tiny_object_detection/docs/literature_review.md

# Literature and novelty verification — 2026-10-31

## Scope and evidence standard

This independent review replaces assumptions from the earlier conversation. Cutoff: October 31, 2026, UTC (the supplied environment date). It does not cover future November publications. Searches used two search engines, publisher/CVF/PMLR/NeurIPS pages, arXiv and author repositories. Searches covered graph/candidate/conflict, loss/false-alarm/Neyman–Pearson/calibration, and motion/temporal/hypergraph/track-before-detect terms, including venue-specific 2025–2026 queries. Earlier foundational papers are included where directly relevant.

**This is a verification dossier, not a completed systematic review or an experimental validation.** Bibliographic identity and reported mechanisms have stronger support than runnable code, data rights, or novelty absence. Indexed publisher excerpts are explicitly distinguished from full-text inspection. A search with no result never proves novelty. No citation counts, performance improvements, or journal quartiles are asserted. Links establish discovery/access to a page, not a successful dataset download or reproduction. Every unknown remains marked. For entries with no captured DOI/arXiv, the proceedings URL is supplied instead of inventing an identifier.

## Decision-changing findings

1. Candidate-region graph classification already suppresses false alarms (A2). EDGRU (A1) is real recent prior art, and its author repository now has a verified code listing. Generic graph use is not novel.
2. SLS, SD Loss, AC-SLSIoU, and CG-IRNet overlap the original Project B. “Size-adaptive loss plus negative weighting” is insufficient.
3. HyperTea, MIST, DeepPro, MoPKL, and classical track-before-detect substantially overlap Project C. Candidate sparsification must retain evidence that would otherwise be discarded; a graph cannot recover a target removed before graph construction.
4. NeurIPS 2025 already addresses metric inconsistency (B10). A paper merely redefining Pd/Fa would not be new.
5. LTT and CRC establish risk calibration (B13–B14). Our proposed certificate is a transparent application of known statistical tools. The potentially useful contribution is matching-aware training and a carefully validated object-level protocol, not a new general theorem.
6. Close-target unmixing (A9) makes a minimum-separation exclusion assumption especially difficult to defend.

## Evidence records

The code/data fields below state the strongest checked status; “not verified” does not mean nonexistent. Original paper-reported results are not copied into our results table. `evidence/papers.json` contains the same records in machine-readable form.

## Project A: 10 directly relevant records

### A1 — EDGRU: edge-guided dual graph reasoning U-Net with spatial graph attention for infrared small target detection

- Authors: Xinying Huang; Jianhua Song; Xinrong Fu; Yizhe Lu; Lei Yuan.
- Date/venue: 2026-10-27, Scientific Reports early article.
- Identifier: 10.1038/s41598-026-76517-6. [Primary/source link](https://www.nature.com/articles/s41598-026-76517-6).
- Already done / relevance: Edge cues and dual graph feature reasoning already address clutter. This is not evidence for physical mutual exclusion between candidates.
- Code: Author repository https://github.com/Huang-1015/EDGRU is accessible: model, train, test, data, loss and metric files listed. Commit b0159312ae8047e37c913cf2cf6116c7776eeffc, dated 2026-10-28. Execution untested; root listing contains no license file. See evidence/upstream_audit.md.
- Data: SIRST and NUDT-SIRST reported.
- Verification: Primary indexed metadata/abstract; full text not audited.

### A2 — Infrared small target detection based on multi-feature fusion combined with graph representation learning

- Authors: Jiakun Deng; Yizhuo Yin; Yanbo Zhang; Chang Long; Kexuan Li; Xingye Cui; Zhenming Peng.
- Date/venue: 2026, 55(1):100–108; received 2025-03-19.
- Identifier: 10.12178/1001-0548.2025060. [Primary/source link](https://www.juestc.uestc.edu.cn/en/article/doi/10.12178/1001-0548.2025060).
- Already done / relevance: Morphological proposals, graph representations, fused handcrafted/deep features and classification already filter candidate false alarms.
- Code: Not verified; no runnable release established.
- Data: Availability/protocol not independently established.
- Verification: Publisher page opened; abstract read.

### A3 — CS-ViG-UNet: Infrared small and dim target detection based on cycle shift vision graph convolution network

- Authors: Jian Lin; Shaoyi Li; Xi Yang; Saisai Niu; Binbin Yan; Zhongjie Meng.
- Date/venue: 2024, Expert Systems with Applications; exact online date unresolved.
- Identifier: 10.1016/j.eswa.2024.124385. [Primary/source link](https://www.sciencedirect.com/science/article/pii/S095741742401251X).
- Already done / relevance: Patch-level sparse graph attention and U-Net integration; graph use alone is not new.
- Code: https://github.com/Linaom1214/CSViG-UNet — indexed repository with train/eval files; not executed.
- Data: SIRST Augment and IRSTD-1k links listed; bytes not acquired.
- Verification: Primary indexed metadata/abstract; full text not audited.

### A4 — IRST-CGSeg: Infrared Small Target Detection Based on Clustering-Guided Graph Learning and Hierarchical Features

- Authors: Guimin Jia; Tao Chen; Yu Cheng; Pengyu Lu.
- Date/venue: 2025-02-21, Electronics.
- Identifier: 10.3390/electronics14050858. [Primary/source link](https://www.mdpi.com/2079-9292/14/5/858).
- Already done / relevance: Clustering initializes subgraphs and guides graph/texture feature fusion.
- Code: No public code verified.
- Data: Publisher says study data available on request.
- Verification: Primary indexed metadata/abstract; full text not audited.

### A5 — Graph Laplacian regularization for fast infrared small target detection

- Authors: Ting Liu; Yongxian Liu; Jungang Yang; Boyang Li; Yingqian Wang; Wei An.
- Date/venue: 2025 volume 158; DOI assigned 2024; online day unresolved.
- Identifier: 10.1016/j.patcog.2024.111077. [Primary/source link](https://www.sciencedirect.com/science/article/pii/S0031320324008288).
- Already done / relevance: Frame-node graph regularization replaces expensive low-rank estimation; MCP sparsity separates targets. Explicit optimization already exists.
- Code: Not verified; no runnable release established.
- Data: Availability/protocol not independently established.
- Verification: Primary indexed metadata/abstract; full text not audited.

### A6 — Infrared small target detection based on hypergraph and asymmetric penalty function

- Authors: Yuan Luo; Xiaorun Li; Shuhan Chen.
- Date/venue: 2025, Pattern Recognition.
- Identifier: 10.1016/j.patcog.2025.111634. [Primary/source link](https://www.sciencedirect.com/science/article/pii/S0031320325002948).
- Already done / relevance: Patch-tensor hypergraphs combine spatial and temporal weights; low-rank background and asymmetric target sparsity are jointly modeled.
- Code: Not verified; no runnable release established.
- Data: Availability/protocol not independently established.
- Verification: Primary indexed metadata/abstract; full text not audited.

### A7 — Robust Infrared Small Target Detection via Jointly Sparse Constraint of l1/2-Metric and Dual-Graph Regularization

- Authors: Fei Zhou; Yiquan Wu; Yimian Dai; Kang Ni.
- Date/venue: 2020, Remote Sensing 12(12):1963.
- Identifier: 10.3390/rs12121963. [Primary/source link](https://www.mdpi.com/2072-4292/12/12/1963).
- Already done / relevance: Spatial and feature manifold regularizers plus nonconvex sparsity suppress clutter. Graph-constrained optimization is established prior art.
- Code: Not verified; no runnable release established.
- Data: Availability/protocol not independently established.
- Verification: Primary indexed metadata/abstract; full text not audited.

### A8 — Directional graph-structured energy propagation for infrared small target detection

- Authors: Sen Yang; Guanxun Cui.
- Date/venue: 2026-06 volume, Infrared Physics & Technology.
- Identifier: 10.1016/j.infrared.2026.106613. [Primary/source link](https://www.sciencedirect.com/science/article/pii/S1350449526002483).
- Already done / relevance: Gradient-directed pixel DAG energy propagation drives U-Net attention. A graph or DAG by itself is not a contribution.
- Code: Not verified; no runnable release established.
- Data: IRSTD-1k reported.
- Verification: Primary indexed metadata/abstract; full text not audited.

### A9 — DISTA-Net: Dynamic Closely-Spaced Infrared Small Target Unmixing

- Authors: Shengdong Han; Shangdong Yang; Yuxuan Li; Xin Zhang; Xiang Li; Jian Yang; Ming-Ming Cheng; Yimian Dai.
- Date/venue: ICCV 2025; preprint 2025-05-25.
- Identifier: arXiv:2505.19148. [Primary/source link](https://openaccess.thecvf.com/content/ICCV2025/html/Han_DISTA-Net_Dynamic_Closely-Spaced_Infrared_Small_Target_Unmixing_ICCV_2025_paper.html).
- Already done / relevance: Dynamic sparse unmixing explicitly resolves close targets, including subpixel positions; exposes the flaw in proximity exclusion.
- Code: https://github.com/GrokCV/GrokCSO — repository indexed.
- Data: CSIST-100K and CSO-mAP released according to paper/repository; download not tested.
- Verification: CVF proceedings metadata and arXiv abstract verified; author order follows proceedings (preprint differs).

### A10 — Motion Prior Knowledge Learning with Homogeneous Language Descriptions for Moving Infrared Small Target Detection

- Authors: Shengjia Chen; Luping Ji; Weiwei Duan; Shuang Peng; Mao Ye.
- Date/venue: 2025-04-11 proceedings release, AAAI.
- Identifier: 10.1609/aaai.v39i2.32217. [Primary/source link](https://ojs.aaai.org/index.php/AAAI/article/view/32217).
- Already done / relevance: Motion representations refined by graph attention; motion-aware graph learning is already used.
- Code: Not verified; no runnable release established.
- Data: ITSDT-15K and IRDST reported.
- Verification: Primary indexed metadata/abstract; full text not audited.


## Project B: 15 directly relevant records

This includes IRSTD-specific objectives/evaluation and directly applicable general loss/calibration foundations, explicitly labeled by dataset domain.

### B1 — Infrared Small Target Detection with Scale and Location Sensitivity

- Authors: Qiankun Liu; Rui Liu; Bolun Zheng; Hongkui Wang; Ying Fu.
- Date/venue: CVPR 2024; preprint 2024-03-28.
- Identifier: arXiv:2403.19366. [Primary/source link](https://arxiv.org/abs/2403.19366).
- Already done / relevance: SLS combines scale-weighted IoU and centroid-related localization; generic size-sensitive loss is not new.
- Code: https://github.com/ying-fu/MSHNet — opened; model/utils/main.py visible.
- Data: IRSTD-1k and NUDT-SIRST; links in repository.
- Verification: Primary metadata plus method equations read in arXiv HTML.

### B2 — Pinwheel-shaped Convolution and Scale-based Dynamic Loss for Infrared Small Target Detection

- Authors: Jiangnan Yang; Shuangli Liu; Jingjun Wu; Xinyu Su; Nan Hai; Xueli Huang.
- Date/venue: AAAI 2025, proceedings release 2025-04-11; preprint 2024-12-22.
- Identifier: 10.1609/aaai.v39i9.32996; arXiv:2412.16986. [Primary/source link](https://ojs.aaai.org/index.php/AAAI/article/view/32996).
- Already done / relevance: Scale-dependent weighting of localization/scale terms already exists.
- Code: https://github.com/JN-Yang/PConv-SDloss-Data — opened; MIT repository license.
- Data: SIRST-UAVB and IRSTD-1k; release links, not downloaded.
- Verification: Primary indexed metadata/abstract; full text not audited.

### B3 — Revisiting the Scale Loss Function and Gaussian-Shape Convolution for Infrared Small Target Detection

- Authors: Hao Li; Man Fung Zhuo.
- Date/venue: 2026-04-11 preprint; peer review not verified.
- Identifier: arXiv:2604.09991. [Primary/source link](https://arxiv.org/abs/2604.09991).
- Already done / relevance: Already critiques scale-loss monotonicity and proposes a signed-area-difference loss. Do not present that critique as new.
- Code: Abstract claims anonymous code/weights; URL/release not established.
- Data: IRSTD-1k, NUDT-SIRST, SIRST-UAVB reported.
- Verification: arXiv record opened.

### B4 — Boosting Infrared Small Target Detection via Logit-Domain Contrast and Adaptive Shape Refinement

- Authors: Handong Zeng; Zhengeng Yang; Shuai Zhang; Shikai Chen; Hongshan Yu.
- Date/venue: 2026-07-02 preprint; submitted to IEEE, acceptance not verified.
- Identifier: arXiv:2607.01555. [Primary/source link](https://arxiv.org/abs/2607.01555).
- Already done / relevance: AC-SLSIoU combines logit-margin discrimination, adaptive boundary penalties and false-alarm focal weighting. A new weighted negative-pixel term is insufficient.
- Code: Not verified; no runnable release established.
- Data: IRSTD benchmarks reported; release not established.
- Verification: arXiv record and method sections opened.

### B5 — CG-IRNet: Structure–Confidence Hybrid Learning for Low-False-Alarm Infrared Small Target Detection

- Authors: Ziwen Zhu; Mengmeng Liao.
- Date/venue: 2026-06-01, Electronics.
- Identifier: 10.3390/electronics15112405. [Primary/source link](https://www.mdpi.com/2079-9292/15/11/2405).
- Already done / relevance: Confidence-aware SLS, instance structure supervision and background suppression already target low false alarms.
- Code: Not verified; no runnable release established.
- Data: SIRST, NUDT-SIRST, IRSTD-1k reported.
- Verification: Primary indexed metadata/abstract; full text not audited.

### B6 — Infrared Small Target Detection based on Adjustable Sensitivity Strategy and Multi-Scale Fusion

- Authors: Jinmiao Zhao; Zelin Shi; Chuang Yu; Yunpeng Liu.
- Date/venue: 2024-07-29 preprint.
- Identifier: arXiv:2407.20090. [Primary/source link](https://arxiv.org/abs/2407.20090).
- Already done / relevance: EEDM loss and adjustable-sensitivity postprocessing address imbalance and detection tradeoffs; threshold selection is not new.
- Code: Not verified; no runnable release established.
- Data: Availability/protocol not independently established.
- Verification: Primary indexed metadata/abstract; full text not audited.

### B7 — Miss Detection vs. False Alarm: Adversarial Learning for Small Object Segmentation in Infrared Images

- Authors: Huan Wang; Luping Zhou; Lei Wang.
- Date/venue: ICCV 2019.
- Identifier: DOI not independently captured; stable CVF proceedings URL. [Primary/source link](https://openaccess.thecvf.com/content_ICCV_2019/html/Wang_Miss_Detection_vs._False_Alarm_Adversarial_Learning_for_Small_Object_ICCV_2019_paper.html).
- Already done / relevance: Separates missed detections and false alarms into adversarial learning tasks. Optimizing this tradeoff predates recent loss work.
- Code: Official lead https://github.com/wanghuanphd/MDvsFA_cGAN not opened; https://github.com/ZhexuanZhou/MDvsFA is a third-party PyTorch implementation, not treated as official.
- Data: Original data release not downloaded.
- Verification: Primary indexed metadata/abstract; full text not audited.

### B8 — ISNet: Shape Matters for Infrared Small Target Detection

- Authors: Mingjin Zhang; Rui Zhang; Yuxiang Yang; Haichen Bai; Jing Zhang; Jie Guo.
- Date/venue: CVPR 2022.
- Identifier: 10.1109/CVPR52688.2022.00095 (secondary DOI metadata; CVF paper verified). [Primary/source link](https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_ISNet_Shape_Matters_for_Infrared_Small_Target_Detection_CVPR_2022_paper.html).
- Already done / relevance: Edge supervision and Dice-based shape learning establish a localization/shape baseline.
- Code: https://github.com/RuiZhang97/ISNet — opened; code directed to master branch.
- Data: Paper says 1,000; repository says 1,001 IRSTD-1k images. Resolve by downloaded manifest..
- Verification: CVF page/PDF excerpt and author repository checked.

### B9 — A Normalized Gaussian Wasserstein Distance for Tiny Object Detection

- Authors: Jinwang Wang; Chang Xu; Wen Yang; Lei Yu.
- Date/venue: 2021-10-26 preprint.
- Identifier: arXiv:2110.13389. [Primary/source link](https://arxiv.org/abs/2110.13389).
- Already done / relevance: Gaussian box distance mitigates tiny-box overlap sensitivity; not a mask loss or false-alarm certificate.
- Code: https://github.com/jwwangchn/NWD — paper-linked, not opened.
- Data: AI-TOD; not an IRSTD dataset.
- Verification: Primary indexed metadata/abstract; full text not audited.

### B10 — Rethinking Evaluation of Infrared Small Target Detection

- Authors: Youwei Pang; Xiaoqi Zhao; Lihe Zhang; Huchuan Lu; Georges El Fakhri; Xiaofeng Liu; Shijian Lu.
- Date/venue: NeurIPS 2025 Datasets and Benchmarks.
- Identifier: arXiv:2509.16888; 10.52202/085713-3860. [Primary/source link](https://papers.nips.cc/paper_files/paper/2025/hash/a81051ae2c8b1e46bd51480917b8ab84-Abstract-Datasets_and_Benchmarks_Track.html).
- Already done / relevance: Hybrid pixel/object metrics, error analysis and cross-dataset evaluation already challenge legacy Pd/Fa evaluation.
- Code: https://github.com/lartpang/PyIRSTDMetrics — opened; MIT; code files visible.
- Data: Toolkit available; not executed.
- Verification: Proceedings/PDF excerpt/repository read; full name follows PDF.

### B11 — Focal Loss for Dense Object Detection

- Authors: Tsung-Yi Lin; Priya Goyal; Ross Girshick; Kaiming He; Piotr Dollár.
- Date/venue: 2017-08-07 preprint; ICCV 2017.
- Identifier: arXiv:1708.02002. [Primary/source link](https://arxiv.org/abs/1708.02002).
- Already done / relevance: Hard-example reweighting handles imbalance; no budget certificate.
- Code: Code linked in arXiv; not executed.
- Data: COCO, not IRSTD.
- Verification: arXiv record opened.

### B12 — V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation

- Authors: Fausto Milletari; Nassir Navab; Seyed-Ahmad Ahmadi.
- Date/venue: 2016-06-15 preprint.
- Identifier: arXiv:1606.04797. [Primary/source link](https://arxiv.org/abs/1606.04797).
- Already done / relevance: Dice objective addresses overlap/imbalance, not object false-alarm constraints.
- Code: Not verified; no runnable release established.
- Data: Medical MRI, not IRSTD.
- Verification: arXiv record opened.

### B13 — Learn then Test: Calibrating Predictive Algorithms to Achieve Risk Control

- Authors: Anastasios N. Angelopoulos; Stephen Bates; Emmanuel J. Candès; Michael I. Jordan; Lihua Lei.
- Date/venue: 2021-10-03 preprint; Annals of Applied Statistics 2025.
- Identifier: arXiv:2110.01052. [Primary/source link](https://arxiv.org/abs/2110.01052).
- Already done / relevance: Finite-sample risk control through hypothesis testing already applies to detection/segmentation. Our certificate is an application, not a new concentration theorem.
- Code: Not verified; no runnable release established.
- Data: General calibration examples; no IRSTD dataset claimed.
- Verification: Primary indexed metadata/abstract; full text not audited.

### B14 — Conformal Risk Control

- Authors: Anastasios N. Angelopoulos; Stephen Bates; Adam Fisch; Lihua Lei; Tal Schuster.
- Date/venue: 2022-08-04 preprint.
- Identifier: arXiv:2208.02814. [Primary/source link](https://arxiv.org/abs/2208.02814).
- Already done / relevance: Monotone bounded losses support marginal expected-risk guarantees. Distinguish these from high-probability-over-calibration guarantees.
- Code: Not verified; no runnable release established.
- Data: General prediction examples; no IRSTD dataset claimed.
- Verification: Primary indexed metadata/abstract; full text not audited.

### B15 — Diffuse to Detect: Bi-Level Sample Rebalancing with Pseudo-Label Diffusion for Point-Supervised Infrared Small-Target Detection

- Authors: Zhu Liu; Yuanhang Yao; Ping Qian; Zihang Chen; Risheng Liu.
- Date/venue: ICML 2026.
- Identifier: DOI/arXiv not captured; PMLR 306:79394–79413. [Primary/source link](https://proceedings.mlr.press/v306/liu26fj.html).
- Already done / relevance: Adaptive sample weights, label diffusion and bi-level optimization already tackle sample imbalance.
- Code: https://github.com/yuanhang-yao/diffuse-to-detect — indexed author repository.
- Data: SIRST-v1, NUDT-SIRST, IRSTD-1k, SIRST3 listed.
- Verification: PMLR proceedings verified.


## Project C: 12 directly relevant records

### A5 — Graph Laplacian regularization for fast infrared small target detection

- Authors: Ting Liu; Yongxian Liu; Jungang Yang; Boyang Li; Yingqian Wang; Wei An.
- Date/venue: 2025 volume 158; DOI assigned 2024; online day unresolved.
- Identifier: 10.1016/j.patcog.2024.111077. [Primary/source link](https://www.sciencedirect.com/science/article/pii/S0031320324008288).
- Already done / relevance: Frame-node graph regularization replaces expensive low-rank estimation; MCP sparsity separates targets. Explicit optimization already exists.
- Code: Not verified; no runnable release established.
- Data: Availability/protocol not independently established.
- Verification: Primary indexed metadata/abstract; full text not audited.

### A6 — Infrared small target detection based on hypergraph and asymmetric penalty function

- Authors: Yuan Luo; Xiaorun Li; Shuhan Chen.
- Date/venue: 2025, Pattern Recognition.
- Identifier: 10.1016/j.patcog.2025.111634. [Primary/source link](https://www.sciencedirect.com/science/article/pii/S0031320325002948).
- Already done / relevance: Patch-tensor hypergraphs combine spatial and temporal weights; low-rank background and asymmetric target sparsity are jointly modeled.
- Code: Not verified; no runnable release established.
- Data: Availability/protocol not independently established.
- Verification: Primary indexed metadata/abstract; full text not audited.

### A9 — DISTA-Net: Dynamic Closely-Spaced Infrared Small Target Unmixing

- Authors: Shengdong Han; Shangdong Yang; Yuxuan Li; Xin Zhang; Xiang Li; Jian Yang; Ming-Ming Cheng; Yimian Dai.
- Date/venue: ICCV 2025; preprint 2025-05-25.
- Identifier: arXiv:2505.19148. [Primary/source link](https://openaccess.thecvf.com/content/ICCV2025/html/Han_DISTA-Net_Dynamic_Closely-Spaced_Infrared_Small_Target_Unmixing_ICCV_2025_paper.html).
- Already done / relevance: Dynamic sparse unmixing explicitly resolves close targets, including subpixel positions; exposes the flaw in proximity exclusion.
- Code: https://github.com/GrokCV/GrokCSO — repository indexed.
- Data: CSIST-100K and CSO-mAP released according to paper/repository; download not tested.
- Verification: CVF proceedings metadata and arXiv abstract verified; author order follows proceedings (preprint differs).

### A10 — Motion Prior Knowledge Learning with Homogeneous Language Descriptions for Moving Infrared Small Target Detection

- Authors: Shengjia Chen; Luping Ji; Weiwei Duan; Shuang Peng; Mao Ye.
- Date/venue: 2025-04-11 proceedings release, AAAI.
- Identifier: 10.1609/aaai.v39i2.32217. [Primary/source link](https://ojs.aaai.org/index.php/AAAI/article/view/32217).
- Already done / relevance: Motion representations refined by graph attention; motion-aware graph learning is already used.
- Code: Not verified; no runnable release established.
- Data: ITSDT-15K and IRDST reported.
- Verification: Primary indexed metadata/abstract; full text not audited.

### C1 — MIST: A Benchmark and Baseline for Multi-Frame Infrared Small Target Detection in Complex Motion

- Authors: Rui Gao; Meihong Zhang; Gongyang Li; Guanyi Li; Kai Zhao; Xianchao Zhang; Dan Zeng.
- Date/venue: TIP 2026; author acceptance announcement 2026-04-27.
- Identifier: 10.1109/TIP.2026.3689420 (PubMed metadata). [Primary/source link](https://github.com/ShuCvlab/MIST).
- Already done / relevance: Complex-motion benchmark; shifted-neighborhood compensation and information-bottleneck decoding already address irregular weak motion.
- Code: Author repository opened; train/test/config files; dataset/weights released.
- Data: MIST 78 train / 42 test sequences; 11 hard test subset; links not downloaded.
- Verification: Author repository read; DOI secondary metadata, publisher full text not audited.

### C2 — Probing Deep into Temporal Profile Makes the Infrared Small Target Detector Much Better

- Authors: Ruojing Li; Wei An; Yingqian Wang; Xinyi Ying; Yimian Dai; Longguang Wang; Miao Li; Yulan Guo; Li Liu.
- Date/venue: Preprint 2025-06-15; TPAMI 2026 volume.
- Identifier: arXiv:2506.12766; 10.1109/TPAMI.2026.3683258. [Primary/source link](https://arxiv.org/abs/2506.12766).
- Already done / relevance: Long temporal profiles and one-dimensional anomaly modeling accumulate weak evidence without candidate graphs.
- Code: https://github.com/TinaLRJ/DeepPro — opened.
- Data: NUDT-MIRSDT, HiNo and satellite dataset links; README 120 total vs 80 train +20 test leaves 20 unexplained.
- Verification: arXiv, repository; journal DOI from PubMed, author order follows journal/repository.

### C3 — HyperTea: Hypergraph-based temporal enhancement and alignment network for moving infrared small target detection

- Authors: Zhaoyuan Qi; Weihua Gao; Wenlong Niu; Jie Tang; Yun Li; Xiaodong Peng.
- Date/venue: Preprint 2025-08-14; KBS 2026.
- Identifier: arXiv:2508.10678; 10.1016/j.knosys.2026.116099. [Primary/source link](https://arxiv.org/abs/2508.10678).
- Already done / relevance: Global/local temporal hypergraph enhancement and alignment directly overlap generic Project C.
- Code: https://github.com/Lurenjia-LRJ/HyperTea — indexed README says full code/weights still being organized; not a verified runnable release.
- Data: DAUB and IRDST links; no dataset bytes acquired.
- Verification: Primary indexed metadata/abstract; full text not audited.

### C4 — MI-DETR: A Strong Baseline for Moving Infrared Small Target Detection with Bio-Inspired Motion Integration

- Authors: Nian Liu; Jin Gao; Shubo Lin; Yutong Kou; Sikui Zhang; Fudong Ge; Zhiqiang Pu; Liang Li; Gang Wang; Yizheng Wang; Weiming Hu.
- Date/venue: 2026-03-05 preprint.
- Identifier: arXiv:2603.05071. [Primary/source link](https://arxiv.org/abs/2603.05071).
- Already done / relevance: Explicit cellular-automaton motion maps and appearance interaction; explicit motion modeling is not new.
- Code: https://github.com/nliu-25/MI-DETR — paper-linked, not executed.
- Data: IRDST-H, DAUB-R, ITSDT-15K reported.
- Verification: Primary indexed metadata/abstract; full text not audited.

### C5 — IRDINO: Adapting DINOv3 with Second-Order Motion Awareness for Moving Infrared Small Target Detection

- Authors: Qian Xu; Shuaipeng Fan; Fei Gao; Mingjin Zhang.
- Date/venue: CVPR Findings 2026, not main-track CVPR.
- Identifier: DOI/arXiv not captured; stable CVF URL. [Primary/source link](https://openaccess.thecvf.com/content/CVPR2026F/html/Xu_IRDINO_Adapting_DINOv3_with_Second-Order_Motion_Awareness_for_Moving_Infrared_CVPRF_2026_paper.html).
- Already done / relevance: Second-order motion plus pretrained features is an additional temporal baseline.
- Code: Not verified; no runnable release established.
- Data: DAUB, ITSDT-15K, IRDST reported.
- Verification: Primary indexed metadata/abstract; full text not audited.

### C6 — TLCFormer: synergizing temporal motion and local contrast for robust infrared video small object detection

- Authors: Zihan Wang; Lun Li; Fan Bai.
- Date/venue: 2026-05-20, Scientific Reports.
- Identifier: 10.1038/s41598-026-53189-w. [Primary/source link](https://doi.org/10.1038/s41598-026-53189-w).
- Already done / relevance: Temporal motion and contrast fusion already target very small weak video objects.
- Code: Not verified; no runnable release established.
- Data: Availability/protocol not independently established.
- Verification: Primary indexed metadata/abstract; full text not audited.

### C7 — Temporal-Emerged Prompting for Segment Anything in Multiframe Infrared Small Target Detection

- Authors: Yinghui Xing; Donghao Chu; Shizhou Zhang; Di Xu.
- Date/venue: ICML 2026; preprint 2026-06-26.
- Identifier: arXiv:2606.27655. [Primary/source link](https://proceedings.mlr.press/v306/xing26b.html).
- Already done / relevance: Global/local motion discrepancy provides temporal prompts for SAM; temporal weak-evidence emergence is established.
- Code: Not verified; no runnable release established.
- Data: Availability/protocol not independently established.
- Verification: PMLR proceedings and arXiv metadata verified.

### C8 — DP–MHT–TBD: A Dynamic Programming and Multiple Hypothesis Testing-Based Infrared Dim Point Target Detection Algorithm

- Authors: Jinming Du; Huanzhang Lu; Luping Zhang; Moufa Hu; Yingjie Deng; Xinglin Shen; Dongyang Li; Yu Zhang.
- Date/venue: 2022-10-11, Remote Sensing.
- Identifier: 10.3390/rs14205072. [Primary/source link](https://www.mdpi.com/2072-4292/14/20/5072).
- Already done / relevance: Dynamic programming and multiple hypotheses accumulate trajectories before detection. A differentiable DP alone is incremental.
- Code: Not verified; no runnable release established.
- Data: Availability/protocol not independently established.
- Verification: Publisher indexed abstract; author list cross-check from DBLP, full text pending.

## High-priority unresolved references and coverage limitations

- X1: **Neural-Network-Based False Alarm Controllable Detector for Weak Targets via a Differentiable NP Loss**, Qu, Chen, Liu, Li, Wang; TAES 2025; [DOI lead](https://doi.org/10.1109/TAES.2025.3572501). Secondary records agree; primary access failed. Full technical comparison is mandatory before claiming novelty for Project B.
- [ECFNet author repository](https://github.com/IVPLabs/ECFNet): ECCV 2026 author claim, coarse/fine region proposals and distillation; authors Houzhang Fang, Ruixuan Huang, Qiuhuan Chen, Xiaolin Wang, Yi Chang, Luxin Yan. Proceedings DOI not independently captured. Add to candidate-stage baseline audit.
- [SPIRE author project](https://nirixiang.github.io/SPIRE-IRSTD/): ECCV 2026 claim; probabilistic point-response supervision and peak extraction. Full identifier and method comparison pending.
- [2026 graph-spectral TGRS paper](https://ieeexplore.ieee.org/abstract/document/11667104/): *Dynamic Spectral Graph Wavelet Transform and Iterative Reweighted Physical Residual Feedback for Infrared Small Target Detection*, DOI 10.1109/TGRS.2026.3727434. Publisher indexed existence verified, full author/code audit pending; not counted toward the ten A records.
- [MISA](https://ieeexplore.ieee.org/abstract/document/11646933/): DOI 10.1109/JSTARS.2026.3722252; imperfect-correspondence temporal fusion. Full author/code audit pending.
- [ReCIRC](https://arxiv.org/abs/2609.38112) and [Macro Mondrian CRC](https://arxiv.org/abs/2610.35035) are recent calibration leads. Do not claim adaptive/groupwise calibration novelty before reading them.
- A NeurIPS 2026 “under review” homepage listing is not an accepted paper. None is promoted to publication status here.

## Remaining verification gate

Obtain full texts and exact versions of A2, A1, B2, B4, B5, X1, C1 and C3; audit actual algorithms rather than abstracts. Record upstream commit SHAs and license scope for reused code. Download authorized datasets, hash split lists, inspect annotation conventions and duplicates, and compute target-size distributions. These are open tasks; the constrained prototype is useful even if subsequent review rejects its novelty.

## Follow-up source audit

The initial EDGRU retrieval failure was an access failure, not evidence of absent code. The release listing is now verified. SLS and SD loss source files were inspected; their exact blob identifiers and implementation caveats are in [upstream_audit.md](evidence/upstream_audit.md). This narrows the remaining baseline work to faithful integration and execution, not just locating code.

[Learn then Test, sections 2.2–2.3](https://arxiv.org/html/2110.01052v5) explicitly supplies bounded-risk tests and fixed-sequence threshold testing. Neither mechanism can be claimed as new here. The inspected HTML has inconsistent displayed date fields; retain the arXiv identity/version rather than using its page-render date as publication evidence. The unresolved TAES NP-loss full text remains a novelty blocker: secondary metadata alone is insufficient to exclude overlap.

The requested end-of-November 2026 coverage is future coverage relative to the supplied October 31 environment date. The date header has been corrected accordingly; this dossier does not claim a complete November search.