed transformation applied to image segmenta- tion. Scanning microscopy 6, SUPPL (1991), 299–314

work page 1991

[3] [3]

Hermann Blum, Paul-Edouard Sarlin, Juan Nieto, Roland Siegwart, and Cesar Cadena. 2021. The fishyscapes benchmark: Measuring blind spots in semantic segmentation. International Journal of Computer Vision 129, 11 (2021), 3119–3135

2021

[4] [4]

Marius Zöllner

Daniel Bogdoll, Maximilian Nitsche, and J. Marius Zöllner. 2022. Anomaly Detection in Autonomous Driving: A Survey. CVPR Workshops (2022). https:// openaccess.thecvf.com/content/CVPR2022W/WAD/papers/Bogdoll_Anomaly_ Detection_in_Autonomous_Driving_A_Survey_CVPRW_2022_paper.pdf

work page 2022

[5] [5]

Anirudh S Chakravarthy, Meghana Reddy Ganesina, Peiyun Hu, Laura Leal-Taixé, Shu Kong, Deva Ramanan, and Aljosa Osep. 2024. Lidar Panoptic Segmentation in an Open World. International Journal of Computer Vision (2024), 1–22

work page 2024

[6] [6]

Robin Chan, Krzysztof Lis, Svenja Uhlemeyer, Hermann Blum, Sina Honari, Roland Siegwart, Pascal Fua, Mathieu Salzmann, and Matthias Rottmann. 2021. Segmentmeifyoucan: A benchmark for anomaly segmentation. arXiv preprint arXiv:2104.14812 (2021)

arXiv 2021

[7] [7]

Robin Chan, Matthias Rottmann, and Hanno Gottschalk. 2021. Entropy maxi- mization and meta classification for out-of-distribution detection in semantic segmentation. In Proceedings of the ieee/cvf international conference on computer vision. 5128–5137

work page 2021

[8] [8]

Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, and Alan L. Yuille. 2017. DeepLab: Semantic Image Segmentation with Deep Convo- lutional Nets, Atrous Convolution, and Fully Connected CRFs. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) (2017)

work page 2017