Segment Any Change
Authors: Zhuo Zheng
Yanfei Zhong
Liangpei Zhang
Stefano Ermon
Venue: NeurIPS 2024 poster (2024)
Keywords: change detection
remote sensing
zero-shot adaptation
visual foundation models
satellite imagery
Primary area: machine_vision
TL;DR: Our training-free adaptation enables Segment Anything Model (SAM) to zero-shot change detection by exploiting the latent space of SAM

Abstract: Visual foundation models have achieved remarkable results in zero-shot image classification and segmentation, but zero-shot change detection remains an open problem. 
In this paper, we propose the segment any change models (AnyChange), a new type of change detection model that supports zero-shot prediction and generalization on unseen change types and data distributions.
AnyChange is built on the segment anything model (SAM) via our training-free adaptation method, bitemporal latent matching.
By revealing and exploiting intra-image and inter-image semantic similarities in SAM's latent space, bitemporal latent matching endows SAM with zero-shot change detection capabilities in a training-free way. 
We also propose a point query mechanism to enable AnyChange's zero-shot object-centric change detection capability.
We perform extensive experiments to confirm the effectiveness of AnyChange for zero-shot change detection.
AnyChange sets a new record on the SECOND benchmark for unsupervised change detection, exceeding the previous SOTA by up to 4.4\% F$_1$ score, and achieving comparable accuracy with negligible manual annotations (1 pixel per image) for supervised change detection. Code is available at https://github.com/Z-Zheng/pytorch-change-models.

Segment Any Change
Zhuo Zheng', Yanfei Zhong Liangpei Zhang", Stefano Ermon
SStanford University
Wuhan University
zhuozhengScs. stanford.
Abstract
Visual foundation models have achieved remarkable results in zero-shot image classification and segmentation, but zero-shot change detection remains an open problem. In this paper, we propose the segment any change models (AnyChange), new type of change detection model that supports zero-shot prediction and gener- alization on unseen change types and data distributions. AnyChange built the segment anything model (SAM) via our training- free adaptation method, bitemporal latent matching. By revealing and exploiting intra-image and inter-image semantic similarities in SAM's s latent space, bitemporal latent matching endows SAM with zero-shot change detection capabilities in training-free way. We also propose a point query mechanism to enable AnyChange's ' zero-shot object-centric change detection capability. We perform extensive experiments to confirm the effectiveness of AnyChange for zero-shot change detection. AnyChange sets new record the SECOND benchmark for unsupervised change detection, exceeding the previous SOTA by up to 4.4% F1 score, and achieving comparable accuracy with negligible manual annotations (1 pixel per image) for supervised change detection. Code is available https: trut..u..... com/ 20mm/2227 Zheng/ pytorch- maannng-modess...
Introduction
The Earth's 's surface undergoes constant changes over time due to natural processes and human activities. Some of the dynamic processes driving these changes (e.g., natural disasters, deforestation, and urbanization) have huge impact on climate, environment, and human life (Zhu et al., 2022). Capturing these global changes via remote sensing and machine learning is crucial step in many sustainability disciplines (Yeh et al., 2020; Burke et al., 2021).
Deep change detection models have yielded impressive results via large-scale pre-training (Manas et al., 2021; Wang et al., 2022; Mall et al., 2023; Zheng et al., 2023) and architecture improvements (Chen et al., 202 lb; Zheng et al., 2022). However, their capabilities depend training data and are limited to specific application scenarios. These models cannot generalize to new change types and data distributions (e.g., geographic areas) beyond those seen during training.
This desired level of generalization on unseen change types and data distributions requires change detection models with zero-shot prediction capabilities. However, the concept of zero-shol chunge detection has not been explored so far the literature. While we are in the era of "foundation models" (Bommasani et al., 2021) and have witnessed the emergence of large language models (LLMs) and vision foundation models (VFMs) (c.g., CLIP (Radford et al., 2021) 1) and Segment Anything Model (SAM) (Kirillov et al., 2023)) with strong zero-shot prediction and generalization capabilities via prompt engineering, zero-shot change detection is still an open problem.
To close this gap, we present Segment Any Change, the first change detection model with zero-shot generalization on unseen change types and data distributions. Our approach builds on SAM, the first
"Corresponding authors: Stefano Ermon, Yanfoi Zhong
38th Conference on Neural Information Processing Systems (NcurIPS 2024).

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Urbanization (infrastructure replacement)
Disaster Damage Assessment
De-agriculturalizzation or Delorestation
Natural Resource Monitoring
Figure 1: Zero-Shot Change Detection with AnyChange geoscience. Each subfigure presents the pre-event image, the post-event image, and their change instance masks in order. The boundary of each change instance mask is rendered by cyan, and meanwhile, these change masks are also drawn on pre/post-even images to show more clearly where the change occurred. The color of each change mask is used to distinguish between different instances.
wide range of application scenarios
promptable image segmentation model, which has shown extraordinary zero-shot generalization on object types and data distributions. While SAM is extremely capable, it is non-trivial to adapt SAM to change detection and maintain its zero-shot generalization and promptability due to the extreme data collection cost of large-scale change detection labels that would be required to enable promptable training as in the original SAM.
To resolve this problem, propose a training-free method, bitemporal latent matching, that enables SAM to segment changes in bitemporal remote sensing images while inheriting these important properties of SAM (i.c., promptability, zero-shot generalization). This is achieved by leveraging intra-image and inter-image semantic similarities that we empirically discovered the latent space of SAM when applying SAM's encoder unseen multi-temporal remote sensing images. The resulting models, AnyChane, are capable of segmenting any semantic change.
AnyChange can yield class-agnostic change masks, however, in some real-world application scenarios, e.g., disaster damage assessment, there is a need for object-centric changes, e.g- to detect how many buildings are destroyed. To enable this capability we propose point query mechanism for AnyChange, leveraging SAM's point prompt mechanism and our bitemporal latent matching for filtering desired object changes. The user only needs a single click desired object, AnyChange with the point query can yield change masks centered on this object's 's semantics, i.c., from this object class to others and vice versa, thus achieving object-centric change detection.
We demonstrate the zero-shot prediction capabilities of AnyChange on several change detection datasets, including LEVIR-CD (Chen & Shi, 2020), S2Looking (Shen et al., 2021), x View2 (Gupta al., 2019), and SECOND (Yang et al., 2021). Due to the absence of published algorithms for zero-shot change detection, we also build baselines from the perspectives of zero-shot change proposal, zero-shot object-centric change detection, and unsupervised change detection. AnyChange outperforms other zero-shot basclines implemented by DINOv2 (Oquab et al., 2023) and SAM

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Image at t
Image at
Zz
Ze41
Metld
Ps1 Pra
m,,i
> from +
SAM Prompt Encoder
SAM Image Encoder
SAM Image Encoder
X 2, m=y1
8e41 Bes m
SAM Mask Decoder
change confidence c.
PP
from (+ to Q------
Xp:
Σ, [mp...]
Mask Decoder
Mask Decoder
== [ma-1.] Ne+1J
Rey [m--1s]
semantic similarity - -c pur (Kg 1et
change confidence score ((-1))X)-1.)
mmg)
[m++1a)
SAM forward
Bitemporal Latent Matching
Point Query
Figure 2: Segment Any Change Models, AnyChange. SAM forward: given grid points {p:} as prompts and input images, SAM produces object masks {m,,;} and image embedding image at time f. Bitemporal Latent Matching does bidirectional matching to compute the change confidence score for each change proposal, and then top-k sorting or thresholding is applied for zero-shot change proposal and detection. Point Query allows users to click some points (the case of two points this subfigure) with the same category to filter class-agnostic change masks via semantic similarity for object-centric change detection.
the
with different matching methods in terms of zero-shot change proposal and detection. From the unsupervised change detection perspective, AnyChange beats the previous sfate-of-the-art model, 13PE (Chen al., 2023), setting
record of 48.2% F, on SECOND. We show some qualitative results Fig. 1, demonstrating the zero-shot prediction capabilities of AnyChange on a wide range of application scenarios (i.e., urbanization, disaster damage assessment, de-aggriclltralizattin. deforestation, and natural resource monitoring). The contributions of this paper are summarized as follows:
AnyChange, the first zero-shot change detection model, enables to obtain both instance-level and pixel-level change masks either in fully automatic mode or interactively with simple clicks.
Bitemporal Latent Matching, a training- adaptation method, empowers SAM with zero-shot change detection by leveraging intra-image and inter-image semantic similarities of images in SAM's latent space.
Zero-Shot Change Detection explored for the first time. We demonstrate the effectiveness of AnyChange from four perspectives, i.e., zero-shot change proposal, zero-shot object-centrie change detection, unsupervised change detection, and label-efficient supervised change detection, achieving better results strong baselines previous SOTA methods.
2 Related Work
Segment Anything Model (Kirillov et al., 2023) is the first foundation model for promptable image segmentation, possessing strong zero-shot generalization on unseen object types, data distributions, and tasks. The training objective of SAM is to minimize a class-agnostic segmentation loss given series of geometric prompts. Based on compositions of geometric prompts, i.e., prompt engineering, SAM can generalize to unseen single-image tasks in zero-shot way, including edge detection, object proposal, and instance segmentation. Our work extends SAM with zero-shot change detection for bitemporal remote sensing images via training- frre adaptation method, extending the use of SAM beyond single-image tasks.
Segment Anything Model for Change Detection. SAM has been used for change detection via a (parameter-effiiene fine-tuning" (PEFT) paradigm (Mangrulkar et al., 2022), such as SAM-CD (Ding et al., 2023) that used Fast-SAM (Zhao et al., 2023) as a frozen visual encoder and fine-tuned adapter networks and the change decoder on change detection datasets in fully supervised way. This model does not inherit the most two important properties of SAM, i.e., promptability and zero-shot generalization. Fine-tuning in promptable way with large-scale training change data may achieve these two properties, however, collecting large-scale bitemporal image pairs with class-agnostic

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change annotations is non-trivial (Tewkesbury et al., 2015; Zheng et al., 2023), thus no such method exists the current literature. Our work introduces a new and economic adaptation method for SAM, i.e., training- fareeng--reeeee adaptation, guaranteeing these two properties with zero additional cost, making zero-shot change detection feasible for the first time.
Unsupervised Change Detection. The most similar task to zero-shot change detection is unsu- pervised change detection (Coppin & Bauer, 1996), however zero-shot change detection a more challenging task. They both require models to find class-agnostic change regions, and the main difference is that zero-shot change detection also requires models to generalize to unseen data distri- butions. From early model-free -free change vector analysis (CVA) (Bruzzone & Prieto, 2000; Bovolo & Bruzzone, 2006) to advanced deep CVA (Saha al., 2019) and I3PE (Chen al., 2023), unsupervised change detection methods have undergone a revolution enabled by deep visual representation learning. These -oaeel-based unsupervised change detection methods need to re-train their models on new data distributions. Our proposed model is training-free and can achieve comparable or even better performance for unsupervised change detection.
3 Segment Any Change Models
This paper introduces Segment Any Change to resolve the long-standing open problem of zero-shot change detection (see Sec. 3.1). As illustrated in Fig. 2, we propose a new type of change detection models that support zero-shot prediction capabilities and generalization on unseen change types and data distributions, allowing two output structures (instance-level and pixel-level), and three application modes (fully automatic, semi-automatic with custom threshold, and interactive with simple clicks). AnyChange achieves the above capabilities building on SAM in a training- training-free way.
3.1 Background
Preliminary: Segment Anything Model (SAM) is promptable image segmentation model with an image encoder of VIT (Dosovitskiy et al., 2021), a prompt encoder, and a mask decoder based on transformer decoders. As Fig. 2(a) shows, given as input a single image at time f. the image embedding z, is extracted from its image encoder. Object masks {m,,) } can be obtained by its mask decoder given Ze and dense point prompts obtained by feeding grid points {p,} into its prompt encoder.
Problem Formulation: Zero-Shot Change Detection is formulated at the pixel and instance levels. Our definition of "zero-shot" is similar to that of SAM, i.e., the model without training on change detection tasks can transfer zero-shot to change detection tasks and new image distributions. U = {0, 1} denotes a set of classes of non-change and change. A pixel position or instance area belongs to the change class if the corresponding semantic categories at different times are different. This means that the model should generalize unseen change types, even though all change types are merged into a class of 1. The input is a bitemporal image pair L, I++1 E Rhx where the image size is (h, w) and * denotes the channel dimensionality of each image. For pixel level, the model is expected to yield pixel-level change mask C 2yhx For instance level, the model is expected to yield an arbitrary-sized set of change masks {m:}. where each instance m, is a polygon.
3.2 Exploring the Latent Space of SAM
Motivated by the experiments in Kirillov et al. (2023) on probing the latent space of SAM, we known there are potential semantic similarities between mask embeddings in the same natural image. We further explore the latent space for satellite imagery from both intra-image and inter-image perspectives, thus answering the following two questions:
Q1: Do semantic similarities exist on the same satellite image? Empirically, we find they do. We show this semantic similarity in two ways, i.e., visualizing the first components of principal components analysis (PCA) (Oquab et al., 2023) and probing the latent space (Kirillov et al., 2023), as Fig. 3 (a) shows. Observing the first three PCA components, geospatial objects with the same category have a similar appearance in this low- -wwddmmnniooal subspace. This suggests that this satellite image embedding from SAM encodes the semantics of geospatial objects reasonably well. Furthermore, we do the same latent space probing experiment as Kirillov et al. (2023) did, but on satellite images, and present the results in Fig. (a) (bottom). We compute the mask embedding of object proposals and manually select three proposals with different categories (water, building, and bareland) queries.

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Ba Image
Obgec Proposes
First PCA componerrc
Sucoilo
Quary: water
Cuery, buikiing
Query: barslend
(a) intra-image semantic similarity
(b) inter-image semantic similarity
Figure 3: Empirical evidence semantic similarity on (a) the same satellite image and (b) the satellite images at different times. (a) The visualization of the first three PCA components indicates the objects with the same category are well matched with each other in SAM's latent space; Three red point queries confirm the existence of semantic similarity on the same satellite image. (b) The object proposals (indicated by red points) from the satellite image at f1 queries are used to match all proposals from the satellite image at t2. (best viewed with zoom, especially for the point query) By matching the query embedding with other mask embeddings, we obtain the most similar object proposals with the query proposal. We find that the most similar object proposals mostly belong to the same category as the query.
Q2: Do semantic similarities exist on satellite images of the same location collected different times? Empirically, we find they do. To verify this, we introduce a new satellite image from the same geographic area but different time t1. The above satellite image is captured at time tz. Different from the above latent space probing experiment, we use three object proposals with different spatial positions from the image (,, as queries. These three queries have the same category, i.e., building. By matching with all proposals from the image at 12, we obtain three basically consistent results (Fi of 6..1% 10.6.6 and recall of 96.2%%0.666 66.), as shown in Fig. 3 (b). This suggests that this semantic similarity exists on the satellite images at different times, even though the images have different imaging conditions because they were taken at different times.
From the above empirical study, we find that there are intre-image and inter-image semantic similari- ties in the latent space of SAM for unseen satellite images. These two properties are the foundation of our training- free adaptation method.
3.3 Bitemporal Latent Matching
Based on our findings, we propose a training-free adaptation method, namely bitemporal latent matching, which bridges the gap between SAM and remote sensing change detection without requiring for training or architecture modifications.
The main idea is to leverage the semantic similarities in latent space to identify changes in bitemporal satellite images. Given the image embedding z, and object proposals M, fie,,, generated from SAM on the satellite image time 6, each object proposal m,,; E R is binary mask. We can compute the mask embedding Xt, = z, [m,,]] E Rdm by averaging the image embedding over all non-zero positions indicated by the object proposal my,,. Next, we introduce similarity metric measure the semantic similarity. To do this, we need to consider the statistical properties of the image embeddings from SAM. In particular, SAM's image encoder uses layer normalization (Ba al., 2016). This means that the mask embedding has zero mean and unit variance if we drop the affine transformation in the last layer normalization, i.e., the variance 1, thus we have the mask embedding's 's l norm ||xx,|l| Vdm- which is constant since dm is the channel dimensionality. Given this, cosine similarity a suitable choice to measure similarity between two mask embeddings since they are on a hypersphere with a radius of Vdm, and differences are encoded by their directions. Therefore, we propose to use negative cosine similarity as the change confidence score c(x;, x;) for mask embeddings x; and xy:
D(x,,)) = d= Σ, (xx, 12)2
X,
Xx x,)
dm
The next question is which two mask embeddings to use to compute the change confidence score. The real-world change is defined at the same geographic location from time to + 1. This means
5

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that it is comparable only if two mask embeddings cover the approximate same geographic region. Therefore, we additionally compute the mask embedding X,+1, = Z++1 | E Rd on the image embedding 2 using the same object proposal Mt,i- We then compute the change confidence score e(xt,i, 1(+1,) for the change at mi,i from i to +
Since we need to guarantee the temporal symmetry (Zheng et al., 2021, 2022) of class-agnostic change, we propose to match the object proposals bidirectionally. To this end, the change confidence score c( xi) for the change at 1,+1,; from & + 1 to & is also computed, where Xt, Ze [m=+1.i] E Rdm is computed on the image embedding 2, with the same object proposal 1 A Afterwards, we can match object proposals M₁ and M,+1 bidirectionally, and (N, + NN+1) change proposals with their confidence score are obtained in total. We propose to finally obtain change detection predictions by sorting by confidence scores and selecting the top-k elements or by angle thresholding. The pseudo-code of Bitemporal Latent Matching is in Appendix B.
3.4 Point Query Mechanism
To empower AnyChange with interactive change detection with semantics, we combine our bitemporal latent matching with the point prompt mechanism of SAM, thus yielding the point query mechanism. Given as input a set of single-temporal point {Pt,}} = (It.s, Mt,s)) with the same category, & denote that this point belongs to the image at time t, and (æ, y) indicates the spatial coordinate of image domain. The object proposals {mp..) ) can be obtained via SAM's point prompts. Following our bitemporal latent matching, we then compute their average mask embedding Xp, = n ΣT Xp., and match it with all proposals (Me,Ms++ via cosine similarity. In this way, the object-centric change detection results can be obtained via a custom angle threshold.
4 Experiments
In this section, we demonstrate the two most basic applications of AnyChange, i.e., (i) zero-shot change proposal and detection and (ii) change data engine. We conduct experiments from these two perspectives to evaluate our method.
4.1 Zero-Shot Object Change Proposals
Datasets:
We use four commonly used change detection datasets to evaluate AnyChange. The first three, i.e., LEVIR-CD (Chen & Shi, 2020), S2Looking (Shen et al., 2021), and xView2 (Gupta et al., 2019), are building-centee
change detection datasets. SECOND (Yang et al., 2021) is a multi-class (up to 36 change types) urban change detection dataset with full annotation. For zero-shot object proposal evaluation, we convert their labels into binary if the dataset has multi-class change labels. Metrics: Conventional change detection mainly focuses on pixel-level evaluation using F1, precision, and recall. More and more real-world applications have started to focus on instance-level change, also called "change parcel". Based on this requirement and AnyChange's 's capability of predicting instance change, we adapt the evaluation protocol of the zero-shot object proposal (Kirillov et al., 2023) for the zero-shot change proposal since they have the same output structure. The metric is mask AR @1 1000 (Lin et al., 2014). Note that change proposals are class-agnostic, therefore, for the first three building-centric datasets, cannot obtain accurate Fj and precision due to incomplete "any change" annotations. These two metrics are only used for reference and to see whether the model is close to the naive baseline (predict all as "change" class, and vice versa). Here mainly focus on recall for both pixel- and instance-levels.
Baselines: AnyChange is based on SAM, however, there is no SAM or other VFM2-based zero-shot change detection model that can be used for comparison in the current literature. For a fair comparison, we build three strong baselines based on DINOv2 (Oquab et al., 2023) state-of-the-art VFM) or SAM. The simple baseline is CVA (Bruzzone & Prieto, 2000), a model-free unsupervised change detection method based on &g norm as dissimilarity and thresholding. We build "DINO*2+CVA" an improved version with DINOv2 using an idea similar to DCVA (Saha et al., 2019). We build "SAM+Mask Match". which follows the macro framework of AnyChange and replaces the latent match with the mask match that adopts the IoU of masks as the similarity. "SAM+CVA Match" follows the same idea of AnyChange but adopts the negative l, norm of feature difference as the similarity to compute pixel-level change map via SAM feature- based CVA first. The instance-level voting is then adopted to obtain change proposals. We also build each "Oracle" version of AnyChange as upper bound, where we fine-tune SAM with LoRA (r = 32) (Hu et al., 2022) and train a change
Visuul foundation model, like SAM, CLIP, and DINOv2, etc.

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Table 1: Zero-shot Object Change Proposals. The metrics include pixel-based Fj. Precision (Prec.), and Recall (Rec.) and instance- based mask AR @ 1000. Note that the metric names rendered with gray represent inaccurate estimations due the absence of ground truth of "any change", but reflect whether their predictions approximate the naive baseline (predict all as "change" class).
LEVIR-CD
S21.ooking (hinory)
x View2 (binary)
SECOND (ennry)
Method
Backhone
Prec. Rec. mock ART
F1 Prec. Rec. meak ART
Fi
Prec. Rec. mmok ART
Fj Prec. Rec. mask ART
pisel level
CVA
12.2 7.5 32.6
5.8
3.1
44.3
7.6
4.3
33,3
30,2
26.5
35.2
DINOv2+CVA
VIT-G/14
17.3
9.5
96.6
4.3
2.2
92.9
5.9
3.1
62.0
41.4
26.9
89.4
o pisel and instonce level
MAM-Mask Match
VITBB
12.2
8.7
20.2
6.8
4.7
2.6
28.6
15.1
8.6
8.7
23.5
10.2
23.5
30.3
19.2
7.2
SAM+CVA Match
VIT-B
12.7
7.5
41.9
9.0
3.7
1.9
78.3
31.5
3.0
1.6
29.3
12.2
34.1
23.9
59.5
18.6
Ahynheg
WTTB
23.4
13.7
83.0
32.6
7.4
3.9
94.0
48.3
13.4
7.6
59.3
27.8
44.6
30.5
83.2
27.0
AnyChange (Oracle)
WTTBB
73.3
65.2
83.6
37.3
60.3
53.4
69.1
29.8
49.7
38.7
69.5
31.6
69.5
68.2
70.8
16.8
SAM+Mask Match
VIT-L
16.8
11.9
28.8
13.4
4.0
2.3
13.6
8.5
6.8
4.3
15.7
7.1
16.0
29.0
11.0
4.2
SAM+CVA Match
VIT-I.
13.2
7.2
88.2
29.7
3.0
1.5
95.5
40.3
2.6
1.3
26.6
11.6
35.3
22.0
90.1
25.5
Any Change
VIT-L
21.9
12.5
87.1
39,5
6.5
3.3
93.0
50.1
9.8
5.3
66.1
30.5
42.3
27.9
87.4
28.6
Ahyngee (Oracle)
VIT-L
75.3
65.4
88.7
44.8
62.2
57.6
67.6
30.6
51.4
37.5
81.7
38.8
69.1
63.0
76.5
19.9
SAM+Mask Match
VIT-I1
17.8
12.6
30.2
16.1
4.1
2.5
13.1
8.6
5.5
3.5
13.3
6.3
14.2
28.8
9.5
3.7
SAM+CVA Match
VIT-H
13.2
7.1
92.3
36.8
2.9
1.5
96.3
41.2
3.1
1.6
34.1
15.7
35.6
22.1
91.1
25.7
Any Change
VIT-H
23.0
13.3
85.0
43.4
6.4
3.3
93.2
50.4
9.4
5.1
62.2
29.3
41.8
27.4
88.7
29.0
(Oracle)
VITH
76.3
64.7
93.1
51.4
61.0
53.2
713
31.6
50.8
36.4
84,2
40.4
69.5
63.7
76.5
19.3
confidence score network on each dataset. More implementation details can be seen in Appendix C.I.
We compare the change recall of AnyChange with other zero-shot baselines in Table 1. For pixel-level change recall, AnyChange achieves better recalls than the other two SAM baselines, especially when using a small VÝT-B. This is because bitemporal latent matching better measures semantic similarity, i.e., using the angle between two embeddings. We can observe that the gap between AnyChange and SAM baselines gradually reduces as the backbone becomes larger since visual representation capabilities generally become stronger. AnyChange still has better average performance on four datasets, although a stronger representation can close the performance gap to some extent. This highlights the importance of finding the essential semantic difference. Besides, our Any with ViT-H (636M parameters) achieves comparable recalls to CVA with DINOv2 (ViT-G, 1, 1,100M parameters) on four datasets in fewer parameters. On the SECOND dataset, all vari- ants of AnyChange achieve better zero-shot change detection performance than the strong baseline, DINOv2+CVA, and the margin is up to 3.2% F1. For instance-level change recall, Any out- performs the other two SAM baselines by a significant margin. This further confirms the effectiveness and superiority of bitemporal latent matching. We observe that the Oracles obtained via supervised learning have superior precision since they learn semantics and dataset bias explicitly, however, this comes with the cost of recall on pixel or instance levels.
Results:
Ablation: Matching Strategy. "Mask Match" performs geometry-based matching, while "CVA Match" and bitemporal latent matching perform latent-based matching. In Table 1, we that latent- based matching is much more promising than geometry-based based matching from multiple perspectives of pixel-level and instance-level change recall and zero-shot change detection performance. Compared with "CVA Match", AnyChange outperforms "SAM+CVA Match" on instance- object change proposals by large margins and has comparable recalls on pixel-level change recalls. In principle, the similarity of "CVA Match" is linearly related to our bitemporal latent matching since the magnitude of the embeddings is constant. Therefore, the performance difference lies in the computational unit (pixel or instance). "CVA Match" is pixel-wise, while bitemporal latent matching is instance- wise. This suggests that it is more robust compute mask embedding averaged over all involved pixel embeddings for matching.
Ablation: Matching Direction. As presented in Table 2, we can find that the performance of single-directional matching is sensitive to temporal order, e.g., mask AR of two single- single-directionll matching LEVIR-CD 1.3% and 35.9%, respectively. This is because the class- alass-agnostic change is naturally temporal symmetric which is exactly the motivation of our bidirectional design. This result also confirms generally higher and more robust zero-shot change proposal capability.
Ablation: Robustness to radiation variation. We used ViT-B as the backbone for fast experiments. The results are presented in Table 3. The performance jitter of mask AR is less than 2% (-1.9%,+0. 1%,
7

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Table 2: Ablation: Matching Direction. The backbones are VIT-B, Single-directions matching is sensitive to temporal order, while bidirectional matching is more stable due to its guaranteed temporal symmetry.
LEVIR-CD
S2Looking (binary)
VVew22 (binary)
SECOND (hinary)
Direction
F1 Prec.
Rec.
Prec. Rec.
mask ART
Prec. Rec.
mask ART
1 Prec. Rec.
ART
bidirectional
23.4 13.7 83.0
32.6
7.4
3.9
94.0
48.3
13.4
7.6
59.3
27.8
44.6
30.5 83.2
27.0
from to f- 17.7
10.1
72.8
1.3
9.0
4.8
85.6
32.1
15.3
9.0
49.8
27.3
41.2
31.2
60.6
14.8
from + t 23.6
13.6
88.7
35.9
8.1
4.3
79.5
19.4
12.3
7.6
32.7
6.7
46.1
34.1 71.3
14.9
Table 3: Ablation: Robustness to radiation variation. The backbones are VTT-B. Radiation variation was simulated by applying random color jitter independently to the pre- and post-event images.
LEVIR-CD
S2Looking (binary)
xVie22 (hinary)
SECOND (binary)
Condition
F1 Prec. Rec.
musk ART
F2 Prec. Rec. musk ART
F1
Prec.
Rec.
musk ART
F1 1 Prec. Rec. mask ART
baseline
23.4 13.7
83.0
32.6
7.4
3.9
94.0
48.3
13.4
7.6
59.3
27.8
44.6
27.0 28.7 81.7 26.4
30.5 83.2
w/ color jilter
22.6 13.1
84.2
30.7
744
3.8
94.1
48.4
13.5
7.7
54.7
25.9
42.4
-1.9%, -1.4%) on these four datasets. We believe this sensitivity to radiation variation is acceptable for most applications.
4.2 Zero-shot Object-centric Change Detection
Zero-shot object change proposal yields class-agnostic change masks. The point query mechanism can convert the class-agnostic change into object-centric change via simple clicks of the user on a temlerle image, thus providing an interactive mode for AnyChange. This step is typically easy for humans. Here we evaluate the point query on three building-centric change datasets.
Results: We demonstrate the effect of the point query in Fig. 4. Without the point query, AnyChange yields class-agnostic change masks, including building changes, vegetation changes, etc. With a single-point query on building, we can observe that change masks unrelated to the building are filtered out. Further clicking two more buildings to improve the stabil- ity of mask embedding, we find the building changes previously missed covered. Table 4 quantitatively reflects this mechanism. With a single-point query, the zero- shot performances hurts recall as a cost, however it achieves a better trade-off between precision and recall.
successfully re-
three datasets significantly gain 15% Fi score. This improvement
After increasing to three-point queries, the recalls of the model on three datasets get back to some extent, and the model has comparable pre- cision with the single-point query. The zero-shot perfor- mances on three datasets gain 3% F, further. These re- sults confirm the effectiveness of the point query plugin for AnyChange to provide an interactive mode.
Table 4: Zero-shot Object-centric Change Detection. All results of introducing semantics from the point query are accurate estimations since the detected changes are object-centric.
LEVIR-CD
S2Looking (binury)
x XView2(tinay)
Method
F1 Prec. Rec.
F1
Prec.
Rec.
F1
Prec.
Rec.
AnyChange-H
23.0
13.3
85.0
6.4
3.3
93.2
9.4
5.1
62.2
+ Point Query
point query
38.6
30.9 51.4
21.8
16.9 34.1
25.1
24.8 25.4
115.6
155.4
115.7
3 point queries
42.2
28.5 81.1
24.0
14.5
68.5
28.1
20.0
47.2
119.2
117.6
118.7
4.3 AnyChange as Change Data Engine
AnyChange can provide pseudo-labels for unlabeled bitemporal image pairs with zero or few anno- tation costs. To evaluate this capability, we conduct experiments on two typical tracks for remote supervised object change detection and unsupervised change detection.
sensing change detection:
On S2Looking, we train the model on its training set with pure pseudo-labels of AnyChange with VIT-H with single-point query. All training details follow Zheng et al. (2023) except the loss function. On SECOND, we train the model on its training set with pure pseudo-labels of AnyChange with ViT-H via fully automatic mode, and other details follow Zheng et al. (2022). For
Training recipe:
8

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image at time t
image at time 1+1
wil query
w/ point query
point queries
0o0gun
Figure 4: Examples of Point Query Mechanism. The effects of w/o point query, one-point query, and three-point queries are shown sequence from left to right. (best viewed digitally with zoom, especially for the red points)
Table 5: Supervised Object Change Detection. Comparison with the state-of-the-art change detectors on the S2I Looking test. "R-18": ResNet-18. The amount of Flops was computed with float32 tensor of shape [2,5 12.512,512,3 as input.
Method
Backbone
Fine-tuning
#labeled pixels]
Fi f
Prec.
Rec.
#Params.
#Flops
FC-Siam- Diff (Daudt 2018)
100% GT
1.2 10
13.1
83.2
15.7
1.3M
18.7G
STANet (Chen & Shi, 2020)
R-18
100% GT
1.2 10
45.9
38.7
56.4
16.9M
156.7G
CDNet (Chen et ul., 2021a)
R-18
100% GT
1,2 x 10
60.5
67.4
54.9
14.3M
BiT (Chen et al., 2021b)
R-18
100% GT
1.2 10
61.8
72.6
53.8
11.9M
34.7G
ChangeStar (1 x96) (Zheng et al., 2021)
R-18
100% GT
1,2 10
66.3
70.9
62.2
16.4M
65.3G
Changen 90k (Zheng et al., 2023)
R-18
100% GT
10
70.1
65.3G
ChangeStar (1 x 96) (Zheng et al., 2021)
MiT-B1
100% GT
1.2 x 10
64.3
69.3
59.9
18.4M
67.3G
+ Changen-90X, (Zheng et al., 2023)
MiT-B1
100% GT
1.2 x 10.00
67.9
70,3
65.7
18.4M
67.3G
ChangeStar (16)
R-18
1% GTT
1.4 10*
37.2
63.1
26.3
16.4M
65.3G
ChangeStar (1x96)
R-18
0.1% GT
4.7 10
9.2
10.0
8.5
16.4M
65.3G
ChangeStar 96) (Ours)
R-18
AnyChange
3,5 10a
40.2
40.4
39.9
16.4M
65.3G
both these two cases, the loss function is BCE loss with a label smoothing of 0.4. See Appendix C.2 for more details.
Supervised Object Change Detection Results. Table 5 presents standard benchmark results on the S2Looking dataset, which is the one of most challenging building change detection datasets. From data-centric perspective, we use the same architecture with different fine-tuned data. The model architecture adopts ResNet
ChangeStar (1 x 966 (Zheng et al., 2021) due to its simplicity and good performance. We set the fine-tuning on 100% ground truth as the upper bound, which achieved 66.3% F,. We can observe that fin-tuning on the pseudo-labels of AnyChange with ViT-H yields 40.2% F1 with 3,500- pixel annotations Leveraging AnyChange, we achieve 61% % of the upper bound at negligible cost (~ 10 % full annotations). We also compare it to the model trained with fewer annotations (1% and 0. 1)). We find that their performances are reduced by significant amount, and are inferior to the model trained with pseudo-labels from AnyChange. This confirms the potential of AnyChange as change data engine for supervised object change detection.
Unsupervised Change Detection Results. AnyChange's 's class-agnostic change masks are nat- ural pseudo-labels for unsupervised change detection. We also compare our AnyChange with unsupervised change detection methods. In Table 6, we find that AnyChange with ViT-B a zero-shot setting improves over the previous state-of-the-art method, 13PE (Chen et al., 2023). To learn the biases on the SECOND dataset, we trained ChangeStar (1 x256) model with lsbudo-labels of AnyChange on the SECOND training set. The setting follows I3PE
'The training set of S2Looking has 3,500 image pairs. We apply AnyChange with angle-point query for each pair to produce pseudo-labels. Therefore, the number of manual annotations is 3,500 pixels.

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(Chen et al., 2023), thus we align their backbone and use ResNet50 for ChangeStar (1x256). The results show that two variants of unsupervised ChangeStar (1x256) outperform 13PE. Notably, based on pseudo-labels of AnyChange with ViT-B, our model set a new record of 48. 28.2% FL on the SECOND dataset for unsupervised change detection. Besides, we obtain some use- ful insights for unsupervised change detection: (i) deep features from VFMs significantly as- sist unsupervised change detection models since DINOv2+CVA beats all advanced competitors except 13PE. Before
Table 6: Unsupervised Change Detection. Com- parison with the state-of-the-art unsupervised change detectors on the SECOND test. "*" indi- cates this change detection model is trained with pseudo labels predicted by AnyChange.
Method
Backbone
F1 f Prec.
Rec.
ISFA (Wu et 2013)
32.9
29.8
36.8
DSFA (Du et ol., 2019)
33.0
24.2
51.9
strong baseline, DI- NOv2+CVA, CVA has been always regarded simple and ineffective baseline for unsu- pervised change detection. (ii) dataset biases helpful for in-domain unsupervised change detection since our model trained with pseudo- labels achieves higher performance than these pseudo-labels. This indicates the model learns some biases on the SECOND dataset, which may be change types and style.
DCAE (Bergamasco al,, 2022)
33.4
35.7
31.4
OBCD (Xiao et al., 2016)
34.3
29.6
40.7
TRMAD (Nielsen, 2007)
34.5
28.6
43.6
APCA-MNet (Wi al., 2021)
36.7
29.5
48.5
DCVA (Saha et al,, 2019)
36.8
29.6
48.7
DINOv2-CVA (zero shol, our impl.)
VIT-G/14
41.4
26.9
89.4
13PE (Chen et al., 2023)
R-50
43.8
36.3
55.3
AnyChange (acro-shot, ous)
VIT-H
41.8
27.4
88.7
ChangeStar (1 (1x2 (ours)
R-50
45.0
30.2
88.2
Any Changee (eero-shot, ours)
VITBB
44.6
30.5
83.2
ChangeStar (1x)" (ours)
R-50
48.2
33.5
86.4
Conclusion
We present the segment any change models (AnyChange), a new type of change detection model for zero-shot change detection, allowing fully automatic, semi-automatic with custom threshold, and interactive mode with simple clicks. The foundation of all these capabilities is the intra-image and inter-image semantic similarities in SAM's latent space we identified on multi-temporal remote sensing images. Apart from zero-shot change detection, we also demonstrated the potential of AnyChange as the change data engine and demonstrated its superiority in unsupervised and supervised change detection. AnyChange an out-of-the-bor zero-shot change detection model, and step forward towards a "foundation model" for the Earth vision community.
Acknowledgements
This work was supported in part by ARO (W911NF-2I-1-0125)) ONR (N00014-23-1-21559, the CZ Biohub, and the National Natural Science Foundation of China under Grant No. 42325105.
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A Appendix supplemental material
B Pseudocode of Bitemporal Latent Matching
Algorithm Bitemporal Latent Matching (top-k)
Require: Zz. Z(+1; image embeddings; Mc+1 object proposals; k: number of change propos.
als
1: for = to N2 do
2: my, f- -Me[il]
3: Xt,i & Zi m
4: X++1. & Z(+1 mmaa
5: C1, -dm2xe, Xt,i 8,41,
compute change confidence score via embedding dissimilarity
6: for to No+1 do
7: m MM+1li]
8: Xe+Ij + Be+1 [
9: Xt, + zi m1+1,,]
10: C++1, - - dm XX+11 Rtj compute change confidence score via embedding dissimilarity
11: {m,} sort(M M411 keybla t,,:( U (t, > Python-style
top-k sorting
12: return [m:}f
C Implementation Details
C.1 Baselines for zero-shot change proposal and detection
SAM forward: For object proposal generation, we adopt point per side of 64, an NMS threshold of 0.7, predicted IoU threshold of 0.5, and stability score threshold is 0.8 for LEVIR-CD, S2Looking, SECOND, and 0.95 for x 2.ew2. To obtain the image embedding with a constant & norm (yd)). we demodulate the output of the image encoder
SAM with the affine transformation (w; b) of the last layer normalization, i.c., z = w (f((x) - b), where x is the single input image. The forward computation was conducted on 8 NVIDIA A4000 GPUs.
DINOv2 CVA: The implementation straightforward where the image embedding is first extracted from DINOv2 and then upsampled into the image size with bilinear interpolation. The change intensity map is computed by the l norm of the difference between bitemporal image embeddings. The optimal threshold is obtained by an adaptive threshold selection method, OTSU (Otsu, 1979). We also conduct a linear search for its optimal threshold on small validation set, and the searched results have comparable performance with OTSU. Considering optimal peak performance and ease of use, we choose OTSU's threshold as the default for this baseline.
The main idea of this baseline is to use the geometric difference measure an object's s change. In general, an object disappears at the next time image, the object mask generated by SAM is empty or has different geometrie shape, and vice versa. Therefore, using their geometric shape difference to measure if the change occurred is reasonable. To this end, we compute pairwise mask IoU between pre-event object masks and post-event object masks match potentially identical object at another time for each object. We recognize the object region belongs to non-change when successfully matched (15U>0.5), otherwise this region belongs change.
SAM Mask Match:
This baseline follows the same idea of AnyChange, i.c., latent-based matching, but adopts the negative l norm of feature difference the similarity. This method first computes pixel-level change map via SAM feature-based CVA, the procedure of which is similar to "DINOv2 + CVA". The instance-level voting with threshold of 0.5 is then adopted to obtain change proposals, which means that each region is considered as a change when more than half of the pixels are identified as changes.
SAM+ + CVA Match:
AnyChange: For a fair comparison and automatic benchmark evaluation, we use fully automatic mode for AnyChange in Table 1. The change angle threshold (155") is obtained by OTSU and linear search on the small validation set sampled from the SECOND training set. This threshold directly used for the other three datasets without any adjustment.
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AnyChange (Oracle): We only train a LoRA (r 32, alpha 320, dropout = 0.1) for the SAM model on each dataset due unaffordable fall-paremeter training. Besides, we attach change confidence score network on the image encoder of SAM to predict accurate semantic similarity instead of our negative cosine similarity. This network architecture is composed of an ML.P block -awprrooomm--..----ieeee-------
an upsampling block (ConvTransposee Transpose-LayerNoonn Lineaar-Lyyeenormm) for xx upsampling, and a linear layer for predicting change confidence score. The loss function is compound of binary cross-entropy loss and soft dice loss. The inference pipeline exactly follows AnyChange, where first generates object masks and computes mask embeddings, and the change confidence score is obtained from the trained score network. The training iterations are 200 epochs with batch size of 16, Adam W optimizer with weight decay of 0.01. The learning rate schedule is "poly" rate 6e-5 The training data augmentation adopts random rotation, flip, scale jitter, and cropping. The crop size is 512 for LEVIR-CD and S2Looking and 256 for View2 and SECOND, respectively.
0.9) decay with initial learning
C.2 Pseudo-label Training
We have training step of 20k, batch size 16, SGD with momentum 0.9, and a weight decay of le-3 for optimization on the SECOND training set with pseudo-labels of AnyChange in fully automatic mode. For regularization, we adopt the label smoothing of 0.4 and strong data augmentations which include random crop to 256 x 256, flip, rotation, scale jitter, and temporal-wiss adjustment of brightness and contrast.
D Limitations
Zero-shot change detection is an open problem remote sensing and computer vision communities. There is little reference to point out any potential effective roadmap before our work. Our work is the first define this problem suitably and provide simple yet effective model and evaluation protocol. The problem formulation and evaluation protocol themselves bring some potential limitations (i.e., scenario coverage, the robustness objects of different geometries) since there is little mature infrastructure, e.g., concept-complete class-agnostic change detection dataset.
E Broader Impacts
The proposed method can detect class-agnostic changes in Earth's surface, however, its actual effect is impacted by SAM's latent space. The model may produce some impossible changes due to SAM's biases. These issues warrant further research and consideration when building upon this work for real-world applications.
F
More visualization
Nature Image Domain. Our method can be also used in the natural image domain to detect more general object changes, shown in Fig. 5.
More demonstration point query mechanism. We provide an additional example to supplement Fig. 4, which includes unchanged and changed buildings simultaneously. It is more clear to show that AnyChange can more accurately detect building changes with the help of the point query.
Effectiveness of AnyChange in detecting tiny object changes. Fig. 7 demonstrates a case of tiny/minor changes, such as small vehicle changes. We observe that directly applying AnyChange to the original image overlooks these subtle changes (see the first row). After we bilinearly upsampled red box region by 2x and then applied AnyChange to it, we find some tiny/minor changes could be detected (see the second row). This observation shows that our method has the ability on tiny object change detection, although it is not yet optimal. Future work could use our approach as strong baseline to further improve the detection of subtle changes.
14

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Figure 5: AnyChange on Natural Image Domain. Best viewed digitally with zoom.
image
image at time 1+1
Figure 6: Examples Point Query Mechanism. The effects of w/o point query, one-point query, and three-point queries are shown in sequence from left to right. (best viewed digitally with zoom, especially for the red points)
Figure 7: Iluustraioon of the effectiveness of AnyChange in detecting tiny minor object changes.
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NeurIPS Paper Checklist
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Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?
Answer: [Yes]
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We have made claims in the abstract and introduction.
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Answer: [Yes]
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We have discussed the limitations in Appendix D
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Reviews:
[{"review_id": "APtvLNoKsd", "reviewer": "Reviewer_MDhn", "summary": "This paper introduces the AnyChange model, aimed at enabling zero-shot change detection in remote sensing imagery. The model builds upon the SAM, utilizing a training-free adaptation method called bitemporal latent matching. This method leverages semantic similarities within and between images captured at different times to enable change detection without additional training. The paper demonstrates AnyChange's performance through extensive experiments, highlighting its effectiveness in various remote sensing change detection tasks.", "questions": "- How did you set up the dataset and model to prove Q2? Could you provide more details on the empirical setup and the specific configurations used to validate the semantic similarities between satellite images of the same location at different times?", "limitations": "They point out important limitation as vague definition and not concrete benchmark dataset.", "rating": 3, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "- The problem and approach proposed in this paper are highly relevant to practitioners in the remote sensing field. Zero-shot change detection could significantly impact the field by enabling more flexible and scalable monitoring of environmental and infrastructural changes.\n- The experiments presented in Table 1 are well-designed, with reasonable baselines. The methodology appears robust, and the results in Table 4, showcasing the performance of AnyChange as a change detection engine, are particularly promising for practitioners.", "weaknesses": "- The technical contribution of bitemporal latent matching does not appear to be very high. Defining feature differences based on cosine distance between latent representations in SAM's hypersphere domain seems insufficient for a significant contribution. The novelty and uniqueness of this approach compared to existing cosine similarity-based methods, such as those in Růžička et al. (2022), are questionable. The primary difference appears to be the use of SAM’s latent representations, which may not be enough to claim substantial innovation.\n  - Růžička, Vít, et al. \"RaVÆn: unsupervised change detection of extreme events using ML on-board satellites.\" Scientific reports 12.1 (2022): 16939.\n- The paper's technical contributions seem too narrow for NeurIPS. The foundational observations Q1 and Q2 (lines 136 and 148) are only demonstrated with electro-optical images in the satellite domain, limiting the broader applicability of the proposed method to other domains or modalities.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "## To Reviewer MDhn\n\n**W1.1: The novelty and uniqueness of this approach compared to existing cosine similarity-based methods (e.g., RaVAEn) are questionable.**\n\nOur AnyChange is fundamentally different from existing methods.\n- **Zero-shot vs. unsupervised**. Existing unsupervised methods need to train a model to obtain visual representation, e.g., RaVAEn needs to train a VAE on specific data distribution to extract features, while ours belongs to zero-shot method without any training. Thus, our method is more resource-efficient, which is also appreciated by Reviewer SmiM.\n\n- **Interactive**. Existing methods (including RaVAEn) do not support interactive mode, while our method can be interactive and used as an efficient label tool (supported by Table 3), thanks to our point query mechanism which is based on our bitemporal latent matching and SAM's point prompt mechanism. This implies our method supports human-in-the-loop. This point is also appreciated by Reviewer eUYB.\n\n- **Instance-level vs. pixel-level**. Existing methods (including RaVAEn) outputs pixelwise change maps (raster), while ours produces instance change masks (polygon).\n\nOur bitemporal latent matching differs from existing cosine similarity-based methods in three major points:\n- **Computational unit (instance vs. pixel)**. Existing methods, e.g., RaVAEn, adopt pixel embedding, while we use instance mask embedding, leveraging SAM's advantage. Our ablation study (Table 1, SAM+CVA match vs. AnyChange) indicates that this factor is key to the performance difference.\n- **Matching direction (bidirectional vs. none)**. Existing methods are pixel-based and do not consider matching directions. Ours is instance-based, and matching direction is a key design for instance-based methods. We reveal bidirectional matching performs better and more robust via the following ablation study (see also response to W1 of Reviewer SmiM).\n- **Integration with SAM's promptability**. Integration with SAM's promptability has not been explored or even made possible by existing approaches, while we demonstrated a valuable integration with point prompt by our point query mechanism.\n\n||LEVIR-CD| S2Looking | xView2  | SECOND   |\n|--|:--:|:--:|:---:|:---:|\n| **Direction**          | F1 / Prec / **Rec** / **mask AR** | F1 / Prec / **Rec** / **mask AR** | F1 / Prec / **Rec** / **mask AR** | **F1** / **Prec** / **Rec** / **mask AR** |\n| bidirectional| 23.4 / 13.7 / 83.0 / 32.6 | 7.4 / 3.9 / 94.0 / 48.3 | 13.4 / 7.6 / 59.3 / 27.8   | 44.6 / 30.5 / 83.2 / 27.0 |\n| only from $t$ to $t+1$ | 17.7 / 10.1 / 72.8 / 1.3  | 9.0 / 4.8 / 85.6 / 32.1  | 15.3 / 9.0 / 49.8 / 27.3   | 41.2 / 31.2 / 60.6 / 14.8                 |\n| only from $t+1$ to $t$ | 23.6 / 13.6/ 88.7 / 35.9| 8.1 / 4.3 / 79.5 / 19.4 | 12.3 / 7.6 / 32.7 / 6.7 | 46.1 / 34.1 / 71.3 / 14.9                 |\n\nTo the best of our knowledge, **we did not claim the use of cosine similarity as our contribution**. On the contrary, we explicitly stated that cosine similarity is a suitable choice for measuring similarity (Lines 174-175).\n\n---\n\n**W1.2: The primary difference appears to be the use of SAM’s latent representations.**\n\nWe anticipated that reviewers might be concerned about this point. Therefore, we included two baselines: **SAM + CVA Match** and **SAM + Mask Match** to ablate the impact of SAM’s latent.\nOur AnyChange and two baselines use the same SAM's latent representations, however, **due to the different matching strategies**, our AnyChange is superior (see Table 1 and Ablation: Matching Strategy, Line 251-261).\n\n---\n\n**W2: The foundational observations Q1 and Q2 are only demonstrated with electro-optical images in the satellite domain, limiting the broader applicability of the proposed method to other domains or modalities.**\n\n- Our method can be also used in natural image domain. **Please check Figure 1 in the rebuttal PDF**. \n- The idea of utilizing intra-image and inter-image similarities in SAM's latent space can potentially aid in solving any vision tasks that require multi-temporal inputs, which is appreciated by Reviewer SmiM.\n\n- Modality limitation comes from SAM itself rather than our bitemporal latent matching and point query mechanism. Our design itself is modality-agnostic.\n\n\n- Although our method is evaluated solely on optical satellite images, it still has a broad range of applications. The following five NeurIPS main conference papers all only focus on electro-optical images in the satellite domain. \n    - [NeurIPS 2023] Cross-Scale MAE: A Tale of Multi-Scale Exploitation in Remote Sensing\n    - [NeurIPS 2023] SAMRS: Scaling-up remote sensing segmentation dataset with segment anything model\n    - [NeurIPS 2022] SatMAE: Pre-training Transformers for Temporal and Multi-Spectral Satellite Imagery\n    - [NeurIPS 2021] Spatial-Temporal Super-Resolution of Satellite Imagery via Conditional Pixel Synthesis\n    - [NeurIPS 2021] LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation\n\n---\n\n**Q1: How did you set up the dataset and model to prove Q2?**\n\nThe images were chosen from the LEVIR-CD dataset. We manually label all buildings of $t_2$ images as ground truth. The model is SAM (ViT-H) with default configurations. For $t_1$ image, we randomly select one building point as a point prompt and run SAM inference to obtain the building mask. For $t_2$ image, we adopt grid points (segment anything mode) to run SAM inference to obtain mask proposals. We compute all mask embeddings and then match $t_2$'s all mask embeddings by $t_1$'s building mask embedding, thus obtaining $t_2$'s building masks. Similarity is measured by cosine distance. Thresholding adopts the OTSU algorithm.\nThe F$_1$ score and recall rate as metrics are reported to confirm Q2.", "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": "cnukH6N5qA", "reviewer": "Reviewer_SmiM", "summary": "The authors address the problem of zero-shot change detection. While some models focus on zero-shot semantic segmentation, there hasn't been much work in the area of zero-shot change detection. The lack of availability of large change detection datasets makes it non-trivial to train such models from scratch using existing methods easily. The authors propose a training-free method to adapt SAM for change detection to circumvent this. They utilize the semantic space of SAM to find regions of change. More specifically, they propose \"Bitemporal Latent Matching\". For a given image, they extract the mask embeddings for each object proposal. They use the negative cosine similarity between two mask embeddings (at the same location at different times) as a measure of confidence of change. The region proposals are then sorted by their confidence scores and selected via thresholding. Their results show that they outperform other naive baselines for zero-shot change detection.", "questions": "Please address the concerns that are listed as weaknesses. A suggestion from my side would be to add some more qualitative results to the paper.", "limitations": "The authors adequately discuss the limitations and societal impact of their work.", "rating": 5, "confidence": 2, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "- The paper is well-written with only a few grammatical errors\n- The proposed method neatly avoids training making the approach quite resource-efficient. \n-  While the method is focused on Change Detection in Satellite Imagery, the proposed method could have more extended implications. This paper explores the idea of utilizing intra-image and inter-image similarities in SAM's embedding space to solve Change Detection in bi-temporal satellite images. This strategy can potentially aid in solving any vision tasks that require multi-temporal inputs.", "weaknesses": "- The paper lacks important ablation studies. For example: there is no experiment that shows the value of computing the change confidence scores bidirectionally as opposed to in a single direction\n- I think the example used in Figure 4 is not the best to demonstrate the efficacy of the model. It is hard to know if the model is segmenting all buildings or just the buildings that have changed (since all buildings are changed between the two images). It might have been better to show examples where the changes are more localized; for example: only a few buildings are missing in image 2 and the rest of the area is unchanged. \n\nMinor Grammatical Errors\n- Ln 132-133. \"we known\"\n- Ln 195 \"t denote\"", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "## To Reviewer SmiM\n\n**W1: ablation study for matching direction.**\n\nWe have added this ablation study. The results are as follows:\n|                        |              LEVIR-CD             |             S2Looking             |               xView2              |                   SECOND                  |\n|------------------------|:---------------------------------:|:---------------------------------:|:---------------------------------:|:-----------------------------------------:|\n| **Direction**          | F1 / Prec / **Rec** / **mask AR** | F1 / Prec / **Rec** / **mask AR** | F1 / Prec / **Rec** / **mask AR** | **F1** / **Prec** / **Rec** / **mask AR** |\n| bidirectional          | 23.4 / 13.7 / 83.0 / 32.6         | 7.4 / 3.9 / 94.0 / 48.3           | 13.4 / 7.6 / 59.3 / 27.8          | 44.6 / 30.5 / 83.2 / 27.0                 |\n| only from $t$ to $t+1$ | 17.7 / 10.1 / 72.8 / 1.3          | 9.0 / 4.8 / 85.6 / 32.1           | 15.3 / 9.0 / 49.8 / 27.3          | 41.2 / 31.2 / 60.6 / 14.8                 |\n| only from $t+1$ to $t$ | 23.6 / 13.6/ 88.7 / 35.9          | 8.1 / 4.3 / 79.5 / 19.4           | 12.3 / 7.6 / 32.7 / 6.7           | 46.1 / 34.1 / 71.3 / 14.9                 |\n\nWe can find that the performance of single-directional matching is sensitive to temporal order, e.g., mask AR of two single-directional matching on LEVIR-CD are 1.3% and 35.9%, respectively. This is because the class-agnostic change is naturally temporal symmetric which is exactly the motivation of our bidirectional design. This result also confirms generally higher and more robust zero-shot change proposal capability.\n\n\n---\n\n**W2: Figure 4 does not effectively demonstrate the model's efficacy because it is unclear if the model is segmenting all buildings or just the changed buildings.**\n\nThanks for this good suggestion. We have updated Figure 4 as you suggested, which includes unchanged and changed buildings simultaneously. Please check **Figure 3 in the rebuttal PDF** we newly uploaded. It is more clear to show that AnyChange can more accurately detect building changes with the help of the point query.\n\n---\n\n**minor grammatical issues**: we will proofread thoroughly to fix them.", "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": "WSWbk8LdtZ", "reviewer": "Reviewer_eUYB", "summary": "The authors propose AnyChange, a novel framework for zero-shot change detection in remote sensing imagery. This framework leverages the Segment Anything Model (SAM) and introduces a \"bitemporal latent matching\" method to identify changes between images taken at different times. AnyChange identifies changes by comparing the semantic similarities of image regions in SAM's latent space, eliminating the need for explicit training on change detection tasks. Furthermore, the model incorporates a point query mechanism that allows for interactive, object-centric change detection through user clicks. Experimental results demonstrate AnyChange's effectiveness in various change detection scenarios, highlighting its potential as a valuable tool for researchers and practitioners alike.", "questions": "* Is the framework generalizable? Is SAM able to detect non-building related changes? All the experiments on the datasets shown are related to building change detection.", "limitations": "Limitations are discussed in the appendix section.", "rating": 6, "confidence": 3, "soundness": 4, "presentation": 2, "contribution": 4, "strengths": "* One of the first works to propose zero-shot change detection of remote sensing imagery. The authors repurpose SAM for comparing satellite imagery captured at two different timesteps.\n* The authors propose bidirectional latent matching technique which compares latent embeddings of SAM mask proposals coming from the bitemporal satellite imagery. The authors empirically showed high correlation between intra-image and inter-image latent embeddings, which eventually enables detecting changes.\n* Optionally, the framework supports human in the loop point query mechanism to refine mask proposals from SAM and potentially reduce false positives.\n* Custom baselines are constructed from scratch and experiments on three change detection benchmark datasets show superior performance of AnyChange over the baselines.", "weaknesses": "* Several components of the paper are poorly explained. SAM uses the MAE's image encoder based on ViT. The image features extracted from such a model are downsampled due to the patching effect. How is the framework able to compute pixel level features for the mask proposals? Is there an interpolation step?\n* Although the idea is interesting and novel, it seems the model can easily be fooled by small radiometric changes between the timesteps or presence of other conditions such as clouds. SAM, which is extensively trained on consumer photographs, may easily confuse **seasonal changes**, which may not be relevant for a task. The experiments presented in the paper are on benchmark datasets and may not reflect practical applicability of the proposed framework.\n* How does the matching algorithm handle overlapping objects with an image? Such as a tree canopy covering a part of the road. Averaging image embeddings in such cases might lead to erroneous results.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "## To Reviewer eUYB\n\n**W1: How is the framework able to compute pixel level features for the mask proposals? Is there an interpolation step?**\n\nThere is a bilinear interpolation to upsample the feature map back to the original image size. Then we compute the mask embedding by averaging each position's embedding of the mask proposal. This can avoid quantized errors caused by changing mask geometry to adapt the feature map.\n\n---\n\n**W2.1: The model may be easily impacted by radiometric changes.**\n\nWe simulate the radiometric changes by randomly applying color jitter to the pre and post-event images, respectively. The results are listed as follows:\n\n\n|                    |              LEVIR-CD             |             S2Looking             |               xView2              |                   SECOND                  |\n|--------------------|:---------------------------------:|:---------------------------------:|:---------------------------------:|:-----------------------------------------:|\n| **condition**          | F1 / Prec / **Rec** / **mask AR** | F1 / Prec / **Rec** / **mask AR** | F1 / Prec / **Rec** / **mask AR** | **F1** / **Prec** / **Rec** / **mask AR** |\n| baseline           | 23.4 / 13.7 / 83.0 / 32.6         | 7.4 / 3.9 / 94.0 / 48.3           | 13.4 / 7.6 / 59.3 / 27.8          | 44.6 / 30.5 / 83.2 / 27.0                 |\n| w/ color jitter | 22.6 / 13.1 / 84.2 / 30.7         | 7.4 / 3.8 / 94.1 / 48.4           | 13.5 / 7.7 / 54.7 / 25.9          | 42.4 / 28.7 / 81.7 / 26.4                 |\n\n---\n\n**W2.2:The experiments presented in the paper are on benchmark datasets and may not reflect practical applicability of the proposed framework.**\n\nThe data of these four benchmark datasets were almost collected for real-world applications, e.g., urbanization, disaster damage assessment, natural resource monitoring. \n- **LEVIR-CD** is designed for ordinary building change.\n- **S2Looking** is designed for building change under side-looking / off-nadir / different viewpoint observation conditions. The data were collected from GaoFen (GF), SuperView (SV), and BeiJing-2 (BJ-2) satellites.\n- **xView2** aims to assess building damage changes, which includes 19 real disaster events with six disaster types (wildfire, earthquake, tsunami, hurricane, volcano, flooding). The data were collected from WorldView-2, WorldView-3, GeoEye-1.\n- **SECOND** is designed for land-use/land-cover changes, including 30 change types. The object changes involve the categories including non-vegetated ground surface, tree, low vegetation, water, buildings and playgrounds.\n\nWe have demonstrated our method on practical apllications in Figure 1. The Disaster Damage Assessment cases of Figure 1 include 2023 Kalehe DRC Flooding (first row) and 2023 Turkey-Syria Earthquake (second row) events, which are not included in any public benchmark dataset we used.\n\n\n---\n\n**W3: How does the matching algorithm handle overlapping objects with an image? Such as a tree canopy covering a part of the road. Averaging image embeddings in such cases might lead to erroneous results.**\n\nWe have experimented the mentioned case. Thanks to SAM's strong generalization on object segmentation. The result shows the tree canopy and road segments are segmented as three independent parts, please check **Figure 2 in the rebuttal PDF** we newly uploaded. The tree canopy or road's embedding only encodes information belonging to themselves. At least in this case, there is no erroneous result. \nThis implies that our matching algorithm does not need to deal explicitly with overlapping objects once the visual foundation model is strong enough in our framework.\n\n---\n\n**Q1: Is the framework generalizable? Is SAM able to detect non-building related changes? All the experiments on the datasets shown are related to building change detection.**\n\nYes, our design is general for change types. The SECOND dataset we have used in experiments is a land use/land cover change dataset, including 6 land-cover classes (non-vegetated ground surface, tree, low vegetation, water, buildings, and playgrounds) and up to 30 change types, extending beyond just building-centric changes.", "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": "RjX8ZoHU3R", "reviewer": "Reviewer_n4V1", "summary": "This paper proposed a new type of change detection model that supports zero-shot prediction and generalization on unseen change types and data distributions. The proposed method called AnyChange is built on the segment anything model (SAM) via our training-free adaptation method. By revealing and exploiting intra-image and inter-image semantic similarities in SAM’s latent space, the proposed AnyChange could perform change detection. The authors also designed 1 1-point query mechanism for AnyChange, leveraging SAM’s point prompt mechanism and our bitemporal latent matching for filtering desired object changes.", "questions": "Please refer to the item 1 and 4 in the weakness part. I am very skeptical about the ability of SAM to detect the object changes. From the existing experimental results, the proposed AnyChange did not show any superiority over the existing algorithms under the supervised setting.", "limitations": "The results of the current version are not that convincing to demonstrate that the proposed AnyChange is better than existing algorithms under the supervised setting. I am also skeptical about the ability of SAM to detect the minor changes, since SAM is mainly optimized by dense masks with some implicit semantics. From my point of view, SAM should not have a strong ability to identify small changes from its training nature.", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. Building a foundation model for object change detection is novel and interesting.\n2. The authors provided comprehensive experimental results on 4 various datasets. Some designed baselines such as different SAM variants (such as SAM+Mask Match, SAM+CVA Match, and DINOv2+CVA) are reasonable.\n3. The authors considered various settings to demonstrate the ability of the proposed method and better revealed the power of the proposed method.", "weaknesses": "1) I have one main concern about the capacity of the SAM. Due to SAM being optimized by dense masks with the high-level implicit semantics, I doubt whether SAM has the ability to detect some very minor changes if the authors are performing bitemporal latent matching in the feature space of SAM. The authors should provide more demonstrations about this. \n\n2) Have the authors evaluated the robustness of their proposed method to the illumination/color changes and viewpoint changes?\n\n3) As reported in Table 3, why did not the authors report the experimental results of using 10% GT and 100% GT? From current results, the proposed method did not demonstrate the advantage over existing algorithms even though I know they used fewer annotations. The accuracy is far below the existing results, which is not acceptable. The current results only demonstrate that the proposed method has a stronger zero-shot ability but I am curious about the upper bound of the proposed method if the proposed method is conducted at the same experimental setting. Furthermore, I do not really understand the meaning of \"This confirms the potential of AnyChange as a change data engine for supervised object change detection.\" in Lines 308-309.\n\n4) The authors should create one new table to combine some results (on the S2Looking dataset) from Table 1 and Table 3 to provide a better comparison. Table 1 demonstrates the zero-shot ability of some designed baselines and Table 3 reports the results under the supervised setting. However, I also noticed that AnyChange (Oracle) only achieved 62.2, 57.6, and 67.6 for F1, Prec. and Rec., respectively on S2Looking dataset. Meanwhile, the best results on the S2Looking dataset in Table 3 are 67.9, 70.3, and 65.7. I am very doubtful about the ability of the proposed method as the author said \"Oracles obtained via supervised learning have superior precision\" (Line 248-249). This observation also aligns with my doubt about whether it is suitable to perform change detection built on SAM.\n\nIf the authors could address such main concerns, I am willing to raise my score after the rebuttal.\n\nSome minor issues:\n\n1) The generalization ability of the proposed method to unseen images as they claim the proposed model is a foundation model.\n\n2) The figures should be reorganized to better illustrate the differences between the pre-event image and the post-event image.\n\n3) The best results in Table 3 should be bold.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "## To Reviewer n4V1\n\n**W1: I doubt whether SAM has the ability to detect some very minor changes.**\n\nAs you suggested, we demonstrate the case of tiny/minor changes, e.g., small vehicle changes. Please check **Figure 4 in the rebuttal PDF**.\nThe main observation is that directly applying AnyChange to the original image overlooked these subtle changes (**see the first row of Figure 4**). After we bilinearly upsampled red box region by 2x and then applied AnyChange to it, we found some tiny/minor changes can be detected (**see the second row of Figure 4**). This observation shows that our method has the ability on tiny object change detection, while it is not perfect. Future works can take our method as a strong baseline and improve this point further.\n\n---\n\n\n**W2: the robustness to the illumination/color changes and viewpoint changes**\n\n- **illumination/color changes** have been simulated by randomly applying color jitter to the pre and post-event images, respectively. \nWe used ViT-B as the backbone for fast experiments. The results are presented as follows. The performance jitter of mask AR is less than 2% (-1.9%,+0.1%, -1.9%, -1.4%). We think this sensitivity to color variance is acceptable.\n\n|| LEVIR-CD|S2Looking|xView2|SECOND|\n|-|:-:|:-:|:-:|:-:|\n| **condition**          | F1 / Prec / **Rec** / **mask AR** | F1 / Prec / **Rec** / **mask AR** | F1 / Prec / **Rec** / **mask AR** | **F1** / **Prec** / **Rec** / **mask AR** |\n| baseline| 23.4 / 13.7 / 83.0 / 32.6| 7.4 / 3.9 / 94.0 / 48.3| 13.4 / 7.6 / 59.3 / 27.8| 44.6 / 30.5 / 83.2 / 27.0|\n| w/ color jitter | 22.6 / 13.1 / 84.2 / 30.7| 7.4 / 3.8 / 94.1 / 48.4| 13.5 / 7.7 / 54.7 / 25.9| 42.4 / 28.7 / 81.7 / 26.4|\n\n- **viewpoint change** cannot be simply simulated in image space, therefore, the above experiments cannot be conducted. Sorry for this. However, we indeed have considered evaluating the viewpoint robustness of our method since S2Looking is exactly an off-nadir/side-looking (can be seen as different viewpoints) building change detection dataset. Compared with our baselines, AnyChange performs better under viewpoint changes.\n\n---\n\n**W3: Concerns about Table 3.**\n\nWe believe there is a misunderstanding in Table 3. Sorry for the confusion. Table 3 presents a **data-centric** experiment to explore the possibility of AnyChange as a change data engine. The network architecture is fixed ChangeStar (1x96). **The only difference in the comparison is the training labels used by the network**.\nOur model (**last row**) is the network trained on the predictions produced by our AnyChange. The compared models (**5th, 9th, 10th rows**) are the same network trained with 100%, 1%, and 0.1% of ground truth labels (GT). \n\n**W3.1: in Table 3, why did not authors report results of using 10% GT and 100% GT?**\n\nIn our original submission, we have reported the performance of models trained on 100% GT (see 5th row of Table 3).\nThe quantitative gap between the labels generated by our AnyChange and 100% GT has been revealed.\nWhen reducing the number of GT to 1% and 0.1%, our result is better than both of them, although 0.1% (4.7x10$^6$ pixels) still greatly exceeded the number of labels we used for prompting (3.5x10$^3$ pixels).\nTherefore, we decided that testing the 10% GT case was unnecessary.\n\n\n**W3.2/Q1: the proposed method did not demonstrate the advantage over existing algorithms even though I know they used fewer annotations. The accuracy is far below the existing results, which is not acceptable.**\n\n- 100% GT comes from manual labeling, our pseudo-label is generated by AnyChange with point prompts in a zero-shot way. Therefore, it is reasonable and acceptable that 100% GT trained model has superior accuracy than ours. This is because pure manual labeling is the upper bound of our data engine based on AnyChange.\n\n- The advantage of our method is 1) training-free. 2) enabling human-in-loop. 3) better zero-shot performance. 4) SOTA performance under the unsupervised setting. 5) better performance under the supervised setting with fewer annotations. All of these are not achieved by existing algorithms.\n\n\n**W3.3: upper bound of the proposed method when conducted in the same experimental setting.**\n\nWe conducted this experiment by training a pure pixel-based ViT-B-based AnyChange model in a fully supervised manner, exactly as done with the other methods in Table 3. \nThe F1, precision, and recall are **68.2**, 71.8, and 64.9, respectively.\nThis is the upper bound of AnyChange as a change detection network architecture.\n\n**W3.4: the meaning of \"This confirms the potential of AnyChange as a change data engine for supervised object change detection.\"**\n\nHere we claimed that AnyChange can be utilized as a more efficient and interactive labeling tool, functioning as a change data engine, capable of providing superior change pseudo-labels with fewer manual labels as prompts.\nThis claim is supported by Table 3 (9-11 th rows).\n\n---\n\n**W4.1: The gap between AnyChange (Oracle)[F1: 62.2] and previous best result [F1: 67.9].**\n- AnyChange (Oracle) is an instance-level, promptable change detector, **not specialized in pixel-level change detection**. In Table 1, we need to evaluate it at both pixel and instance levels. Therefore, its trainable modules consist only of LoRA layers and a change score network. This setup allows us to determine the upper bound of embedding dissimilarity while preserving its promptability and zero-shot instance prediction capabilities. If we drop this setup and train a pixel-level specialist, it achieves a better F1 of 68.2%.\n\n- Previous best is only a pixel-level change detection specialist with extra syntheic data pre-training and 100% GT fine-tuning.\n\n**W4.2: doubt the ability of the proposed method as authors said \"Oracles obtained via supervised learning have superior precision\"**\n\nWe only claimed this point in Table 1. AnyChange (Oracle) with supervised learning has higher precision (metric) than its zero-shot counterparts.", "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:
We are sincerely grateful for the reviewer's efforts and their constructive feedback. We appreciate the reviewers’ acknowledgment that
- [**pioneer**] Our work is one of the first works to propose zero-shot change detection of remote sensing imagery (eUYB).
- [**novel**] Our work is novel and interesting (eUYB, n4V1).
- [**broad impact**] Our idea can potentially aid in solving any vision tasks that require multi-temporal inputs (SmiM). 
- [**solid**] comprehensive experiments (n4V1, MDhn)
- [**practical**] robust methodology and promising results for AnyChange as a change data engine (MDhn, eUYB)

Our work aims to resolve an open problem: zero-shot change detection, which **has not been explored so far in the literature**. Our contribution includes:
- [**new task**] Our work is the first to provide a problem formulation, benchmarks, and models for zero-shot change detection.
- [**new roadmap**] We propose a training-free roadmap built upon SAM to achieve zero-shot change detection and point out its key designs.
- [**new tool**] Apart from superior zero-shot performance, our AnyChange model is promptable/interactive/human-in-the-loop and thus can be used as a more efficient change label tool. 
- [**new results**] We demonstrate our AnyChange with better zero-shot performance, SOTA unsupervised change detection performance, and comparable supervised change detection performance.

We have detailed point-by-point responses to each reviewer, you can check our rebuttal in your Ofiical Review section.  **We also provide a PDF file** containing four figures to better address your concerns. Please remember to download it.

Meta-review: The authors introduce the AnyChange model, which enables zero-shot change detection in remote sensing imagery. This model adapts the Segment Anything Model (SAM) using a training-free method called bitemporal latent matching, leveraging semantic similarities within and between images to identify changes without additional training. The authors demonstrate the effectiveness of AnyChange through extensive experiments, highlighting its potential as a valuable tool for remote sensing change detection tasks.

While the idea is intriguing, several reviewers raised concerns about the paper's novelty, methodology, and experimentation. Specifically, they questioned the applicability of the proposed method to small-size change detection, among others, citing a strong basis for their criticism. The authors addressed these comments during the rebuttal phase, but this did not result in a significant improvement in the 
overall rating from the reviewers.

Although I agree with the authors' claim that zero-shot change detection is first-time introduced in their work, overall novelty is questionable since the method proposed in this paper is a specific case of change vector analysis (or clustering) with vectors extracted from SAM embeddings and region proposals.
Decision: Accept (poster)