FSOD-VFM: Few-Shot Object Detection with Vision Foundation Models and Graph Diffusion
Authors: Chen-Bin Feng
Youyang Sha
Longfei Liu
Yongjun YU
Chi Man VONG
Xuanlong Yu
Xi SHEN
Venue: ICLR 2026 Poster (2026)
Keywords: Few-Shot Learning
Foundation Models
Object Detection
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: This work presents a state-of-the-art few-shot object detection method without training. The proposed graph diffusion based score reweighting provide a novel view of few-shot object detection.

Abstract: In this paper, we present FSOD-VFM: Few-Shot Object Detectors with Vision Foundation Models, a framework that leverages vision foundation models to tackle the challenge of few-shot object detection. FSOD-VFM integrates three key components: a universal proposal network (UPN) for category-agnostic bounding box generation, SAM2 for accurate mask extraction, and DINOv2 features for efficient adaptation to new object categories. Despite the strong generalization capabilities of foundation models, the bounding boxes generated by UPN often suffer from overfragmentation, covering only partial object regions and leading to numerous small, false-positive proposals rather than accurate, complete object detections. To address this issue, we introduce a novel graph-based confidence reweighting method. In our approach, predicted bounding boxes are modeled as nodes in a directed graph, with graph diffusion operations applied to propagate confidence scores across the network. This reweighting process refines the scores of proposals, assigning higher confidence to whole objects and lower confidence to local, fragmented parts. This strategy improves detection granularity and effectively reduces the occurrence of false-positive bounding box proposals. Through extensive experiments on Pascal-5$^i$, COCO-20$^i$, and CD-FSOD datasets, we demonstrate that our method substantially outperforms existing approaches, achieving superior performance without requiring additional training. Notably, on the challenging CD-FSOD dataset, which spans multiple datasets and domains, our FSOD-VFM achieves 31.6 AP in the 10-shot setting, substantially outperforming previous training-free methods that reach only 21.4 AP. Code is available at: https://intellindust-ai-lab.github.io/projects/FSOD-VFM.

Published as a conference paper at ICLR 2026
FSOD-VFM: FEW-SHOT OBJECT DETECTION WITH VISION FOUNDATION MODELS AND GRAPH DIFFU- SION
Chen-Bin Fengl.2; Youyang Sha" Longfei Liu", Yongjun Yu², Chi Man Vong'!, Xuanlong Yu 21, Xi Shen2t
University of Macau *Intellindust AI Lab
ABSTRACT
In this paper, we present FSOD-VFM: Few-Shot Object Detectors with Vision Foundation Models, a framework that leverages vision foundation models to tackle the challenge of few-shot object detection. FSOD-VFM integrates three key components: a universal proposal network (UPN) for category-agnostic bound- ing box generation, SAM2 for accurate mask extraction, and DINOv2 features for efficient adaptation to new object categories. Despite the strong generaliza- tion capabilities of foundation models, the bounding boxes generated by UPN often suffer from overfragmentation, covering only partial object regions and leading to numerous small, false-positive proposals rather than accurate, com- plete object detections. To address this issue, introduce novel graph-baset confidence reweighting method. In our approach, predicted bounding boxes are modeled nodes in directed graph, with graph diffusion operations applied to propagate confidence scores across the network. This reweighting process re- fines the scores of proposals, assigning higher confidence whole objects and lower confidence to local, fragmented parts. This strategy improves detection granularity and effectively reduces the occurrence of false-positive bounding box proposals. Through extensive experiments on Pascal-5', COCO-20', and CD- FSOD datasets, we demonstrate that our method substantially outperforms ex- isting approaches, achieving superior performance without requiring additional training. Notably, on the challenging CD-FSOD dataset, which spans multiple datasets and domains, our FSOD-VFM achieves 3 31.6 AP in the 10-shot setting, substantially outperforming previous training- free methods that reach only 21.4 github.
AP. Code is available at: https://inte http://inneelnnnuuuuutee inniinndunaaa io/p2ojectts/PSODD-U
INTRODUCTION
Few-shot object detection (FSOD) (Han al. 2022a; Wang et al.) 2020 Xiong 2023), the ability to detect objects with very few labeled samples, is critical task for real-world applications where labeled data are scarce or expensive to obtain. Precisely, FSOD, only small support set provides the limited labeled bounding boxes for each category, and the goal is to detect those categories in unseen images. For instance, in a K-shot setting, only K-annotated bounding boxes per class are provided. The demand for efficient and generalizable solutions in fields like autonomous driving, robotics, and medical imaging has rendered this task increasingly critical.
Recent years, vision foundation models (VFMs), which pre-trained on large-scale datasets, have demonstrated remarkable success across a wide range of vision tasks (Radford et al., 2021 Oquab al., 2023) Siméoni et al. 2025 Jiang al. 2024b; Ravi al., 2024). These models, mostly based on transformer archtecture (Vaswani et al.] 2017: Dosovitskiy et al. (000)), have set new bench- marks in tasks like object detection (Liu al. 2024: Siméoni al. (0255), segmentation (Siméoni et al., 2025)), and image classification (Radford et al. 2021 Siméoni et al. 2025). Their ability to generalize across diverse tasks makes them appealing for solving complex vision challenges, partic-
"Equal contribution. Corresponding author.

---

Published as a conference paper at ICLR 2026
154 40-
(a) Input
(b) w/o Graph Diffusion
(c) Graph Diffusion 30 Steps
(d) w/o Graph Diffusion
(e) Graph Diffusion Step
(1) Graph Diffusion 30 Steps
Figure 1: Effect of graph diffusion. The first shows: (a) an input sample (Figure Tab, (b) its
prediction without graph diffusion (Figure [b), and (c) the final result after applying graph diffusion for 30 steps (Figure Ic). The second row illustrates how the distribution of high-quality boxes (IoU with any ground truth > 0.75) and low-quality boxes (IoU with any ground truth < 0.1) evolves: (d) without graph diffusion (Figure Idh. (e) after step (Figure Tep, and (f) after 30 steps (Figure If).
ularly in settings that require minimal task- supervision. Beyond the benefits of fine-tuning for downstream tasks, vision foundation models also enable certain tasks to be performed in a fully unsupervised, zero-shot manner without any additional training. For example, DINO (Caron al., 2021) can co-segment objects directly from extracted features (Amir et al. 2021 Wang et al. 2023). Likewise, combining SAM2 (Ravi et al., 2024) with Kalman Filter yields a strong tracker that re- mains robust under occlusion (Yang et al. 2024), all without task-specific training.
An interesting research question arises: can powerful vision foundation models be directly applied to complex tasks such as object detection with only few-shot annotations, and even without additional training? In this work, we explore this question by leveraging foundation models to construct a uni- versal few-shot object detector. Specifically, we build on three recent advances: universal proposal network (UPN) (Jiang al., 2024b) for category- agnostic bounding box generation, SAM2 (Ravi) et al. 2024) for accurate mask extraction, and DINOv2 (Oquab et al., 2023) features for efficient adaptation to novel categories. However, directly applying this pipeline reveals a major limitation: bounding box overfragmmentation. UPN often generates boxes that capture only small salient parts of objects rather than the full object, producing many redundant proposals for object fragments (See Figure Tb). This overfragmentation not only inflates false positives but also undermines de- tection accuracy and reliability. To overcome this issue, we propose a novel graph diffusion based confidence refinement method that reweights bounding box proposals by exploiting their structural relationships, effectively suppressing local fragments and emphasizing complete objects.
Our graph diffusion approach represents predicted bounding boxes as nodes in a directed graph. Heat is propagated from one node to another when the latter not only receives a higher confidence score from UPN (Jiang et al., 2024b) but is also substantially overlapped by the former. By constructing this graph and performing diffusion over it, we propagate and refine decay scores across proposals. This formulation effectively captures relationships among candidate regions, promoting proposals that cover entire objects while suppressing fragmented parts. As a result, the detector assigns higher confidence to complete object regions and lower confidence to local parts, improving detection gran- ularity. This effect is illustrated in Figure la lb and [c where bounding box scores are visualized through transparency, showing how over- fragmeeeeett proposals are strongly suppressed after diffu- sion. A broader view on the first split of P (Everingham al. 2010) under the 1-shot setting (Figures Id] Ic and Ii] further demonstrates that high-quality boxes (IoU with any ground truth > 0.75) retain their scores, while low-quality boxes (IoU with any ground truth < 0.1) are significantly
2

---

Published as a conference paper at ICLR 2026
reduced. Extensive experiments Pascal-5' Everrngham et al. 2010). COCO-20' (Kang al. 2019 Lin et al., 2014), and CD-FSOD (Xiong, 2023 demonstrate that our method delivers signifi- cant improvements over previous approaches, enhancing performance without requiring additional training. Notably, on the challenging CD-FSOD dataset, which integrates six datasets from diverse domains, our PSOD-VFM achieves 31.6 AP in the 10-shot setting, substantially surpassing previous training-free methods that achieve only 21.4 AP.
In summary, our contributions are threefold:
We present FSOD-VEM, few-shot object detector that leverages vision foundation models to enable detection without any additional training.
We design a graph diffusion- confidence reweighting mechanism that effectively al- leviates proposal over-fragmenn wrr-fraagmetttion and enhances detection granularity.
We conduct comprehensive experiments across multiple benchmarks, demonstrating that our method consistently and substantially outperforms existing approaches.
Our code is available at: httos///neelllcdos-aaa-aaa.a.aa wtwpchuub.i/prrojeee.. F VEM
2 RELATED WORK
Few-shot object detection. Few-shot object detection (FSOD) aims to detect unseen objects with
limited labeled examples, addressing the challenge of generalizing to novel categories with only a few training samples. Transfer-leaming methods (Chen et al.) 2018 Wang et al.] 20200] Qiao eet al. 2021; Sun al., (021) leverage model traited abundant base classes and adapt it to novel categories using fine-tuning schemes. Specifically, TFA (Wang et al. 2020) illustrates that fine-tuning only the final layers of pre-trained model can effectively harness the capabilities of existing object detectors with minimal modifications. Meta-learning based approach also plays central role in FSOD, Meta R-CNN (Yan al.) 2019) incorporated meta-learing into two-stage detectors by designing class-aware attention mechanisms and dynamically reweighting features for novel categories. FSDetView (Xiao et al., 2022) introduces feature aggregation to exploit the rich feature information shared from base classes. DE-ViT (Zhang al. 2023) classifies proposals using similarity maps with support prototypes, rather than raw visual features, to improve generalization to novel classes. FM-FSOD (Han & Lim 2024) achieves strong performance in few-shot object detection by leveraging a foundation model together with a large language model (LLM). Yet, all aforementioned methods require training. More recently, No-time-to-traie (Espinosa et al. 2025) leverages a training-free strategy based on vision foundation models, demonstrating superior per- formance compared to training-based methods. In this work, we further explore training-free FSOD by integrating multiple vision foundation models and developing more effective ways of leveraging them, and pushing the frontier of this domain.
Vision foundation models. Vision foundation models have rapidly evolved and reshaped the downstream vision tasks. Thanks to the DINO series (Caron al. 2021 Oquab et al. 2023] (Siéénni et al. 2025), large-scale self-supervised learning has endowed the vision transformers with strong representation capabilitics, enabling superior performance on tasks such as image classification and salient object segmentation with minimal adaptation. Furthermore, task-oriented foundation models have also emerged. Segment Anything Model (SAM) (Kirillov et al. 2023) and SAMv2 (Ravi et al.) 2024) introduce universal segmentation paradigm through interactive visual prompting and gener- ate class-agnostic segmentation masks for the prompted areas. On the other hand, detection-oriented models such as the Universal Proposal Network (UPN) (Jiang al. 2024b), which is based on de- tection transformers (Carion et al., 2020: Jiang et al. 2024a), can provide class-agnostic bounding boxes for the given image in coarse or fine- tin-grained objectness modes. By unifying the complemen- tary powers of diverse vision foundation models, our approach pushes the boundaries of zero-shot and few-shot object detection, delivering state-of-the-an performance without single extra training step.
Training-free applications with vision foundation models. Beyond transfer learning, vision foundation models also enable powerful training-free applications. DINO (Caron et al. 202T)
3

---

Published as a conference paper at ICLR 2026
Input
Output
Query Matching
UPN
ROk-aaed
Box Proposal
Query
ROH-based Feat. Extract.
Graph Diffusion
Law connectiity
Han correctiity
SAM2
Support
Protocype
Figure 2: Overview FSOD- VFM. Our method integrates UPN, SAM2, and DINOv2 generate bounding box proposals and perform query matching. We build graph and perform graph diffusion to mitigate over-fragmentation. The over-fragmented box regions appear more transparent after graph diffusion, indicating that their confidence has decayed.
veals object structures directly from attention maps, while LOST (Siméoni et al. 2021 and Token- Cut (Wang al., 2023) enhance unsupervised object discovery, even in videos. Parts likewise be segmented without supervision (Amir et al., (0211, and SAM2 (Ravi et al., 2024) combined with a Kalman Filter yields robust zero-shot tracking Yang al., 2022).. These studies show that foundation models can solve complex tasks without task-specifer training. Motivated by this, we design training-free few-shot object detector. While concurrent work (Espinosa et al.] (202)) also explores this direction, we demonstrate that stronger graph-based refinement yields substantially better performance.
3 METHOD
We propose FSOD-VFM, framework designed for few-shot object detection. The method builds on three complementary vision foundation models: the Universal Proposal Network (UPN) (Jiang al. 2024b) generate category-agnostic bounding boxes, SAM2 (Ravi al., 2024] extract accurate masks based on these boxes. and DINOv2 (Oquab et al., 2023) the feature extractor to obtain rich representations for both support and query objects. In the K-shot setting, first denote the support set which contains K bounding box annotations:
S (s
s' s'= 21 vi 2 c2)
where (xi, vi. 22., vL) specifies the bounding box coordinates in image 1', and et E {l....,Cy denotes class label among C categories.
An overview of the FSOD-VFM pipeline is shown in Figure For each annotation sf in the sup- port S, we extract feature representations for the exact object region using SAM2 and DINOv2 according to the provided ground truth bounding boxes, and aggregate these features according to their category labels to form category-level prototypes. For each query image, the UPN generates bounding box region proposals, from which features are extracted and matched against the proto- types produce class predictions and the corresponding matching scores. Finally, we propose to apply graph diffusion mechanism refine the scores of the bounding boxes, which helps suppress over- frammeraeeeteed bounding boxes.
The section is organized as follows: Section 3.1 introduuces the baseline, which leverages UPN (Jiang et al., 2024b), SAM2 (Ravi et al. 2024), and DINOv2 (Oquab et al. (2023) to construct category- level matching and perform bounding box classification. Section 3.2 details the graph diffusion procedure as well as the implementation details. Detailed pseudo-code for our FSOD VFM method provided in Appendix Section A.1

---

Published as a conference paper at ICLR 2026
3.1 BUILDING FEW-SHOT OBJECT DETECTORS WITH FOUNDATION MODELS
ROI-based feature extraction. For given annotation s', we perform Region-of-Interest (Rol) based feature extraction obtain object-specific representations. Specifically, given bounding box in 8f, we first generate a binary object mask M in image I using SAM2 (Ravi et al., 2024). Next, we extract dense feature map from the whole image I' E R3x with DINOv2 (Oquab al. 2023) to obtain F E CR xW" where we do additional downsampling from the original size. The bounding box is then mapped to the coordinates of the feature map. By interpolating M
the feature map resolution, we obtain Maownn which allows us to pool features focusing only on the object region inside the bounding box:
1/2
Fing U, 2) M [u, v), N mask
Mdown U, v).
(1)
N mask
u= V/ 2=
w= e=
Building prototype features. After obtaining Rol-based features {F)} in i-th annotation from the support set, we aggregate them to form class-level prototypes. Specifically, for each class E (1.-.., C), collect all support features that belong class c: Fe (Fi c === c). The prototype of class e is then computed as the mean feature Pe = F. E5eF, followed by Ey-normalization TPella These normalized prototypes {pe} serve the category-level
ensure unit length: Pe-
Ph.
representations for matching query proposals.
Matching query proposals. For each query image, we first employ UPN (Jiang et al., 2024b) to generate bounding box proposals (x), vi oz v along with their corresponding class-agnostic object detection scores sunn- We extract Rol-based features using SAM2 and DINOv2 for each proposal, yielding feature representation F/ for proposal j. We can achieve the class prediction for each proposal j by calculating the cosine similarity between the proposal feature and the category- level support prototypes }
G arg max cos( (F) Pe)
Finally, for the j-th proposal, we record its feature P.. predicted class a³ class-agnostic detec- tion score supn and correponding mask M1, These quadruples (E e sipp M serve as inputs for the subsequent graph diffusion step, which refines the bounding box scores and reduces over- fragmentation.
3.2 GRAPH DIFFUSION
While UPN is effective at generating bounding box proposals, it may produce fragmented detections for single object. To address this issue, we adopt graph-based based approach.
Construction of the graph. We construct directed graph g = (V,E), where V and & denote the sets of vertices and edges, respectively, and the graph has N nodes, i.e., |V N, indicat- ing N proposals within the same class in the query image. The j-th node stores the quadruple (F)) é² supn, MP). The edge connecting from to is defined follows:
0, Area(M n no) Ar)a(M))
if sypn supn:
g/. =
otherwise.
The edge Eij characterizes the diffusion of energy from node i to node j. Nodes with higher UPN scores are treated as high-quality proposals and thus retain their energy without diffusion. In contrast, nodes with lower UPN scores diffuse their energy toward more confident nodes. The diffusion factor is determined by the spatial coverage of node over node i, following the idea of suppressing fragmented proposals while consolidating coherent object regions, inspired by Espinosa et al. (2025).
5

---

Published as a conference paper at ICLR 2026
Novel Split
Novel Split 2
Split
Method
Nuvel.
2
3
5
10
2
3
5
10
3
5
10
Avg
PeDerView (Xio
2022
20.4
37.4
36.1
42.3
21.7
22.6
25.6
29.2
32.4
19.0
29.8
33.2
39,8
29.2
TFA (Wang ata
20/00
36.1
44.7
55.7
5600
23.5
26.9
34.1
35.1
39.1
30.8
34.8
42.8
49.5
49.8
39.9
Retentive SONIEERCNN RCNN (FSa 2021
2021
42.4
45.8
45.9
53.7
56.1
21.7
27.8
35.2
37.0
40.3
30.2
37.6
43.0
49.7
50.1
41.1
DiGea et
37.9
39.4
48.5
58.6
61.5
28.9
41.9
42.1
49.1
46.9
52.7
54.7
44.0
HeteroGapii Rinh
42.4
51.9
55.7
62.6
63.4
25.9
37.8
46.6
48.9
51.1
35.2
42.9
41.8
5488
53.5
48.0
Meta Faster R-U.NN Hiin el 2022e
43.0
54.5
60.6
66.1
65.4
27.7
35.5
46.1
47.8
51.4
40.6
46.4
53.4
59.9
58.6
50.5
CrosTransffomee
20220
57.9
63.2
57.0
LVC (Kaul al 2022)
51.5
53.2
58.8
63.2
65.7
32.8
29.2
50.7
49.8
50.6
48.4
52.7
55.0
59.6
59.6
52.3
NIFF Liinguus et a.. 2023
62.8
67.2
68.0
70.3
68.8
38.4
42,9
54.0
56.4
54.0
56.4
62.1
61.2
64.1
63.9
59.4
Multi-Relation Det
(Fan 2020aa
51.6
56.5
DE-VIT (VIT-S/14)
ZZhang et al. 2023
X
47.5
64.5
57.0
65.5
67.3
43.1
34.1
49.7
56.7
60.8
52.5
62.1
60.7
61.4
64.5
56.7
DE-VIT (VIT-B114)
Zhang et al.
2125
56.9
61.8
68.0
73.9
728
45.3
47.3
58.2
89.8
60.6
58.6
62.3
62.7
64.6
67.8
614
DE-VIT (ViT-L/14) Ziaan
61.6
No-Time To-Tm---T-Taaa
Espaaosa el al 2025
70.8
72.3
73.3
77.2
79.
54.5
67.0
76.3
75.9
78.2
61.1
67.9
71.
70.8
72.6
71.2
FSOD-VEM
77.5
82.3
83.0
85.8
85.8
64.8
68.0
77.4
79.5
81.6
65.3
75.1 78.7
78.2
79.3
77.5
Results for competing methods are taken from Zhang et al
2023)
with the best highlighted in bold.
Table 1: Results on Pascal-5' (Everingham et al., 2010). We report nAP50, i.e., the average precision at IoU 0.5 on novel classes.
Graph diffusion. The diffusion process follows scheme inspired by the classic PageRank algo- rithm (Brin & Page 1998). Each node is assigned a prior weight w, where
w = max (E*
(4)
which reflects the strongest outgoing relation of node i to other nodes. The initial state is set to σ [ N:.... N. _ and the transition matrix P is obtained by row-normalizing E. The iterative diffusion is then defined as
++1 = a x π + (1 aa
(5)
where is the restart probability that balances global exploration with personalized propagation, and @ denotes point-wise multiplication. To improve efficiency, we employ early stopping when | -'1l <T.
After convergence, the stationary distribution क serves as a confidence weighting vector. For our task, what we ultimately need is measure that penalizes low-score nodes. Therefore, after obtaining the stationary distribution सt, we apply a simple transformation, which consists of taking its negative and adding 1, to convert it into penalty score. The score of the j-th proposal is defined as
f = +,,*** max cos(F), sos(F./.e). (o),
(6)
where proposals with high diffusion scores are downweighted, while confident and coherent propos- als are preserved. The À is hyperparameter that controls the strength of decay. The final prediction for the j-th proposal consists of the class éi in Equation 2 and the score fs.
Comparison between Graph Diffusion and NMS Although both Graph Diffusion and Non- Maximum Suppression (NMS) reduce redundant proposals, their underlying principles differ funda- mentally. NMS makes hard suppression decisions based solely on bounding box IoU and confidence scores. If two boxes overlap beyond a fixed threshold, the lower-score box is directly removed. This binary rule often suppresses valid detections when objects overlap or when bounding boxes are im- perfect. Graph diffusion, in contrast, does not remove boxes based thresholds. Instead, it refines proposal scores by propagating information between proposals using mask-level relationships from SAM2 (Ravi al. 2024), which capture object boundaries much more accurately than bounding boxes. Through diffusion, unreliable proposals naturally receive lower scores, whereas reliable ones gain consistent support.
3.3 IMPLEMENTATION DETAILS.
We adopt SAM2-L (Hierarchical ViT) (Ravi et al. 2024) for mask extraction and DINOv2-L (Oquab
et al., 2023) for feature extraction. During preprocessing, DINOv2 resizes all input images to 630 630, which is aligned with its pretrained patch size of 14 (since 630 is multiple of 14). We additionally utilize DINOv3 (Siméoni et al. (2025), which accepts 624 x 624 input images, consistent with its patch size of 16. An ablation study on different feature extractors is presented in Section 4.2

---

Published as a conference paper at ICLR 2026
Method
ET Novel.
10-shot
30-shot
nAP
nAPS0
DAPS
DAP
nAP50 DAP75
TFA (Wang et al.
2020
10.0
19.2
9.2
13.5
24.9
13.2
FSCE [Sun
10.5
16.4
16.2
Retentive RCNN Fan al. 2021
10.5
19.5
9.3
13.8
22.9
13.8
HeteroGraph (Han ct 2021
11.6
23.9
9.8
16.5
31.9
15.5
Meta E R-CNN (Han el al. 2022a
25.7
10.8
31.8
15.8
(KCl et al.
19.0
34.1
19.0
26.8
45.8
27.5
C. TTanstormer (Han cl al
2022b
17.1
30.2
17.0
21.4
35,5
22.1
NIFF (Guirguis et al. 2025
20.9
DiGeo (Ma al.
10.3
18.7
9.9
14.2
26.2
14.8
CD-VITO(ViFLL)
al. 2024
V
35.3
54.9
37.2
35.9
54,5
38.0
FSRW (Kang et al.
2019
X 5.6
12.3
4.6
9.1
19.0
7.6
Meta R-CININ YYan al. 2019
6.1
19.1
6.6
9.9
25.3
10.8
DE-VIT (VIT-L. ZZanng 2023
53.0
37.0
52,9
37.2
No- To-Tiie-TooTrisee (Espinosa al. 2025
X
36.6
54.1
38.3
36.8
54.5
38.7
FSOD-VFM
X
44.0
59.4
47.6
45.8
61.9
49.4
Results for competing methrds taken from Fu al.
2024). with the best highlighted in bold.
Table 2: Results on COCO-20 (Kang et al., 2019; Lin et al., 2014). We report nAP (IoU thresh- olds 0.5 0.5-0.95), nAP50 (IoU 0.5), and nAP75 (loU threshold 0. 0.75) on novel classes.
Mithod
ArTasDe
Clip
Deep
LODD
IIA wices
Wong
2000
31/88/148
80/1K1/205
44/87/1118
R
33/102/159
86/182/21.9
39/98/1200
DeHRCN
2021
36/99/155
93/189/229
45/99/12.1
Didll-atee
Xeny
5.1/125/1R1
26/23.3/223
10.5 19.1 (365
-1155/155
1/16//211
59/122/145
201
MTD
30/4
38/2299/24
61/263/256
124/2533/94
09290163
24/135/158
40/111//15
56/1699/94
Deni-FF
32/87/120
15.1/02//223
4./122//55
9./163//199
38/14.1168
42/144
66/133//65
DE-WI-TT
Zhing
3025
105/380749.2
130/381800
167/23/4/266
193/212/213
06/78/88
24/50/54
12.1/223/222
CD-VITO
310/4479/60.5
19.9/411/443
178/269
203/223/223
16/14/113
X1768/20
13.97661220
Misure
Lu
26.//63//733
20.1/551/499
206/521/
242/295/341
9.1/190/237
10/196/221
/8/2/44//98
McNN Yu
28/85/140
78/177//06
36/88/1112
Detk Zhou et N02
06/06/08
114/11//114
0.1/0.1/011
09/09:09
00/00/000
0.0/0.000000
22/22/22
Zhug aial
04/10/192
05/55/110
27/78184
04/25/2.
04/15/18
15/3.1/
10/5./39
No Time To Tran Espinos et 2025
282/357/33.0
189/249/259
18.518857144
305/269/296
55/52/55
100:202/160
18.0/224/114
514/620/60.5
291/437//65
183/238/225
350/339/34
59/74/7.2
118:17.7/155
253/313/316
indicates Distll-CD-FSOD (Xiong 2023 results, and denotes CD-VITO Fu ct al. 2024 results. Best results are in bold.
Table 3: Results the CD-FSOD benchmark (Xiong, 2023). We report nAP (loU thresholds 0.5-0.95) for all datasets, with each entry showing results for 1-shot, 5-shot, and 10-shot.
For proposals generated by UPN, we use "coarse" as the text prompt, discard bounding boxes with confidence scores below 0.01, and retain most 500 proposals per image accelerate inference. For the final predictions, we follow [Espinosa et al. (2025) and output the top 100 bounding boxes per image ranked by confidence. In the graph diffusion process, we a 0.3 and À 0.5, with detailed study of these parameters provided in Section 4.2) We set the early stopping threshold r to le-6. In terms of efficiency, inference on a single image takes approximately 2.4 seconds on single NVIDIA A40 GPU without additional optimization.
4 EXPERIMENT
In this section. provide experimental results. We evaluate our approach on Pascal-5' (Evering- ham et al. 2010), COCO-20' (Kang et al. 2019 Lin et al. 2014), and CD-FSOD (Xiong 2023)
For evaluation, we follow prior work (Xiong 2023 Espinosa et 2025) and adopt the novel Av- erage Precision (nAP) metric. This metric computes the mean Average Precision (mAP) over novel classes, which has become the standard evaluation protocol in few-shot object detection (Wang et al. 2020). The mAP is defined as the average precision across loU thresholds from 0.5 to 0.95, following the COCO convention (Lin et al. 2014). For Pascal-5 we report nAP50, i.c., the AP at a single IoU threshold of 0.5 on novel categories, consistent with previous works (Wang et al. 2020 Han et al. 2022a Xiao et al. 2022). Note that on Pascal-5 and COCO-20', most training-bases approaches (Wang al. 2020 Xiao et al. 2022) rely training with both base classes and a few samples of novel classes. In contrast, our setup involves no additional training, making the task substantially more challenging. The description of datasets are detailed in Appendix A.3
7

---

Published as a conference paper at ICLR 2026
1-Shot Support
Our Prediction
1-Shot Support
Our Prediction
ArTaxOrr
Clipart 1K
UODD
FISH
pnceedian racsa
0.92 inclus
inclus
indus on
DIOR
Figure 3: Qualitative results our method on CD-FSOD (Xiong 2023) dataset. Our method is shown to achieve precise object detection and classification across varied domains, with the sole requirement of one label per class.
4.1 COMPARISON STATE- OF-THE-ART METHODS
Results on Pascal-5' Evvringhhm et al., 2010) and COCO-20' (Kang et al.. 2019; Lin et al., 2014). We present results on Pascal-5' and COCO-20' in Table and Table 2 comparing with methods trained on novel classes and methods not trained on novel classes. Under the same training- free setting, method delivers significant and consistent improvements over the recent concurrent work No- Time- To- Train (Espinosa et al.) 2025). Notably, it also outperforms many training- based approaches by large margin. These results demonstrate the effectiveness of the proposed FSOD- VFM, showing that with pre-trained large vision foundation models, carefully designed strategies yield strong few-shot object detection performance.
Results CD-FSOD (Xiong, 2023). We report experimental results the more challenging CD-FSOD dataset in Table 3 where our method achieves consistent performance gains over all training-free baselines. Even in comparison with methods fine-tuned novel categories, our ap- proach remains highly competitive. This superiority is particularly pronounced in the one-shot set- ting. Our method delivers exceptional performance ArTaxOr and Clipart1k, two datasets whose domains are close to natural images. In contrast, NEU-DET, Deep Fish, and UODD pose sub- stantially greater challenges: these benchmarks involve detecting low-saliency defect objects in microscopic textures or blurred targets in underwater scenes. For such domain-speciff scenarios, fine-tuned methods yield better performance than our training-free approach, which is expected trade-off for not using target domain fine-tuning. Notably, Mixture (Liu et al. 2025) leverages DINO Detector (Zhang al., 2022) and DINOv2 (Oquab al., 2023), which are both pretrained on large- scale corpora and further fine-tuned target domains. It achieves nate-offtheeart performance on this dataset. Even so, our FSOD- VFM maintains a performance advantage over Mixture in the one-shot case, which underscores the strong robustness of training-free free framework. Qualitative visual examples in Figure 3 and segmentation examples in Figure iillustrate the inherent difficulty and rich domain diversity of the CD-FSOD henchmark.
8

---

Published as a conference paper at ICLR 2026
Mraneae 0.84
MONKUTTer 0.70
sefe 0.9
BAR
6.31
ArTaxOr
Clipart 1K
UODD
Inclan usion
3
NEU-DET
DIOR
FISH
Figure 4: Segmentation visualization of CD-FSOD Xiong, 2023). SAM2 delivers reliable seg-
mentation results across most scenarios (c.g., insect-related, cartoon, and deep-sea scenes).
Eqn.
Eqn,
Eqn.
/mm)
Method
Pascal-S:
COCO-20
1.0
0.0
30
76.8
59.7
2,42
w/o Refine. (A
7.4
9.9
0.3
0.3
30
72.0
59.5
2.42
0.5
0,3
30
77.5
59.4
2,42
Soft NMS [Bddla et al. 2017
28.1
26.6
0.3
30
59.0
2,42
(Saloryer et
27.8
3,43
2,42
Soft Merging Espuiossa 2025
66.0
5044
0,5
30
71A
593
2,42
+
Gruph Diffusion (Ours)
77.5
59.4
03
0.3
78.5
59.1
2.37
GGaaph Diffusion NMS
0.
0.3
72.5
59.4
2.39
0.5
0.3
10
77.5
59.4
2.39
Table 4: Post-processing comparison of nAP50 Pascal-5' (1-shot) and COCO- 20' (10-shot).
59.4
2.42
2.48
Table 5: Impact of and of nAP50
Pascal-5' (1-shot) and COCO-20 (10-shot).
4.2 DISCUSSION AND ANALYSIS.
this section, we analyze the impact of different modules FSOD-VFM. All experiments are conducted on Pascal-5' splitl under 1-shot setting and COCO-20* under the 10-shot setting.
Graph diffusion versus other post-processing. We compare the proposed graph diffusion to other post-processing approaches, which including: Mon-Maximum Suppression (NMS), Soft NMS (Bodla al., 2017). Weighted-Box--ussie Box-Fusion (WBF) (Solovyev al., (201)), and Soft Merg- ing (Espinosa et al., 2025). The results are provided in Table [ We demonstrate that our graph diffusion approach achieves significantly better performance. In addition, graph diffusion in- tegrated with NMS delivers marginally inferior performance. This observation demonstrates that NMS is necessary for the reweighting pipeline proposed in this study.
Hyper-parameters in graph diffusion. We analyze the impact of key hyperparameters in the graph diffusion process, including the decay factor in Equation [6] the restart probability in [q and the number of diffusion steps in Equation 5 The results are summarized in Table From the table, we can observe that our method exhibits low sensitivity to these hyperparameters, resulting in relatively consistent performance across different settings. Among them, 0.5,
0.5. 0.3 for all
0.5 yield the best performance, datasets. Additionally, performance improves with increasing diffusion steps and stabilizes after the fifth steps, indicating approximate convergence. The diffusion process fully converge around 70 steps (illustrated in Appendix (A.2). To balance between computation time and stable performance, adopt 30 diffusion steps in all our experiments.
0.3
choose À = 0.5.

---

Published as a conference paper at ICLR 2026
Backbone
V2-S-Reg
v2-B-Reg
v2-1.
V2-1.Reg
v2-G-Reg
v1-Splus
v3-8
v3-1.
x3-Hplus
Pascal-5'
73.1
66.7
81.2
69.0
77.5
73.5
69.2
64.9
64.4
71.8
75.0
71.6
58.7
COCO-20
52.5
47.2
53.3
59.4
56.4
48.3
52.2
55.5
56.2
45.5
Table 6: Ablation results of nAP50 on Pascal-5' (1-shot) and COCO-20' (10-shot) on different
backbone choices for FSOD-VFM. 'RReg" denotes the backbone with register (Oquab al. 2023
Darcet et al. 2023).
Impact of feature extractor. In the proposed FSOD-VEM, adopt DINOv2-L without reg- istration as our feature extractor. We conduct a full ablation study on different backhone choices, including various sizes of DINOv2 (Oquab et al. 2023), DINOv2 with registers (Darcet al. 2023), and the more recent DINOv3 (Siméoni et al., 2025). The results are summarized in Table 6 The DINOv2-B achieve the best performance in Pascal-5' splitl (1-shot). However, its overall perfor- mance is weaker than DINOv2-L (illustrated in Appendix A.4)
Limitations. Despite the significant improvements achieved by FSOD-VFM, our approach has several limitations. First, the performance gain decreases as the number of annotated samples in- creases; for example, the improvement from 5-shot to 10-shot marginal compared to that from 1-shot to 5-shot. Second, FSOD- VFM incurs a relatively high inference cost, as it relies on mul- tiple foundation models and is not optimized for real-time applications. Nevertheless, the method requires very few annotations and no additional training, making it powerful tool for data analysis and applicable across diverse domains.
CONCLUSION
This paper proposed FSOD-VEM, a few-shot object detection framework leveraging vision founda- tion models, integrating category-agnostic UPN for class-agnostic bounding box generation, SAM2 for mask extraction, and DINOv2 for feature extraction. To address bounding box overfragmmentation in the prediction, characterized by partial object coverage and excessive false positives, we designed a graph- confidence reweighting method. This method modeled predicted boxes as nodes in a directed graph, then employed graph diffusion to propagate confidence scores. This graph diffusion- based approach elevates scores for whole-object proposals while lowering those for fragmented local parts, thereby enhancing detection granularity and reducing false positives. Experiments on Pascal- 5", COCO-20', and CD-FSOD showed that FSOD-VEN outperformed existing methods without extra training. We believe this work offers novel perspective leveraging foundation models to address visual tasks in a training-free free paradigm.
Acknowledgment We thank Intellindust for their support, in particular Caizhi Zhu and Xiao Zhou. This work was supported part by the Shenzhen Science and Technology Innovation Committee under Project SGDX202205301 1001006, and in part by the Science and Development Fund of Macau under Grants 01 1//2024/RIAA and 0216//02//AGJ.
ETHICS STATEMENT
We declare that they do not find any potential violations of the ICLR code of ethics.
REPRODUCIBILITY STATEMENT
Our implementation details are provided in Section 3.3 including all necessary hyperparameters.
A general algorithm is presented in Appendix A.I All datasets used in our work are publicly available: Pascal VOC can be accessed at wtt.t/hhttt..tt
uk httl/aacca///e and CD-FSOD can be downloaded from
COCO available at wttpps://ccoattast...t..
https://yugiin httpa//aannooooooooooooooaaaaaae
10

---

Published as a conference paper at ICLR 2026
REFERENCES
Shir Amir. Yossi Gandelsman, Shai Bagon, and Tali Dekel. Deep vit features as dense visual de- scriptors, arXiv preprint 12.05:211 2(3):4 2021.
Navaneeth Bodla, Bharat Singh, Rama Chellappa, and Larry S Davis. Soft-mms-improving object detection with one line of code. In Proceedings of the IEEE international conference on computer vision, pp. 5561-5569, 2017.
Sergey Brin and Lawrence Page. The anatomy of a large-scale hypertextual web search engine. Computer networks and ISDN systems, 30(1-7):007-117, 1998.
Nicolas Carion, Francisco Massa, Gabriel Synnaeve. Nicolas Usumier, Alexander Kirillov, and Sergey Zagoruyko. End-to-end object detection with transformers. In European conference on computer vision, pp. 213-229. Springer, 2020.
Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin. Emerging properties self-supervised vision transformers. Proceedings of the International Conference on Computer Vision (ICCV), 2021.
Hao Chen, Yali Wang, Guoyou Wang, and Yu Qiao. Lstd: A low-shot transfer detector for object detection. In Proceedings of the AAAI conference artificial intelligence, volume 32, 2018.
Timothée Darcet, Maxime Oquab, Julien Mairal, and Piotr Bojanowski. Vision transformers need registers. arXiv preprint 1688,68.00,,6888 2023.
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al. An image is worth 16x16 words: Transformers for image recognition scale. arXiv preprint arXhv:2010.11929, 2020.
Miguel Espinosa, Chenhongyi Yang. Linus Ericsson, Steven McDonagh, and Elliot J Crow- ley. No time to train! training- free reference-base instance segmentation. arXiv preprint M0Xi:22007.0279.. 2025.
Mark Everingham, Lue Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman. The pascal visual object classes (voc) challenge. International journal of computer vision, 88(2): 303-338, 2010.
Qi Fan, Wei Zhuo, Chi-Keung Tang, and Yu-Wing Tai. Few-shot object detection with attention-rpn and multi-relation detector. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 4013-4022, 2020a.
Qi Fan, Wei Zhuo, Chi- Tang, and Yu- Wing Tai. Few-shot object detection with attention and multi-relation detector. In Proceedings of the IEEE/CVF conference on computer vision and paltern recognition, pp. 4013-4022, 2020b.
Zhibo Fan, Yuchen Ma, Zeming Li, and Jian Sun. Generalized few-shot object detection without for- getting. In Proceedings of the IEEF/CVF conference compuler vision and pattern recognition, pp. 3527-4536, 2021.
Yuqian Fu, Yu Wang. Yixuan Pan, Lian Huai, Xingyu Qiu, Zeyu Shangguan, Tong Liu, Yanwei Fu, Lue Van Gool, and Xingqun Jiang. Cross-domale few-shot object detection via enhanced open-set object detector. In European Conference Computer Vision, pp. 247-264. Springer, 2024.
Karim Guirguis, Johannes Meier, George Eskandar, Matthias Kayser, Bin Yang, and Jürgen Bey- erer. Niff: Alleviating forgetting in generalized few-shot object detection via neural instance feature forging. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 24193-24202, 2023.
Guangxing Han and Ser-Nam Lim. Few-shot object detection with foundation models. In Pro- ceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 28608- 28618, 2024.
11

---

Published as a conference paper at ICLR 2026
Guangxing Han, Yicheng He, Shiyuan Huang, Jiawei Ma, and Shih-Fu Chang. Query adaptive few- shot object detection with heterogeneous graph convolutional networks. In Proceedings of the IEEE/CVF International Conference Computer Vision. pp. 3263-32777 2021.
Guangxing Han, Shiyuan Huang, Jiawei Ma, Yicheng He, and Shih-Fu Chang. Meta faster r-cnn: Towards accurate few-shot object detection with attentive feature alignment. Proceedings of the AAAI conference on artificial intelligence, volume 36, pp. 780-789, 2022a.
Guangxing Han, Jiawei Ma, Shiyuan Huang, Long Chen, and Shih-Fu Chang. Few-shot object detection with fully cross-transformer. In Proceedings of the IEEE/CVF conference computer vision and pattern recognition, pp. 5321-5330, 2022b.
Qing Jiang, Feng Li, Zhaoyang Zeng, Tianhe Ren, Shilong Liu, and Lei Zhang. T-rex2: Towards generic object detection via text-visual prompt synergy. In European Conference on Computer Vision, pp. 38-57. Springer, 2024a.
Qing Jiang, Gen luo, Yuqin Yang, Yuda Xiong, Yihao Chen, Zhaoyang Zeng, Tianhe Ren, and Lei Zhang. Chatrex: Taming multimodal 11m for joint perception and understanding, 2024b. URL
https://arxiv. htggabbb/241111 18363
Bingyi Kang, Zhuang Liu, Xin Wang. Fisher Yu, Jiashi Feng, and Trevor Darrell. Few-shot object detection via feature reweighting. In Proceedings of the IEEE/CVF international conference computer vision, pp. 8420-8429, 2019.
Prannay Kaul, Weidi Xie, and Andrew Zisserman. Label, verify, correct: A simple few shot object detection method. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 14237-14247, 2022.
Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloc Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C. Berg, Wan-Yen Ven Lo, Piotr Dollár, and Ross Girshick. Segment anything. arXiv:2304.02643 2023.
Yanghao Li, Hanzi Mao, Ross Girshick, and Kaiming He. Exploring plain vision transformer back- bones for object detection. In European conference on computer vision, pp. 280-296. Springer, 2022.
Tsung-Yi Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In European conference on computer vision, pp. 740-755. Springer, 2014.
Changhan Liu, Xunzhi Xiang, Zixuan Duan, Wenbin Li, Qi Fan, and Yang Gao. Don't need retrain- ing: A mixture of detr and vision foundation models for cross-domain few-shot object detection. In The Thirty-ninth Annual Conference Neural Information Processing Systems, 2025.
Shilong Liu, Zhaoyang Zeng, Tianhe Ren, Feng Li, Hao Zhang, Jie Yang, Qing Jiang, Chunyuan Li, Jianwei Yang, Hang Su, al. Grounding dino: Marrying dino with grounded pre-training for open-set object detection. In European conference on computer vision, pp. 38-55. Springer, 2024.
Jiawei Ma, Yulei Niu, Jincheng Xu, Shiyuan Huang, Guangxing Han, and Shih-Fu Chang. Digeo: Discriminative geometry-aware learning for generalized few-shot object detection. In Proceed- ings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 3208-3218, 2023.
Maxime Oquab, Timothée Darcet, Théo Moutakanni, Huy Vo, Mare Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, et al. Dinov2: Learning robust visual features without supervision. arXiv preprint arXiv:2304.07799. 2023.
Limeng Qiao, Yuxuan Zhao, Zhiyuan Li, Xi Qiu, Jianan Wu, and Chi Zhang. Defren: Decoupled faster r-cnn for few-shot object detection. In Proceedings of the IEEE/CVF international confer- ence on computer vision, pp. 8681 8681 16900, 2021.
12

---

Published as a conference paper at ICLR 2026
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International conference machine learning, pp- 7748-8763. PmLR, 2021.
Mike Ranzinger, Greg Heinrich, Collin McCarthy. Jan Kautz, Andrew Tao, Bryan Catanzaro, and Pavlo Molchanov. C-radiov4 (tech report). arXiv preprint arXiv:2601. 17226777.1777,,.. 2026.
Nikhila Ravi, Valentin Gabeur, Yuan-Ting Hu, Ronghang Hu, Chaitanya Ryali, Tengyu Ma, Haitham Khedr, Roman Rädle, Chloe Rolland, Laura Gustafson, Eric Mintun, Junting Pan, Kalyan Va- sudev Alwala, Nicolas Carion, Chao- Yuan Wu, Ross Girshick, Piotr Dollár, and Christoph Fe- ichtenhofer. Sam 2: Segment anything in images and videos. arXiv preprint wwxiv:2408.0077 2024. URL BRRLatts::// arxiv. org abs/ /aab//408... 00714
Oriane Siméoni, Gilles Puy, Huy V Vo, Simon Roburin, Spyros Gidaris, Andrei Bursuc, Patrick Pérez, Renaud Marlet, and Jean Ponce. Localizing objects with supervisde transformers and no labels. arXiv preprint 100. 14279, 2021.
Oriane Siméoni, Huy V. Vo, Maximilian Seitzer, Federico Baldassarre, Maxime Oquab, Cijo Jose, Vasil Khalidov, Marc Szafraniec, Seungeun Yi, Michaël Ramamonjisoa, Francisco Massa, Daniel Haziza, Luca Wehrstedt, Jianyuan Wang. Timothée Darcet, Théo Moutakanni, Leonel Sentana, Andrea Vedaldi, Jamie Tolan, John Brandt, Camille Couprie. Julien Mairal, Hervé Jégou, Patrick Labatut, and Piotr Bojanowski. DINOv3, 2025. https:
Claire Roberts, Andrea
0bas/2508.2 10104
Roman Solovyev. Weimin Wang, and Tatiana Gabruseva. Weighted boxes fusion: Ensembling boxes from different object detection models. Image and Vision Computing, 107:104117, 2021.
Bo Sun, Banghuai Li, Shengcai Cai, Ye Yuan, and Chi Zhang. Fsce: Few-shot object detection via contrastive proposal encoding. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 7352-7362 2021.
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in neural informa- tion processing systems, 30, 2017.
Xin Wang, Thomas E Huang, Trevor Darrell, Joseph E Gonzalez, and Fisher Yu. Frustratingly simple sho-shot object detection. arXiv preprint arXiv:2003. 06957, 2020.
Yangtao Wang, Xi Shen, Yuan Yuan, Yuming Du, Maomao Li, Shell Xu Hu, James Crowley, and Dominique Vaufreydaz. Tokencut: Segmenting objects in images and vidcos with self-supervised transformer and normalized cut. IEEE transactions on pattern analysis and machine intelligence, 45(12):15790-1588 2023.
Yang Xiao, Vincent Lepetit, and Renaud Marlet. Few-shot object detection and viewpoint estimation for objects in the wild. IEEE transactions on pattern analysis and machine intelligence, 45(3): 3090-3106, 2022.
Wuti Xiong. Cd-fsod: benchmark for cross-domain few-shot object detection. ICASSP 2023- 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp- 1-5. IEEE, 2023.
Xiaopeng Yan, Ziliang Chen, Anni Xu, Xiaoxi Wang, Xiaodan Liang, and Liang Lin. Meta r-cnn: Towards general solver for instance-level low-shot learning. In Proceedings of the IEFF/CVF international conference on computer vision, pp. 9577-9586, 2019.
Cheng- Yen Yang, Hsiang- Wei Huang, Wenhao Chai, Zhongyu Jiang, and Leng-Neng Hwang. Samu- rai: Adapting segment anything model for zero-shot visual tracking with motion-aware memory. arXiv preprint arXiv:241 I. 1922, 2024.
Hao Zhang, Feng Li, Shilong Liu, Lei Zhang, Hang Su, Jun Zhu, Lionel M Ni, and Heung- Yeung Shum. Dino: Detr with improved denoising anchor boxes for end-to-end object detection. arXiv preprint arXiv:2203.03660 2022.
13

---

Published as a conference paper at ICLR 2026
Xinyu Zhang, Yuhan Liu, Yuting Wang, and Abdeslam Boularias. Detect everything with few examples. arXiv preprint orXiv2230.119960 2023.
Xingyi Zhou, Rohit Girdhar, Armand Joulin, Philipp Krähenbühl, and Ishan Misra. Detecting twenty-thousand classes using image-level supervision. In European conference on computer vision, pp. 350-368. Springer, 2022.
14

---

Published as a conference paper at ICLR 2026
A
APPENDIX
A.1 PSEUDO-CODE OF FSOD-VFM
We present the pseudo code of FSOD-VFM in Algorithm I
Algorithm FSOD- Few-Shot Object Detection with Foundation Models
1: Input: Support set S
{ 1==1 query image g. max iterations max_iter, diffusion parameters
a,
2: Output: Refined prediction of proposals
3: Stage Build Class Prototypes
4: for each support annotation s' E S do
5: Generate object mask M using SAM2
6: Extract dense feature map Fing using DINOv2
7: Pool features over mask: F f- masked Rol pooling (Eq. 1
8: end for
9: for each class c E f1....,C. C) do
10: Collect support features: F, cF
11: Compute prototype: Pa: = [Fa] 2Fer.
12: Normalize prototype: Pe. Pe/ P
13: end for
Stage Query Proposal Matching
15: Generate bounding box proposals {(0], v/ 2ig. 1 using UPN
16: for each proposal j do
Extract Rol features FiJ using SAM2 DINOv2
Predict class: e = arg max, cos( F? qth Pe) (Eq. 19: Store quadruple (F.- ái supn MD)
20: end for
21: Stage 3: Graph Diffusion for Score Refinement
22: Construct graph 9 = E) with nodes as proposals in each class c E (1,...,CC C}
for each class E [l...... C} do
24: for each edge (i,j) do
25:
if sy Eh +0
supa then
27:
else
g/. + Area(M'nM')
28:
Areal MP))
29:
end if
30:
end for
31:
Row-normalize edges: P <- transition matrix from &
32:
Initialize prior weights: w <- (E
33:
Initialize diffusion vector: T <
1/N]T
34:
for t = 0 to max-iter do
35:
π ++1 <- aP @ n + (1 a:w
36:
if
| x'll < then
37:
break
> Convergence achieved
38:
end if end for
39:
end for
41: for each proposal j do
42: Refine score: fo + (1 a1 max, cos Fjj Pe) (Eq. 6
43: end for
44: return final proposals {()}
15

---

Published as a conference paper at ICLR 2026
Prohability Distribution Convergent Iheration
Profebilit Curve
0.150
0.325
Probbbiiy
0.100
0.000
100
Canoengent
Figure 5: Graph diffuuson convergent iteration. The graph diffusion process stop around 70 steps in Pascal-5 (Everingham et al. 2010) splitl under 1-shot setting. However, most runs end before 50 steps.
A.2 DIFFUSION TIMESTEPS
The graph diffusion process converges in approximately 70 iterations, as illustrated in Figure Most cases converge within 50 steps, with very few cases completing between 50-70 steps. However, as shown in Table [5] the AP performance stabilizes after 5 steps. This is because AP performance depends on the ordering of box proposals rather than the absolute values of confidence scores of each proposal. We opt to run 30 steps to balance stability and efficiency.
A.3 DATASET DESCRIPTION
We describe the datasets used in this section.
|Everingham et al., 2010). The Pascal VOC dataset (Everingham et al. 2010) contains 20 object categories. Following the standard few-shot protocol (Guirguis et al.) (023), it is divided into three splits, each with 5 novel classes and 15 base classes. Our setup performs no additional training and relies only on few-shot samples of novel categories, making the task more challenging.
Pascal-5'
For Pascal-5', we follow prior works (Wang et al. 2020. Han et al. 2022a Xiao al. 2022) and
report nAP50, i.e., the average precision on novel classes at an IoU threshold of 0.5. We use random seed of 33 when sampling support sets, following the approach described in (Espinosa et al. 2025).
COCO-20' Kang et al. 2019; Lin et al. 2014). The COCO dataset (Lin et al. 2014) contains 80 object categories, with 20 classes designated as novel following standard few-shot protocols (Wang et al. 2020). We report 10-shot and 30-shot results using the standard evaluation protocol. Similar to Pascal-5", our setup is training-free and relies solely on few-shot samples of novel categories, making the task more challenging. For COCO-20' we follow prior works (Wang et al. 2020 Han et al. 2022a Xiao et al. 2022) and report nap (loU thresholds 0.5-0.95), nAP50 (IoU 0.5), and nAP75 (IoU 0.75) novel classes.5) and nAP75 (IoU threshold 0.75) on novel classes. We use
random seed of 33 when sampling support sets, following the approach described in (Espinosa et al. 2025).
16

---

Published as a conference paper at ICLR 2026
Novel Split
Split 2
Split
Method
Novel.
2
3
5
10
2
3
5
10
3
5
10
Avg
PeDerView (Xio
2022
20.4
37.4
36.1
42.3
22.6
25.6
29.2
32.4
19.0
29.8
33.2
39,8
29.2
TFA (Wang ata 20/00
36.1
44.7
55.7
5600
23.5
26.9
34.1
35.1
39.1
30.8
42.8
49.5
49.8
39.9
Retentive SONIERRCNN RCNN (FSa 2
2
42.4
45.8
45.9
53.7
56.1
21.7
27.8
35.2
37.0
40.3
30.2
37.6
43.0
49.7
50.1
41.1
DiGea et
37.9
39.4
48.5
58.6
61.5
28.9
41.9
42.1
49.1
46.9
52.7
54.7
44.0
HeteroGapii Ranh
42.4
51.9
55.7
62.6
63.4
25.9
37.8
46.6
48.9
51.1
35.2
42.9
41.8
5488
53.5
48.0
Meta Faster R-U.NN Hiin el
2022e
43.0
54.5
60.6
66.1
65.4
27.7
35.5
46.1
47.8
51.4
40.6
46.4
53.4
59.9
58.6
50.5
CrosTTansformer
ct 202200
57.0
LVC (Kaul al. 2022)
51.5
53.2
58.8
63.2
65.7
32.8
29.2
50.7
49.8
50.6
48.4
52.7
55.0
59.6
59.6
52.3
NIFF Liirguus et m.. 2023
62.8
67.2
68.0
70.3
68.8
38.4
42,9
54.0
56.4
54.0
56.4
62.1
61.2
64.1
63.9
59.4
Multi-Relation Det
(Fan 20200
DE-VIT (VIT-S/14)
ZZhang ei al. 2123
47.5
64.5
57.0
68.5
67.3
43.1
34.1
49.7
56.7
60.8
52.5
62.1
60.7
61.4
64.5
56.7
DE-VIT (VIT-/114)
Zhang et al:
2125
56.9
61.8
68.0
73.9
728
45.3
47.3
58.2
89
60.6
58.6
62.3
62.7
64.6
67.8
614
(ViT-L/14)
Ziaan
67.3
No-Time Toain
Espiassa et
2025
70.8
72.3
73.3
77.2
79.
54.5
67.0
76.3
75.9
78.2
61.1
67.9
713
70.8
72.6
712
BOOD-VE--DINOV.JU
81.2
821
82.4
85.2
84.9
62.9
68.4
75.8
75.8
81.0
61.6
73.1
76.8
75.5
76.8
76.2
82.3
85.8
78.7
FSOD-VEM-RADIE -RADI044
77.5
81.4
81.6
81.6
86.6
69.2
75.2
79.2
79.7
79.8
62.6
75.9
76.4
76.2
79.8
77.7
Results for competing methods are taken from Zhang ct (2225.
with the best highlighted in bold.
Table 7: Results on Pascal-5' (Everingham et al., 2010). We report nAP50, i.e., the average
precision at IoU 0.5 on novel classes.
ET.
10-shot
30-shot
Method
Novel.
nAP
nAPS0
nAP75
nAP
nAPS0 nAP75
TFA (Wang el al.
2020
10.0
19.2
9.2
13,5
24.9
13.2
FSCE (Sun et al
2021
11.9
10.5
16.4
16.2
Retentive RC
al.
10.5
19.5
13.8
22,9
HeteroGraph (Har
2021
11.6
23.9
9.8
16.5
31.9
15.5
Meta F. R-CNN [Han et aL. 2022a
12.7
25.7
10.8
16.6
31.8
15.8
LVC (Kaul al, 2022
19.0
34.1
19.0
26.8
45.8
27.5
C. Trunstormer (Han
20226
17.1
30.2
17.0
21.4
35.5
22.1
NIF (Guirguis èl ni. 2022
18.8
20,9
DiGeo [Ma at. 2U263
10.3
18.7
9.9
14.2
26.2
14.8
(VI-L)) (Fu
2024
35.3
54.9
54.5
FSRW (Kang
2019
5.6
12.3
19.0
Meta R-CNN (Yan ct al. 2019
X
6.1
19.1
6.6
9.9
25.3
10.8
DE-ViT (ViT-L) L) (Zhang el all
2023
X
34.0
53.0
37.0
34.0
52.9
37.2
No-Time-To To-an- (Espinosa el al.
36.6
54.1
36.8
54.5
ESOD-VEM-DINGL
42.4
57.6
45.8
43.7
59.6
47.1
x
44.0
59.4
47.6
45.8
61.9
49.4
TSOD-VEM-RADION
46.3
62.5
50.4
47.8
64.4
52.0
Results for competing methods are taken from Fu et al.
2024), with the best highlighted in bold.
Table 8: Results COCO-20 (Kang et al., 2019: Lin et al., 2014). We report nAP (IoU thresh- olds 0.5-0.95), nAP50 (IoU 0.5), and nAP75 (loU threshold 0.75) on novel classes.
CD-FSOD (Xiong 2023). The Cross-Domain Few-Shot Object Detection (CD-FSOD) bench- mark (Xiong 2023 evaluates performance across six diverse scene domains, including photorealis- tic, cartoon, aerial, underwater, and industrial settings. This benehmark is particularly challenging, as it tests not only the few-shot learning capability but also cross-domain generalization. Unlike Pascal or COCO, there are no base categories in CD-FSOD, and only few-shot samples are provided for each domain. Following (Espinosa et al. 2025), report nAP the evaluation metric.
A.4 FEATURE EXTRACTOR
We provide performance comparisons of DINOv2-B, DINOv2-L and new model RA- DIOv4 (Ranzinger et al. 2026) on Pascal-5', COCO-20' and CD-FSOD datasets. From the results in Table 7 Table 8 and Table 9 we find that FSOD-VEM SSOD-VEM-RADIO&& outperforms FSOD-VFM- DINOv2-B and FSOD-VE WEDD-VEM-DNNNN DINOv2-L in most of the sub-sets. Consequently, RADIOv4 a better feature extractor than DINOV2 for main experiments.
17

---

Published as a conference paper at ICLR 2026
Mechod
ET Nevd
ArTaxOr
Clip artik
DIOR
Deep Fish
NEU DET
UODD
AFK
Wang
31/88//48
80/181/205
Sun
52/102/159
86/187/219
39/96/120
36/992155
93/189//2.9
45799/12.1
Duall-othd
Krng
10:31
51/125/18.
26/233/213
1015/1.1120
-1155/155
1100/211
59/122//45
-7164/205
59/209/234
61/333/256
12.9/33./294
09290/65
24/135/158
40215./156
54/168/194
Dene-T
Zbou al.
32/87/1200
151/202//2.8
411/121/154
90/183/119
38/161/168
42/104/144
66/133/165
DE-VITFFT
Zhang cl
10.5/38.0/492
3614488
14.2/294/266
193/212/223
05/78/88
24/5.0/44
10./22.3/252
20/2
21.0/4/9/013
172/411/463
178/269/
203/223/223
36/114/1288
31/68/70
139/2611288
Mistom
261/6833/713
/41//811/89
266/32.1/378
343/395/34.1
91/190/237
90/196/22.
182/347/888
Mcn-RCNN Min
2019
28785/140
78/177/306
36/88/122
06/06/06
114/114/114
0.170.1701
09:09/09
00/00/00
22/22/22
DE-VI
Zhang
0.47 10.1/92
0.5555/31D
27/78/84
04/25/2.
04/15/18
1.5/3.18.1
10/51/59
No-Time Esparna 2025
28.23357/350
189/249/25,
149/18.5/164
203/289/29.
55/52/55
10.0/00//66
180/32.4/144
1S0D-YEM--DNNF24
556/573
151//400/4.5
41.6
160/20007
341/350/31.
6,1577/88
91/160/173
28.5/384/202
DION-
51.47620/613
183/23.5/2.5
360/339/34.
59/74/72
118/173//15
253/313/316
1S00-YFM-BARR044
51.4768.9/60 16.1
/A//515//5 52,1
188/256/257
14//225/3/1
63/103/ 1066
92/18.0/23 20.7
256/188/947
indicates Distll-CD-FSOD (iiong 2023 results, and denotes CD-VITO
Fu ct al. 2024 results. Best results are in bold.
Table 9: Results the CD-FSOD benchmark (Xiong, 2023 We report nAP (IoU thresholds 0.5-0.95) for all datasets, with each entry showing results for 1-shot, 5-shot, and 10-shot.
A.5 COMPUTATIONAL OVERHEEAS
We provide a summary of the number of parameters, FLOPs, and computation time for each model used, as shown Table TO Quantitative results reveal that SAM2 incurs a longer inference time than both UPN and DINOv2.
Model
Parameters (M)
FLOPs (G)
Average Inference Time (s)
UPN (Jiang et al. 2024b)
218
1523
0.33
SAM2 (Ravi et al. 2024)
225
844
0.86
DINOv2 (Oquab al. 2023)
304
415
0.24
Table 10: Computational Overheads of different components. The inference device is A40 GPU.
A.6 COMPONENT ABLATIONS
We provide component ablation studies of our two main components: UPN and SAM2. Firstly, we conduct experiments without SAM2 on Pascal-Si (split 1-shot) and COCO-Si (10-shot). In this case, we directly apply average pooling over the raw bounding box regions to obtain features. The results are shown Table T1 The substantial performance drop demonstrates that SAM2 is essential for our method. Although SAM2 does not always produce perfect masks, having reasonably accurate foreground mask is far more effective than using the entire bounding box for feature extraction. This validates the necessity of incorporating SAM2 in our pipeline.
We additionally conduct experiments without UPN, where SAM2 used directly for object proposal and mask extraction. The results show clear performance drop compared with using full set of proposed modules.
Settings
Pascal-5'
COCO-20'
Ours
77.5
59.4
w/o SAM2 (Ravi et al. 2024
25.5
15.5
w/o UPN (Jiang et al. 2024b
56.7
41.5
Table II: Component ablation studies. We report nAP50 (IoU 0.5) on novel classes.
A.7 THE USE OF LARGE LANGUAGE MODELS (LLMs)
Large Language Models (LLMs) were utilized to polish the writing in this manuscript.
18


Reviews:
[{"review_id": "BWa2a1ZdxX", "reviewer": "Reviewer_6RnX", "summary": "The paper proposed a graph-diffusion based method for FSOD problem by integrating foundation models. The core novelty is to build a foundation model-based bounding box proposals module and a graph diffusion module.  The proposed network can achieve SOTA performance on classic FSOD dataset with significant improvement.", "questions": "See weaknesses", "limitations": "", "rating": 8, "confidence": 5, "soundness": 4, "presentation": 4, "contribution": 4, "strengths": "1. The idea of utilizing vision foundation models is novel.\n2. Formulas are very helpful to understand the core idea of graph diffusion and the motivation of the research.\n3. The proposed method also shows good generalization to cross-domain scenario, which is remarkable.\n4. The proposed method achieved SOTA performance.\n5. The limitation section provides good analysis of the proposed method, especially the part about the marginal improvement when the  annotated samples increases.", "weaknesses": "1. In the settings of FSOD and CD-FSOD, the basic assumption is that the novel category objects are either never seen in the training set or not labeled. However, with the vision foundation model, how do the authors make sure that the novel objects is absolutely unseen? This part of statement should be more clear.\n2. The proposed network explores a very different way comparing to classic FSOD paradigms, what would be the computational overhead in  the proposed networks setting?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "C9rvURNtjE", "reviewer": "Reviewer_JBSW", "summary": "The paper proposes the FSOD-VFM framework for few-shot object detection by combining several powerful vision foundation models (such as DINOv2, SAM2). It proposes a graph diffusion module that treats bounding box proposals as nodes in a graph and uses graph diffusion operations to adjust the confidence of each bounding box proposals, effectively suppressing local, fragmented boxes. The method performance across multiple benchmark datasets. Experimental study on cross domain datasets show improved performance over the counterpart methods.", "questions": "In addition to the questions raised in the Weaknesses section it would be great to hear authors' thoughst on why the performance in the 10-shot setting is lower than in the 5-shot setting or 1-shot setting in the table 3.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "1. The proposed graph diffusion method effectively suppresses the overfragmentation without training.\n\n2. The proposed method is simple and easy to follow.\n\n3. The writing of the paper is clear and fluent.", "weaknesses": "There are issues with method:\n\na) The graph diffusion module uses Equation 3 to calculate the diffusion energy between two proposed bounding boxes. However, Equation 3 relies heavily on the confidence scores provided by UPN. If fragmented bounding box have higher confidence scores, the graph diffusion process can not improve the confidence of high-quality candidate boxes.\n\nb) The method in the paper uses SAM to segment the bounding box proposals given by UPN, in order to obtain cleaner foreground regions. However, the paper does not explain whether the method's performance would be significantly degrade if SAM fails to generate accurate masks. It would be helpful if the authors could visualize the masks provided by SAM in the cross domain tasks and conduct a comparison experiment without using SAM , to validate the necessity of incorporating SAM into the method.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "Y4fJ6wpL1k", "reviewer": "Reviewer_N1W4", "summary": "This paper proposes FSOD-VFM, a training-free few-shot object detection method that integrates three vision foundation models (UPN for proposal generation, SAM2 for mask extraction, and DINOv2 for feature extraction) to detect novel categories using only a few annotated samples. Its core contribution is a graph diffusion-based confidence reweighting mechanism that effectively mitigates proposal over-fragmentation and enhances detection granularity. Extensive experiments on Pascal-5, COCO-20 , and CD-FSOD benchmarks demonstrate that the method significantly outperforms existing training-free approaches and even surpasses many training-based methods, offering a novel perspective for solving visual tasks in a training-free paradigm.", "questions": "Could the functionality of graph diffusion be achieved through other forms of NMS, based on a comparison of their underlying principles?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "1.  The paper introduces a graph diffusion confidence reweighting mechanism to address the prevalent issue of proposal fragmentation in training-free few-shot detection. Constructing a directed graph among proposals and simulating energy propagation to effectively distinguish between complete object proposals and partial fragment proposals, thereby significantly enhancing detection granularity.\n\n2.  It is commendable that the authors have validated the method's effectiveness across multiple benchmarks (Pascal-5, COCO-20). The notable performance improvement on the cross-domain few-shot detection benchmark (CD-FSOD) particularly underscores the method's strong generalizability and universality, demonstrating its potential to tackle real-world, complex scenarios.", "weaknesses": "1.  The model is overly complex, employing three powerful foundation models to address a relatively specific issue (fragmented bounding boxes generated by UPN). The model complexity and computational cost are exceptionally high. SAM2 is a powerful segmentation model whose generated precise masks already contain rich structural information of objects. Why can't detection boxes be generated directly from SAM2's masks, or why can't a more streamlined pipeline be built around SAM2 as the core, rather than relegating it to an auxiliary role that merely generates masks for UPN proposals?\n\n2.  The methodological description in the paper, particularly concerning the core Figure 3, is insufficient for readers to understand the entire technical pipeline. The limited informational content of the figure creates excessive jumps between key steps like \"Feature Extraction & Matching\" and \"Graph Diffusion,\" making comprehension very difficult. To enhance the work's comprehensibility and reproducibility, it is strongly recommended to supplement the paper with more detailed flowcharts or schematic diagrams.\n\n3.  The definition and explanation of the core concept of \"Heat\" in the graph diffusion process within the paper are highly ambiguous,easily causing significant confusion for readers. As described in Section 3.2, \"Heat\" flows from low-scoring nodes to high-scoring nodes, yet the final stable energy distribution (π̂) is used to penalize nodes, which seems counterintuitive. The authors must add a clear explanation explicitly distinguishing between the \"process of energy flow\" and the \"meaning represented by the steady-state energy distribution.\"\n\n4.  The paper fails to clearly differentiate the proposed \"Graph Diffusion\" method from the classical NMS mechanism, which severely undermines the perceived innovation of the method. Superficially, both aim to eliminate redundant boxes, but the authors do not delve into explaining their fundamental differences. The authors must include a comparative discussion in the text contrasting the underlying principles of their method with those of NMS.\n\n5.  Regarding Table 3 and the CD-FSOD experimental results: the original benchmark's reported results are based on mAP (mean Average Precision), but the comparative results presented in this paper for CD-FSOD use nAP-50. This constitutes a serious methodological error in evaluation.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "qrPK0Njlft", "reviewer": "Reviewer_MxA8", "summary": "The paper addresses few-shot object detection (FSOD). It particularly focuses on training-free detectors by integrating multiple vision foundation models. It uses a universal proposal network (UPN) to detect object proposals and then SAM2 to output object masks. It uses DINOv2 to represent masked objects for matching test-time proposal and few-shot annotated images, achieving FSOD results. It evaluates on traditional benchmarks built on COCO, PASCAL and a few others. It compares against previous FSOD methods which train a base model (on significantly smaller set than foundation models' pretraining set) and finetune on few-shot novel classes. It reports superior performance than the previous FSOD methods.", "questions": "The reviewer asks the authors to address each point in weaknesses listed above and does not repeat them in this Questions box.", "limitations": "", "rating": 2, "confidence": 5, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "Below are notable strengths of this paper.\n- The use of graph difussion to post-process predictions based on confidence scores is interesting.\n- Evaluation metrics are standard.", "weaknesses": "Below are major weaknesses of this paper.\n- While the paper advocates training-free methods for FSOD, it is unclear how such training-free methods can detect objects specified by downstream tasks that require to detect non-trivial objects. For examples, if a downstream task requires amodal detection but SAM cannot output amodal segments, how training-free methods handle this discrepancy? Moreover, if a downstream task requires to detect rare objects, can the training-free detector handle this requirement?. The datasets used in experiments contain common objects (from COCO and PASCAL), which are likely learned by vision foundation models and hence disguise limitations of training-free FSOD detectors.\n\n- Images in Figure 1 are low-quality. It seems that (b) and (c) contain \"motorcycle\" predictions and some more on the cows. There are also dim bounding boxes, likely post-processed in some way. Can authors clarify?\n\n- From Figure 1, it is unclear how graph diffusion helps. From (d), (e) and (f), we can see that, even after graph diffusion which diffuses low confidence scores on x-axis, one can still set the confidence threshold as 0.5. So, it is not easy to follow what messages this figure mean to deliver.\n\n- The paper partially follows a traditional FSOD protocol which evaluates on \"novel classes\" (Line363), but it does not exploit any \"base classes\" (Line368). This is a good practice given that it leverages foundation models which can be thought of having been trained on massive \"base data\". However, it is unfair to compare the proposed method with previous ones which use a much smaller \"base data\" set to pretrain models. For fair comparison, it should run existing FSOD algorithms on foundational detectors (e.g., DINO and GroundingDINO), which are assumed to be pretrained on the same \"base data\".\n\n- The datasets used in this work are outdated and small-scale. First, Pascal and COCO were published in 2010 and 2014; but do vision foundation models see similar data to them in the pretraining dataset? Second, Pascal-5 and COCO-20 seems to mean the evaluation is on 5 and 20 classes, respectively. This is rather a small-scale and artificial setting.\n\n- Hyperparameter setting is a big concern in FSOD. The core challenge in FSOD is the lack of data: limited training data for the few-shot (novel) classes and realistically no validation set. Therefore, when reporting the numeric metrics, do the authors tune hyperparameters on the test sets? In Section 4.2, Table 4 shows that the hyperparameter lambda has a significant effect on the final performance.\n\n- In terms of the use of graph diffusion, the paper lacks intuition or rationale how to build a graph. The paper treats each detection as a node, but how to assign weights to edges? Do edges mean similarities between two detections? More important details and discussions are missing.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)