CamSAM2: Segment Anything Accurately in Camouflaged Videos
Authors: Yuli Zhou
Yawei Li
Yuqian Fu
Luca Benini
Ender Konukoglu
Guolei Sun
Venue: NeurIPS 2025 poster ()
Keywords: SAM2
Video camouflaged object segmentation
Primary area: applications

Abstract: Video camouflaged object segmentation (VCOS), aiming at segmenting camouflaged objects that seamlessly blend into their environment, is a fundamental vision task with various real-world applications. With the release of SAM2, video segmentation has witnessed significant progress. However, SAM2's capability of segmenting camouflaged videos is suboptimal, especially when given simple prompts such as point and box. To address the problem, we propose Camouflaged SAM2 (CamSAM2), which enhances SAM2's ability to handle camouflaged scenes without modifying SAM2's parameters. Specifically, we introduce a decamouflaged token to provide the flexibility of feature adjustment for VCOS. To make full use of fine-grained and high-resolution features from the current frame and previous frames, we propose implicit object-aware fusion (IOF) and explicit object-aware fusion (EOF) modules, respectively. Object prototype generation (OPG) is introduced to abstract and memorize object prototypes with informative details using high-quality features from previous frames. Extensive experiments are conducted to validate the effectiveness of our approach. While CamSAM2 only adds negligible learnable parameters to SAM2, it substantially outperforms SAM2 on three VCOS datasets, especially achieving 12.2 mDice gains with click prompt on MoCA-Mask and 19.6 mDice gains with mask prompt on SUN-SEG-Hard, with Hiera-T as the backbone. The code is available at https://github.com/zhoustan/CamSAM2.

CamSAM2: Segment Anything Accurately in Camouflaged Videos
Yuli Zhou 1.3
Yawei Li
Yuqian Fu!
Luca Benini
Ender Konukoglu' Guolei Sun
'ETH Zurich
Nankaii University
SUnieerstty of Zurich Universityy of Bologna
INSAIT, Sofia University "St. Kliment Ohridski"
Abstract
Video camouflaged object segmentation (VCOS), aiming at segmenting camou- flaged objects that seamlessly blend into their environment, a fundamental vision task with various real-world upplications. With the release of SAM2, video seg- mentation has witnessed significant progress. However, SAM2's capability of seg- menting camouflaged videos is suboptimal, especially when given simple prompts such as point and box. address the problem, we propose Camouflaged SAM2 (CamSAMM), which enhances SAM2's ability to handle camouflaged scenes with- out modifying SAM2's parameters. Specifically, we introduce decamouflaged token to provide the flexibility of feature adjustment for VCOS. To make full use fine-graie -rainedi and high-resolution features from the current frame and previous frames, we propose implicit object-aware fusion (IOF) and explicit object-aware fu- sion (EOF) modules, respectively. Object prototype generation (OPG) introduced abstract and memorize object prototypes with informative details using high- quality features from previous frames. Extensive experiments are conducted to validate the effectiveness of approach. While CamSAM2 only adds negligible learnable parameters to SAM2, substantially outperforms SAM2 on three VCOS datasets. especially achieving 12.2 mDice gains with click prompt on MoCA-Mass and 19.6 mDice gains with mask prompt on SUN-SEG-Hard, with Hiera-T as the backbone. The code is available http::/ggghubbccm//huuutaaaCCCC
Introduction
Camouflaged object detection (COD) and video camouflaged object segmentation (VCOS) aim to identify objects that blend seamlessly into their surroundings. Unlike standard object segmentation tasks, where objects typically exhibit clear boundaries and contrast with the background, camouflaged objects are naturally indistinguishable from the background. These tasks have various applications in wildlife monitoring, surveillance, and search-and-rescus operations [1, 2]. COD focuses on detecting camouflaged objects in individual images, while VCOS extends it to video sequences, adding the complexity of modeling temporal information across frames. Despite recent advancements in COD [3, 4, 5, 6, 7, 8, 9] and VCOS [3, 4, 10, 11, 12, 13, 14], the performance remains far from satisfactory compared to general segmentation tasks.
The recently introduced vision foundation model, Segment Anything Model (SAM2) [15], marks sigmificant advancement in video segmentation. SAM2 has learned rich and generalizable representa- tions for natural scenes on the SA-IB IB [16] (1 (M images, 1B masks) and SA-V [15] (50.9K vidcos, 35.5M masks) datasets. Therefore, features are optimized for natural scenes, while SAM2's ability of segmenting camouflaged objects is suboptimal, as in [17, 18]. Following the setting of SAM2, we apply point, box, mask prompts on the first frame of each video. Specifically, we randomly
"Corresponding author.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Prompt
Prompi encoder
Image encoder
Memory
Mask decoder
attention
Memory module
Prompt
Prompt encoder
Decam token
Mask decoder
encoder
Image
Memory attention
1OF
FOF
OPG
Memory module
Figure 1: Illustration of SAM2 and CamSAM2. Top: SAM2's segmentation of the camouflaged object is suboptimal, primarily because its feature optimization is biased toward natural videos, and its design does not account for the unique challenges inherent to VCOS. Bottom: CamSAM2 improves SAM2's ability to segment and track camouflaged objects by introducing a decamouflaged token, IOF enhance features with high-resolution features, and EOF and OPG to further enhance features by exploiting informative object details across time. CamS AM2 only adds limited number of parameters to SAM2 while keeping all SAM2's parameters fixed and fully inheriting SAM2's zero-shot ability. The segmentation result is overlaid in orange on the frame.
sample a point within the ground truth region as a point prompt, extract the tight bounding box as the box prompt, or use the full ground-truth mask as the mask prompt. As shown in Fig. 1, with point prompt, SAM2 segments only part of a camouflaged animal (hedgehog), indicating that there is still room for improvements in VCOS.
This paper aims to develop model for accurate segmentation in camouflaged videos, requiring both natural image understanding and effective identification of camouflaged objects in complex environments. To achieve this, we identify two core challenges in adapting SAM2 for VCOS: (1) SAM2 is optimized for natural scenes rather than camouflaged environments. (2) The architecture does not account for the complexities of segmenting and tracking camouflaged objects across time. For VCOS, accurately segmenting camouflaged objects for a frame requires: a) exploiting fine-grained and detailed features from the frame, and b) considering the temporal evolvement of fine-grained features from previous frames. For exploiting temporal information, SAM2 is equipped with memory module containing a memory encoder and memory bank. However, only row-resouuiion and coarse features are encoded into the memory, which is suboptimal for accurate VCOS.
To tackle the above limitations and fully keep SAM2's ability to process natural videos, we introduce Camouflaged SAM2, dubbed as CamSAM2. equipping SAM2 with the ability to effectively tackle VCOS, as depicted in Fig. 1. CamSAM2 includes a learnable decamouflaged token, which extends the token structure of SAM2 and provides flexibility to optimize features for VCOS without modifying SAM2's trained parameters. To exploit the fine mee-grained features of the frame, we propose the Implicir Object-aware Fusion (IOF) module, which leverages high-resolution features from the early layers of the image encoder to enhance the model's perception of fine-grained details. To make use of detailed features from previous frames, we further propose Object Prototype Generation (OPG) to abstract high-quality features within the object region into informative object prototypes through Farthest Point Sampling (FPS) and k-means. Those object prototypes are saved to memory for easy usage by the Explicit Object-aware Fusion (EOF) module that is designed to integrate explicit object-aware information across the temporal dimension. Our design avoids saving the high-resolution features in the memory and only adds negligible computations to SAM2 while accounting for a large amount of temporal information.
We conduct extensive experiments in $4 to validate the effectiveness of CamSAM2 on three VCOS benchmarks: two camouflaged animal datasets, MoCA-Mask [4] and CAD [19], and one camouflaged medical dataset, SUN-SEG [20]. Our experiments show that CamSAM2 significantly outperforms SAM2 by achieving improvements of 12.2/13.1 mDice scores with click prompt MoCA-Man for

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Hiera- - backbones, and 19.6 mDice gains with mask prompt on SUN-SEG-HaR for Hiera-T backbone. When directly evaluating CamSAM2 on CAD without further finetuning, we observe strong zero-shot ability. Since all SAM2's s weights remain unchanged, CamSAM2 totally inherits SAM2's capability on segmenting natural videos. summary, our contributions are three-fold:
(1) We propose CamSAM2 to equip SAM2 with the ability to segment and track camouflaged objects in videos while keeping SAM2's strong generalizability natural videos. (2) CamSAM2 introduces a decamouflaged token to achieve easy feature adjustments for the VCOS task without affecting SAM2's trained weights. To effectively exploit the crucial fine- -raiigd and high-resolution features from both the current frame and previous frames, we propose IOF, EOF, and OPG modules. (3) Our approach clearly outperforms SAM2 and sets new state-of-the- performance on public VCOS datasets. Experiments also show the strong zero-shot ability of CamSAM2 in the domain of VCOS.
2 Related work
2.1 Camouflaged object detection
Camouflaged Scene Understanding (CSU) focuses on interpreting scenes where objects blend closely with their backgrounds, such as natural environments like forests, oceans, and deserts. Early works in this field primarily involved the collection of extensive image and video datasets, such as CAMO [21], COD10K [22], NC4K [23], CAD [19], MoCA-Mask [4], and MoCA-Mashe Maskk-ssuudd [4], which laid the foundation for CSU. Traditional COD methods extract foreground-backgroundd features using optical features [24], color, and texture [25]. Deep learning has advanced COD with CNNs and
transformers. SINet [22] and SINet- V2 [26] enhance fine-grained cues by applying receptive fields and texture-enhanced modules, while DQNet [27] applies cross-modal detail querying to detect subtle features. Transformer-basee models like CamoFormer [9] leverage multi-scale feature extraction with masked separable attention, and WSSCOD [5] employs a frequency transformer and noisy pscudo labels for weak supervision. ZoomNeXt [3] further optimizes multi-scale extraction via a collaborative pyramid network. These advancements refine COD by integrating sophisticated architectures and diverse supervision strategies.
2.2 Video camouflaged object segmentation
VCOS [14, 28, 12, 29] extends COD to videos, introducing challenges from motion, dynamic backgrounds, and temporal consistency. Former VCOS models tackle these with motion learning, spatial-tempore attention, and advanced segmentation techniques to maintain object coherence across frames. Motion-guided models enhance segmentation by leveraging motion cues. IMEX [30] inte- grates implicit and explicit motion learning for robust detection. TMNet [28] refines motion features with a transformer- based encoder and neighbor connection decoder. Flow-SAM [31] uses optical flow as input or prompt, guiding SAM to detect moving camouflaged objects. Spatial-temporal attention enhances the tracking of camouflaged objects. TSP-SAM [10] and SAM-PM [11] improve SAM's ability to detect subtle movements. silii--Dnnai---eeppeeee information in spatial-temporal models, aiding balanced approaches. Assessing camouflage quality is also essential for VCOS. CAMEVAL [32] introduces scores evaluating background similarity and boundary visibility, refining datasets and improving model robustness. These advancements drive more accurate and effective VCOS systems.
[12] quantifies static and dynamic
2.3 Segment Anything Model 2
SAM2 [15] is a vision foundation model for promptable segmentation across images and videos. Com- pared SAM [16], which is limited to image segmentation, SAM2 offers significant performance leap in video segmentation SAM2 has demonstrated strong capabilities in many tasks, including medical image, video and 3D segmentation [33, 34, 35, 36, 37, 38, 39, 40], video object tracking and segmentation [41, 42], remote sensing [43], 3D mesh and point cloud segmentation [44], COD and
VCOS [17, 38, 39, 18]. In previous works [17, 18], although SAM2's performance on camouflaged video segmentation has surpassed most existing methods, these studies primarily focused on direct evaluation, lightweight lne-tuning, or integrating SAM2 with multimodal large language models. However, they did not address the fundamental architectural challenges of adapting SAM2 to VCOS,
3

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Rc
IOF
OPG
Cony.
MLP
FPS
Clustering
Conv.
Conv.
Conv.
Cross Atta.
RE
(b)
(c)
Implicit abject-aware Fusion
Explicit Object-aware l'usion
Object Prototype Generation
Image
Memory Alin.
Mask Dec.
Memory
Memory
Enc.
Bank
prompt
# Prompt Enc.
Mask Dec.
12T Attn.
B 2x
Trans. Conv.
post
Addition
Product
FMPP
Cuncarl
(8)
T21 Atmn
Ougas soken per mook
Training
Ougu tokurs
Frueen
Pronept loketa
Attn.
Attn.
tel: taken
Memory retrieval
Decan töken (T)
Figure Overall architecture of CamSAM2. CamSAM2 effectively captures and segments camouflaged objects by leveraging implicit and explicit object-aware information from the current or previous frames. includes the following key components: (a) the decamouflaged token, which extends SAM2's token structure to learn features suitable for camouflaged objects; (b) 1OF module to enrich memory-conditionee features with implicitly object-aware high-resolution features; (c) an EOF module to aggregate explicit object-aware features; and (d) OPG module, generating informative object prototypes, which guides cross- aatesntttnniinn in EOF. These components work together to preserve fine details, enhance segmentation quality, and track camouflaged objects across time.
leaving significant performance gap compared to other VOS tasks, especially when using simple prompts.
Method
We propose CamSAM2, equipping SAM2 with the ability to accurately segment camouflaged objects in videos while retaining SAM2's original capabilities. $3.1 briefly reviews the architecture of SAM2. From $3.2 $3.5, we describe CamSAM2 tailored for VCOS. With fixing SAM2's parameters, CamSAM2 proposes a learnable decamouflaged token, Implicit and Explicit Object-aware Fusion, and Object Prototype Generation to enhance feature representations, thus leading to improved performance, shown in Fig. 2.
3.1 Preliminaries
SAM2 [15] is a pioneering vision foundation model designed for promptable visual segmentation tasks. Different from SAM [16], SAM2 includes a memory module that stores information about the object from previous frames. It contains an image encoder, memory attention, prompt encoder, mask decoder, memory encoder, and memory bank. For each frame, the image encoder extracts representative visual features, which are then conditioned on the features and predictions of past frames. If point or box prompt is given, the prompt encoder encodes it into sparse or dense embeddings. then segments the prompted frame; if mask prompt given, SAM2 directly uses the mask as the current frame output. Exploiting memory- conorr--oondiiioned features and prompt embeddings, mask decoder outputs the segmentation mask. The memory encoder then updates the memory bank with the output mask and the unconditioned frame embedding to support the segmentation of subsequent frames. SAM2 is pre-trained on SA-1B [16] and further trained on SA-V [15], achieving strong performance across video and image segmentation tasks. For more details, please refer to [15].

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3.2 Decamouflaged token
Given video clip containing m frames, we denote all frames as [I-m++1... I..... I with ground-trune truth segmentation masks of (S,mm+1," S,, S.). Especially, L, is the cur- rent frame for the purpose of easy explanation. We use the image encoder to extract features for all frames, denoted as {Fr-m+1;"". F,,". Fr). Here, F, can be further represented as {F). F.,... F/-1), containing feature maps extracted from L different intermediate layers, where F/ RS x with Cj. fi.. and w representing channels, height, and width, respectively.
SAM2's output tokens include an object score (occlusion) token, an loU token, and mask tokens. To enhance SAM2's ability of segmenting camouflaged objects, introduce decamouflaged token T E IR lx256 X2 enabling it to optimize features for segmenting camouflaged objects. As depicted in Fig. 2, integrated with SAM2's output tokens, the decamouflaged token undergoes the same layers as output tokens within SAM2's mask decoder. After this, the output decamouflaged token denoted as TT. This token is updated through back-propagated gradients, while SAM2's 's weights remain frozen. T' is then passed through an MLP layer to participate in computing CamSAM2's final mask logits, which will be explained in $3.4.
learnable
3.3 Implicit object-aware fusion
Early-layer features from the image encoder capture high-resolution details, such as edges and textures, essential for distinguishing subtle differences between the camouflaged object and the background. These early-layer features are implicil object-aware, as features for background and non-relevant objects also exist with similar magnitude. In contrast, deeper layers focus on high-level semantic information. In SAM2, menory-conditioned features are computed by only conditioning high-level semantic features, without using detailed features from early layers. To this end, we propose an IOF module to fuse these implicit object-aware features with memory-conditiee
features.
For SAM2, three feature maps from the Hiera image encoder [45] are extracted for each frame, i.e., L = 3. We have Fl. F/. and F? for the current frame L. We denote the memory-com conditicod feature as Fy encoded by the nemoryyaatention module on F?, as in [15]. The high-resolution features F and F; are fused with Fmen via compression modules and point-wise addition to create refined feature representation F E R X where compression module C(-) consists of two convolutional layers, followed by an upsampling layer. This process is given by:
Flol = Co( + (IF)) + C2(F"""))
3.4 Explicit object-aware fusion
After obtaining F/0/, we further refine it by EOF, which exploits explicit object-aware information from the current frame and previous frames, through employing object mask logits and object prototypes (see $3.5). We have three steps to fuse informative features. First, feature Fiof, with shape Re xhg x wio is directly concatenated with SAM2's mask logits R,, which has shape I8 x X10, This concatenated feature is then processed through a convolutional layer to reduce the channels back to co, resulting the output with the original shape Re x x wo, denoted as:
Feol = Conv ([E)O) :R.)
Second, F goes through a cross-attention layer. Prototypes generated from previous frames, representing clustered camouflaged features, serve as informative priors to help distinguish the camouflaged object from its background. A cross-attention mechanism takes Foo query, and leverages these prototypes as keys and values, effectively exploiting the information within the object prototypes to refine F.o. This design naturally suppresses outdated or irrelevant prototypes: if prototype inconsistent or suboptimal (e.g., due occlusion or missing), receives lower attention and contributes less to the output. This cross-attention mechanism provides robustness and avoids overfitting to specific regions. Formally, we update F by conducting cross-attention with prototypes P. PPo,P1,.. P.-1} from previous frames, given by:
Fetin Frt Pt).
(3)
5

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Third, the cennttion-rffnne feature Fr is combined with the upscaled mask feature Fruask from SAM2 mask decoder. The upscaled mask feature first processed through a convolutional layer and then fused with Fatin via point-wise addition, as follows:
F = Ft + Conv(FFrash))
Finally, we calculate the mask logits R, of CamSAM2, by processing the output decamouflaged token T through an MLP layer, then performing point-wise product with the F as shown below:
R MLP(T') F.
(5)
This approach incorporates both implicit and explicit camouflaged information, which can enhance mask generation for more accurate segmentation for the VCOS task.
3.5 Object prototype generation
To effectively represent the camouflaged features within the mask (object) region, we employ Farthest Point Sampling (FPS) [46] to identify & points within the predicted mask region, which act as cluster centers. This approach ensures that the sampled points are well-distributed throughout the mask, capturing diverse and important characteristics of the camouflaged object. Then, we group all pixels in the predicted mask region into l clusters by conducting one-iteration k-means, using the sampled k points as initial centers. The prototype of each cluster represented as the mean of the spatial features of the points in the cluster. This prototype generation process is denoted as Fp. as shown in:
P [P 1 isi S k] = (F, R2),
(6)
where P. represents the camouflaged object prototypes extracted from high-resolution and detailed features for the frame I. The prototypes are concatenated and then saved in the memory, which will be used by EOF ($3.4) when segmenting the subsequent frames.
4 Experiments
4.1
Experimental setup
Our experiments are conducted on three video datasets: two popular camouflaged animal datasets, McCA-Mask [4] and CAD [19], and one camouflaged medical dataset, SUN-SEG [20]. The pioneering Moving Camouflaged Animals dataset (MoCA) [47] comprises 37K frames from 141 YouTube video sequences. The dataset MoCA-Mask is reorganized from the MoCA, containing 71 video sequences with 19,313 frames for training and 16 video sequences with 3,626 frames for testing, respectively, with pixel-wise ground-truth masks on every five frames. It also generates a MoCA-Mask-Pseuute dataset, which contains pscudo masks for unlabeled frames with a bidirectional optical-flow-bett flow-based consistency check strategy. The Camouflaged Animal Dataset (CAD) includes short videos in total that have 181 I hand-labeled masks on every five frames. SUN-SEG is the largest benchmark for video polyp segmentation, derived from daN-database [48]. It consists of a training set with 1 112 clips (19,544 frames) and two test sets: SUN-SEG-East BEG-Easy, containing 1 119 clips (17,070 frames), and SUN-SEG- SEG-Hard, comprising 54 clips (12,522 frames).
Datasets.
Training and inference. We simulate interactive prompting of the model in the training process, prompting on the first frame of the sampled sequence. Following the training strategy of SAM2, we use three types of prompts (mask, bounding box, 1-click point of foreground) for training, with the probabilities of 0.5, 0.25, and 0.25, respectively.
To train the model, we use a combined loss of binary cross-entropy (BCE) and dice loss for mask predictions across the entire video. This loss applies to both SAM2's ss mask logits R, and the Cam CMmS mask logits R,,, compared with the ground-truth mask S, of frame I, as follows:
Le Σ
[CRCE(Re, Si) LBCR (RS,
i=t=m+1
Ln
Σ
(Ri, + Liiee (R, S.))
i=t-m+1
L Le + LD,
(7)

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Table 1: Comparisons between method and existing approaches MoCA-Mask. CamSAM2 outperforms the existing method by achieving new state-of-the- performance. "SAM2-FT" refers version of SAM2 in which the mask decoder is fine-tuned. The results of all these methods (excluding SAM2) are from the corresponding publications. The best results are shown in bold. f: the higher the better, J: the lower the better.
Model
Backbone
Params (M)
Prompt
Sm
Fg
MAE
Fat
Em
mDice
mloU 1
EGNetans [55]
ResNet-50
111.7
54.7
11.0
3.5
13.6
$7.4
14.3
9.6
BASNe [56]
ResNet-50
87.1
56.1
15.4
17.3
59.8
19.0
13.7
CPD [57]
ResNei-50
12.1
61.3
PraNetanco [58]
ResNet-50
32.6
6
26.6
3.0
29.6
67.4
31.1
23,4
SINetann [22]
ResNet-50
48.9
59.8
23.1
2.8
25.6
69.9
27.7
20.2
SINet-V2ne: [26]
Res2Net-50
27.0
58.8
20.4
22.9
64.2
24.5
18.0
PNS-Netami
ResNet-50
142.9
52.6
5.9
8.4
$3.0
8.4
5.4
RCRNebage [60]
ResNet-50
53.8
55.5
13.8
3.3
15,9
S2.7
17.1
11.6
MG [6]]
VGG
4.8
$3.0
16.8
6.7
19.5
56.1
18.1
12.7
SLT-Net-LT bocn [4]
PVTv2-BS
82.3
63.1
75.9
27.2
ZoomNeXIguc [3]
PVTv2-B5
84.8
73.4
47.6
1.0
49.7
73.6
49.7
42.2
SAM22 [15]
Hiera- T
38.9
1-click
68.2
50.7
52.5
73.6
52.1
44.8
SAM2-FT
Hiera-T
I-click
70.2
54.5
56.0
76.8
CamSAM2
Hiera-T
39.4
1-click
75.2
61.7
7.3
63.7
82.0
64.3
54.6
SAM2 [15]
Hicra-T
38.9
box
81.5
69.9
70.9
89.4
62.3
SAM2-FT
Hiera-T
38.9
box
82.1
71.6
72.8
90.8
73.6
63.4
CamSAM2
Hiera-1 I
39.4
box
82.9
72.4
0.6
73.2
94.2
75.5
64.8
SAM-PMap
ViT-L
303.0
mask
72.8
56.7
81.3
50.2
SAM2 [15]
Hiera-1 T
38.9
mask
84.7
76.0
76.9
91.9
77.1
67.9
SAM2-FT
Hiera-1 T
38.9
mask
84.7
76.2
0.4
77.1
91.9
77.2
68.0
CamSAM2
Hiera-T
39.4
mask
86.2
78.7
79.6
96.2
80.2
70.5
Table 2: Detailed comparisons between SAM2 and CamSAM2 on MoCA-Mask. CamSAM2 consistently outperforms SAM2 for all considered prompt types and backbones. Improvements of CamSAM2 over SAM2 are shown in dark green.
Model
Prompt
Hicra-T
Hiera-S
mDice 1
mloU f
mDice
mloU
SAM2
52.1
44.8
54.9 68.0 (+13.1)
46.7
CamSAM2
1-click
64.3 (+12.2)
54.6 (+9.8)
58.8 (+12.1)
SAM2
72.7
62.3
73.7
63.8
CamSAM2
box
75,5 (+2.8)
64.8 (+2.5)
76.4 (+2.7)
66.1 (+2.3)
SAM2
77.1
67.9
80.3
70.7
CamSAM2
mask
80.2 (+3.1)
70.5 (+2.6)
81.4 (+1.1)
71.7 (+1.0)
where C is the final loss for our approach, summing the BCE loss Lo and the dice loss CD-
During inference, we provide a prompt at the first frame of a video, following [15, 11]. Our final output the average of the logits of SAM2 and CamSAM2 masks for the error correction. For more training and inferencing details, please see Appendix A.5.
Implementation details. The proposed CamSAM2 is implemented with PyTorch [49]. CamSAM2 initialized with the parameters SAM2. We freeze all parameters used in SAM2 and initialize other parameters randomly. We set betas (0.9, 0.999) for the optimizer Adam and use the learning rate le-3, We train CamSAM2 on NVIDIA RTX 4090 GPUs for 10 epochs. For camouflaged animal segmentation, we train the model using the MoCA-Mask-Pseudd training set and evaluate on the MoCA- Mask test set and CAD. During inference, we apply the 1-click, box, and mask prompts only the first frame of each video. For camouflaged polyp segmentation, model using the SUN-SEG training set and perform inference using the mask prompt on the first frame of each video the SUN-SEG-Eaa and SUN-SEG- Hard test sets.
train the
Evaluation metrics. We adopt seven evaluation metrics to measure the quality of predicted pixel- wise masks: S-measure (Sm) [50], F-measure (Fg) [51], weighted F-measure (F)') [52], mean absolute error (MAF) [53], E-measure (Fm) [54], mean Dice (mDice), and mean ToU (mIoU).
4.2 Experimental results
Results on MoCA-Mask. Tab. compares four promptable methods on MoCA-Mask. For fair comparison, fine-tune the mask decoder (4.2M parameters) of SAM2 on MoCA-Mask. CamSAM2
7

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CanSAME SAMA Frames
Figure 3: Qualitative comparisons between SAM2 and CamSAM2 using 1-click prompt with the Hiera-T backbone on two MoCA-Mask clips. From top to bottom: the input frames, SAM2's results, CamSAM2's results, and ground-truth masks. CamSAM2 demonstrates improved accuracy in VCOS, especially in complex backgrounds, as shown by the circles. Best viewed in color.
Table 3: Comparisons between method and existing approaches CAD.
Model
Backbone
Params (M)
Prompt
Sm f
P5
MAE +
Fg
Em
mDiie
mloU
EGNet [SS]
ResNet-50
111.7
61.9
29.8
4,4
35.0
66.6
32.4
24.3
BASNetgms [56]
ResNet-50 50
87.1
63.9
34.9
5.4
39.4
77.3
39.3
29.3
CPDan [57]
ResNet-50
62.2
28.9
4.9
66.7
33.0
23.9
PraNetxmn [58]
ResNet-50
32.6
62.9
35.2
4.2
39.7
76.3
37.8
29.0
SINetgo: [22]
ResNet 50
48.9
63.6
34.6
4,1
39.5
77.5
38.1
28.3
SINet-V2xan [26]
Res2Net-50
38.2
3.9
76.2
31.8
PNS-Nelguzi [59]
ResNet-50
142.9
65.5
32.5
4.8
41.7
67.3
38.4
29.0
RCRNeba1
ResNet-50
62.7
28.7
4,8
66.6
30.9
22.9
MG [61]
59.4
33.6
5.9
37.5
69.2
36.8
26.8
SL.--Ne--LT a0z
PVTv2-B5
82.3
69.6
48.1
3.0
52.4
84.5
49.3
40.2
ZoomNeXtaoe
PVTv2-B5
84.8
75.7
59.3
2.0
63.1
86.5
59.9
$1.0
SAM2 [15]
Hiera-T
38.9
I-click 1-click
75.7
58.3
3.3
62.2
81.4
59.2
48.9
CamSAM2
Hier-TT
39.4
77.1
62.2
3.2
68.1
83.9
62.6
50.7
SAM2,02 [15]
Hiera-T
38.9
box
85.4
77.3
1.7
79.5
95.1
77.8
66.7
CamSAM2
Hiera-T
39.4
box
87.2
79.5
1.3
81.4
96.3
79.6
69.2
clearly outperforms SAM-PM, SAM2, and the fine-tuned SAM2. Even with the I-click prompt, CamSAM2 still outperforms SAM-PM, which uses the mask prompt. The promptable methods clearly outperform other non-promptable models. Tab. further compares SAM2 and CamSAM2 with the Hiera-T and Hiera-S backbones. CamSAM2 consistently outperforms SAM2 across all prompt types. With the 1-click prompt, CamSAM2 achieves mDice/mloU gains of 12.2/9.8 /9.8 with Hiera-T backbone and 1 with Hiera-S backbone, and further improves mloU by 2.5/2.3 (box) and 2.6/1.0 1.0 (mask) over SAM2 on Hiera-T and Hiera-S backbones, respectively.
Fig. presents qualitative comparisons on two MoCA-Mask video clips using 1-click prompts with Hiera-T as the backbone. Fig. 4 shows at- tention maps from the last token-to-image cross- attention layer in the mask decoder, where the SAM2 output token or the decamouflaged token acts as the query and the image embedding as key and value. Compared SAM2, CamSAM2 produces more focused and expansive attention around target objects, highlighting its improved spatial awareness and the effectiveness of our design for VCOS. Despite CamSAM2 introduc- ing only a marginal parameter increase of 0.5M, delivers substantial improvements while keep- ing all SAM2 parameters frozen, fully preserving SAM2's original ability to segment and track ob- jects in natural scenes.
flower emah spider
hookechog
sand cut
cupperiead stake
Frame
GF CmSSMM2 sAMe
Figure 4: Attention map visualization from SAM2 and CamSAM2 using point prompts with the Hiera-T backbone. From top bot- tom: input frames, attention with SAM2 mask token, attention with decamouflaged token, and ground-truth masks. The higher attention regions are indicated by warmer colors.
Results on CAD. We evaluate the zero-shot per- formance CamSAM2 and SAM2 on the CAD using Hiera-T backbone with point and prompts, shown in Tab. 3. CamSAM2 consistently outperforms SAM2, especially with the 1-click prompt,

---

Table 4: Comparisons between CamSAM2 and SAM2 on SUN-SEG-Easy and SUN-SEG- Hard.
Table 5: Ablation study on the effectiveness of main components of CamSAM2. shows the effectiveness of each key component of Cam- SAM2.
Model
Sm t
PB 1 Pm
mDice f
Decam. Token
SUN-SEG-Eayy
IOF EOF OPG mDice t mloU f
SAM2 [15]
83.4
71.6
83.0
73.6
52.1
44.8
CamSAM2
88.3
82.6
93.4
84.3
54.9
47.0
SUN-SER SEG-SEGGHaaE Hard
55.2
47.5
55.9
47.9
SAM2 [15]
75.5
58.4
73.4
61.0
64.3
54.6
CamSAM2
86.4
78.2
91.2
80.6
Table 6: Impact of using different distance metrics for k-means in Object Prototype Gen- eration. Cosine distance shows superiority.
Table 7: Impact of using different number of prototypes in Object Prototype Generation.
Prototypes (k)
mDice t
mIoU f
Distance Metric
mDice f
mloU t
3
60.2
51.8
Euclidean Cosine
61.9
52.7
64.3
54.6
64.3
54.6
7
60.6
50.8
achieving improvements of 3.4 mDice and 1.8 mloU. With the box prompt, also shows clear gains of 1.8 mDice and 2.5 mloU. These results highlight CamSAM2's superior zero-shot performance, demonstrating its effectiveness for segmentation tasks with minimal user input.
Results on SUN-SEG. As shown in Tab. 4, CamSAM2 consistently outperforms SAM2 across all metrics on both SUN-SEG-Ean and SUN-SEG-Hard. Notably, it improves mDice by 10.7 on SUN-SEG-Easy (from 73.6 to 84.3) and by 19.6 on SUN-SEG-Hard (from 61.0 80.6), demon- strating strong capability in segmenting camouflaged polyps. These results confirm that CamSAM2 significantly enhances SAM2 across varying tasks of camouflage, highlighting its effectiveness and generalizability in VCOS.
4.3 Ablation studies
To understand the impact of each component in CamSAM2, we conduct ablation studies on MoCA- Mask using the Hiera-T backbone with the 1-click prompt. The goal is to measure the contributions of key components, including the decamouflaged token, IOF, EOF, and OPG. Additionally, we evaluated the effects of different distance metrics and prototype numbers in the OPG process.
Impact of key components. As shown in Tab. 5, each main component in CamSAM2 clearly contributes its high performance. Starting from baseline (SAM2), adding the decamouflaged token alone improves mDice from 52.1 to 54.9 and mloU from 44.8 to 47.0. Adding IOF further raises mDice 55.2 and mloU to 47.5. Using EOF brings mDice 55.9 and mloU 47.9. With all components included, the model performs the best, achieving 64.3 on mDice and 54.6 on mloU.
Effect of distance metric. We compare different distance metrics for k k-means clustering in OPG, shown in Tab. 6. Cosine distance performs better than Euclidean distance, likely due to its effectiveness in grouping camouflaged features by angular relationships rather than direct distances.
Influence number of prototypes We examine the impact of the number of prototypes as shown in Tab. 7. The results show that both fewer higher numbers of prototypes will reduce the performance due under-renye mnderrrepresentation or redundancy, respectively. is observed that found to be optimal for capturing essential informative details in camouflaged features.

---

5 Conclusion
In this paper, we introduce the CamSAM2, by equipping SAM2 with the ability to accurately segment and track the camouflaged objects for VCOS. While SAM2 demonstrates strong performance across general segmentation tasks, its performance on VCOS suboptimal due to a lack of feature optimization and architectural support for considering the challenges of VCOS. To overcome the limitations, we propose to add a learnable decamouflaged token to optimize SAM2's features for VCOS, well as three key modules: IOF for enhancing memory-condee contiiy-ood features with implicitly object-aware high-resolution features, EOF for refining features with explicit object details, and OPG for abstracting high-quald quality features within the object region into informative object prototypes. Our experiments on three popular benchmarks of two camouflaged scenarios demonstrate that CamSAM2 clearly improves VCOS performance over SAM2, especially with point prompts, while fully inheriting SAM2's s zero-shot capability. By setting new state-of state-of-the-arr performance, CamSAM2 offers more practical and effective solution for real-world VCOS applications.
Acknowledgments
This project partially supported by grant #2022-279 of the Strategic Focus Area "Personalized Health and Related Technologies (PHRT)" of the ETH Domain (Swiss Federal Institutes of Technol- ogy).
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13

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A Appendix
A.1 Training details
During the training process, each training video clip consists of 8 frames, the input frames are resized to 1024 1024, and the ground truths are resized to 256 x 256 since the raw predicted logits are 1/4 of the original size. We train CamSAM2 with batch size of 4. The predicted final mask interpolated to 1024 x 1024 and then encoded with the visual feature of the current frame as the memory feature in the memory bank for subsequent frames.
A.2 Comparative analysis across backhones and prompt types
Table 8: Detailed comparisons between CamSAM2 and SAM2 MoCA-Mask across various backbones and prompt types. CamSAM2 consistently outperforms SAM2 for all cases. Improve- ments are highlighted in dark green.
Model
Prompt
mDice f
mloU f
Hiera-T
SAM2
52.1
44.8 54.6 (+9.8)
1-click
CamSAM2
64.3 (+12.2)
SAM2
72.7
62.3 64.8 (+2.5)
box
CamSAM2
75.5 (42.8)
SAM2
77.1
67.9
mask
CamSAM2
80.2 (43.1)
70.5 (42.6)
Hiera-S
SAM2
54.9
46.7
1-click
CamSAM2
68.0 (+13.1)
58.8 (+12.1)
SAM2
73.7
63.8
box
CamSAM2
76.4 (+2.7)
66.1 (+2.3)
SAM2
80.3
70.7
mask
CamSAM2
81.4 (+1.1)
71.7 (+1.0)
Hiera-B+
SAM2
55.5
45.5
1-click
CamSAM2
68.0 (+12.5)
58.4 (+12.9)
SAM2
73.0
63.1
box
CamSAM2
75.1 (42.1)
64.9 (+1.8)
SAM2
77.8
68.5
mask
CamSAM2
81.0 (43.2)
71.2 (+2.7)
Hiera-L
SAM2
69.3
55,8
1-click
CamSAM2
73.5 (+4.2)
63.3 (+7.5)
SAM2
74.9
65.4
box
CamSAM2
76.2 (+1.3)
66.5 (+1.1)
SAM2
80.9
71.1
mask
CamSAM2
81.9 (+1.0)
72.1 (+1.0)
Tab. 8 compares the performance of SAM2 and CamSAM2 the MoCA-Mask dataset across various backbones (Hiera-T, Hiera-S, Hiera-B+, and Hiera-L) and prompt types (click, box, and mask). The results consistently demonstrate that CamSAM2 outperforms SAM2 across all scenarios, with improvement varying depending on the prompt type and backbone.
For click prompts, CamSAM2 achieves substantial improvements, particularly with smaller back- bones such as Hiera-T and Hiera-S. For instance, with Hiera-T, CamSAM2 achieves a significant improvement of 12.2 micc and 9,8 mloU, while with Hiera-S, the gains are even larger at 13.1
14

---

mDice and 12.1 mloU. These results highlight CamSAM2's ability to utilize minimal user input to enhance segmentation performance effectively.
For box prompts, CamSAM2 consistently maintains its advantage. With the Hiera-T backbone, CamSAM2 achieves gains of 2.8 mDice and 2.5 mloU. With Hiera-L. backbone, delivers consistent improvements.
For mask prompts, where the input is the most detailed, CamSAM2 continues to outperform SAM2. For example, with Hiera-T, CamSAM2 achieves gains of 3.1 mDice and 2.6 mloU, while with Hiera-B+, the improvements are 3.2 mDice and 2.7 mloU.
Overall, the results highlight CamSAM2's adaptability across different backbones and prompt types. Its ability to achieve notable improvements with minimal input, while consistently maintaining advantages as prompts become more detailed, underscores its practicality and effectiveness for real-world applications requiring accurate segmentation of camouflaged objects in videos.
A.3 Comparative analysis across point prompt with different numbers of clicks
We evaluate the point prompt by randomly selecting points from the ground-truth mask regions using fixed random seed of 42. Click prompts are evaluated on the MoCA-Mask dataset with varying numbers of clicks: 1, 2, 3, and 5. Across all settings, CamSAM2 consistently outperforms SAM2, as shown in Tab. 9, demonstrating its robustness and adaptability.
With the 2-click prompt, CamSAM2 achieves significant improvements of 16.4 mDice and 15.2 mloU with the Hiera-T backbone. Similarly, with the Hiera-S backbone, it achieves gains of 6.8 mDice and 5.9 mloU, effectively leveraging additional user input for enhanced segmentation accuracy. For the 3-click prompt, CamSAM2 continues to deliver improvements, achieving a 3.4 mDice gain with the Hiera-T backbone and 1.9 mDice gain with the Hiera-S backbone. These results showease CamSAM2's ability to utilize increasing user input effectively, maintaining its advantage in producing accurate segmentation outcomes.
With the 5-click prompt, while both models benefit from the additional user input, CamSAM2 still achieves noticeable gains. The Hiera-T backbone records improvement of 2.2 mDice and 1.9 mloU, while with the Hiera-S backbone, the gains are 5.4 mDice and 4.6 mIoU, highlighting CamSAM2's consistent effectiveness across varying input levels. These results emphasize CamSAM2's ability to adapt and maintain robust performance as the complexity of the interaction grows.
These results highlight CamSAM2's adaptability and consistent effectiveness across the varying number of user clicks. Its ability to achieve substantial improvements, while maintaining robust performance, underscores its practicality and scalability for real-world applications that demand precise segmentation of VCOS.
A.4
Comparison with SAM2-Adapter
To provide fair and comprehensive comparison, we evaluate CamSAM2 alongside SAM2- Adapter [38]. SAM2-Adapter is an approach to adapt SAM2 to downstream tasks and achieve enhanced performance. This method effectively integrates task-specific knowledge with the general knowledge learned by the model. During this process, only the adaptor layers and the mask decoder trained, while the rest of the image encoder is kept frozen. Since the official SAM2-Adaphe implementation uses Hiera-L as the backbone, we directly compare on the same backbone to avoid unintended modifications to their codebase. We re-train the SAM2-Adapter on the MoCA-Mask dataset using their provided settings. Results are reported in Tab. 10. Importantly, CamS AM2 and SAM2-Adapter represent two fundamentally different design philosophies for extending SAM2:
Evaluation granularity. SAM2-Adapter operates at the image level, making per-frame predictions independently. In contrast, CamSAM2 is designed for video level inference, where only the first frame receives a prompt, and subsequent frames are segmented using memory-augmented features that evolve over time. This enables prototype refinement and accumulated improvements across frames, which is a capability SAM2-Adapter lacks.
Prompt usage. SAM2-Adapter relies on learned prompt injected into different stages in the image encoder at every image during inference. In contrast, CamSAM2 follows the original SAM2 design
15

---

Table 9: Detailed comparisons between CamSAM2 and SAM2 with Hiera-T and Hiera-S backbones on MoCA-Mask using different numbers of click prompts.
Model
Prompt
mDice f
mloU f
Hiera-T
SAM2
52.1 64.3 (+12.2)
44,8
1-click
CamSAM2
54.6 (49.8)
SAM2
48.4
39.8 55.0 (+15.2)
2-click
CamSAM2
64.8 (+16.4)
SAM2
63.7
55.4
CamSAM2
3-click
67.1 (+3.4)
56.3 (+0.9)
SAM2
65.9
CamSAM2
S-click
68.1 (+2.2)
(+1.9)
Hiera-S
SAM2
54.9
46.7
1-click
CamSAM2
68.0 (+13.1)
58.8 (+12.1)
SAM2
56.8
47.8 53.7 (+5.9)
2-click
CamSAM2
63.6 (+6.8)
SAM2
71.7
61.7
3-click
CamSAM2
73.6 (+1.9
62.9 (+1.2)
SAM2
68.5
58,4
5-click
CamSAM2
73.9 (45.4)
63.0 (44.6)
Table 10: Comparison between SAM2-Adapter and CamSAM2 with Hiera-L the backbone.
Model
Prompt
S, t
Fig t
MAE
F3
Em
mDice
mloU
SAM2-Adapter [38]
68.4
38.7
0.9
43.0
80.0
43.2
35.8
CamSAM2
1-click
82.2
71.8
0.6
73.4
92.1
73.5
63.3
as a promptable segmentation model, accepting a single external prompt (e.g., a click) on the first frame and leveraging internal mechanisms to propagate the segmentation time. While usage of
prompts may raise faimess concerns, this a common and practical setting in semi-supervised video segmentation and is consistent with SAM2's prompt-driven paradigm.
In summary, CamSAM2 and SAM2-Adapter offer two distinct solutions. CamSAM2 enhances SAM2 for video by leveraging memory and minimal user guidance, while SAM2-Adapter adapts SAM2 to images through internal prompt learning. Although they address different use cases, our results demonstrate that CamSAM2 provides more effective and temporally coherent framework for VCOS, maintaining compatibility with SAM2's promptable design without modifying its parameters.
A.5 Additional training and inferencing details
As we mentioned in the $4.1, we adopt combined loss of binary cross-entropy (BCE) and dice loss for mask predictions across the entire video sequence. This loss applies to both the original SAM2 mask logits R, and our CamSAM2 mask logits Rç. Although the original SAM2 parameters remain frozen during training, we still apply supervision to its outputs. The reason is that the introduction of our learnable decamouflaged token changes the self-attention dynamics in the mask decoder: this token is concatenated with the original output tokens and jointly participates in self-attention and cross-attention layers (see Fig. 2(a) in the main paper). Consequently, the outputs R, are no longer guaranteed to be identical to those of the original SAM2. By supervising them, we ensure that SAM2's s predictions remain aligned with the segmentation intent and are not perturbed by the token interactions.
Inspired by HQ-SAM [62], we adopt a simple post-processing strategy where final mask prediction is obtained by averaging SAM2's mask logits RG and the CamSAM2 mask logits RG; which is calculated by the SAM2 mask token and the decamouflaged token. This strategy integrates SAM2's global representation with the fine-grained, camouflage-awee features extracted by proposed
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modules. Despite its simplicity, this approach effectively unifies global generalization and fine-grained object sensitivity.
Tab. 11 reports mloU scores on the Hiera-T' backbone. The first column shows the original SAM2 performance. The second and third columns report the mask predictions from the SAM2 token and the decamouflaged token within CamSAM2, respectively. The final column presents the result of averaging both. Each individual output mask already outperforms the SAM2 baseline, and their combination leads to further improvements across all prompt types, validating the effectiveness of our averaging strategy.
Table 11: Comparison of SAM2, individual token predictions within CamSAM2, and their combination. Results are reported as mloU on the Hiera-T backbone across three prompt types. "SAM2 baseline" refers to the vanilla SAM2 prediction. "SAM2 token" denotes the prediction generated from the SAM2 mask token, while "decamouflaged token" refers to the prediction from the decamouflaged token after integrating features from our proposed IOF, EOF, and OPG modules. "CamSAM2" is the final result obtained by averaging the mask logits calculated by the SAM2 token and the decamouflaged token.
Prompt
SAM2 baseline
SAM2 token
decamouflaged token
CamSAM2
1-click box
44.8
53.3
53.1
54.6
62.3
63.9
64.1 1
64.8
mask
67.9
70.3
68.4
70.5
Notably, the mask of the SAM2 output token within CamSAM2 yields better results than in the original SAM2 model, despite having identical parameters and no additional fine-tuning. This improvement stems from two key factors. First, all output tokens, including the SAM2 mask tokens and the decamouflaged token, are jointly processed in the same transformer, where they interact through self-attention layer and different cross-attention layers. Second, SAM2's architecture conditions the current frame's 's feature From on the previously predicted mask stored in memory, enriching the encoder's output over time. This mutual influence further enhances their individual representational capacity.
A.6 First frame performance analysis
We evaluate CamSAM2 on the first frame of each video clip on the MoCA-Mask dataset using 1 1-click point prompt with Hiera-T as the backhone. This setting isolates the model's ability to handle camouflaged object segmentation, on the first frame, the OPG module is naturally inactive, since there are no prior frames from which to extract prototypes. This makes the first frame an ideal testbed for evaluating the model's segmentation capability only without temporal information.
Table 12: First frame comparison between SAM2 and CamSAM2 with the point prompt.
Model
Sm f
MAE
Fg f
Em
mDice 1
mloU f
SAM2
72.2
56.7
7.4
58.1
75.9
58.6
51.1
CamSAM2
73.0
59.4
7.2
60.8
80.7
61.4
52.6
As shown in Tab. 12, CamSAM2 consistently outperforms SAM2 across all metrics on the first frame. This improvement is particularly meaningful, as it demonstrates that CamSAM2 enhances SAM2's segmentation capability even in the absence temporal cues, laying a stronger foundation for subsequent predictions. Since CamSAM2 stores the predicted mask of each frame into memory and uses it to generate object prototypes, these prototypes can progressively enhance segmentation in future frames within EOF, resulting in cumulative performance gains time, which shows that CamSAM2 further enhances the tracking ability of SAM2 for the VCOS task.
A.7 Efficiency analysis of OPG
To assess the runtime efficiency of CamSAM2, we profile its per-frame latency using the Hiera-T backbone on representative 106-frame camouflaged video from the MoCA-Mask test set. Tab. 13
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Table 13: Average runtime per frame and overhead breakdown of CamSAM2.
Component
Avg Time (ms)
Relative Percentage
SAM2
83.7
100.0%
FPS
3.5
4.2%
k-means
1.0
1.2%
OPG
4.6
5.5%
CamSAM2
89.7
107.2%
reports the average runtime breakdown and compares CamSAM2 against the baseline SAM2. Overall, CamSAM2 introduces only ms (7.2%) additional latency per frame relative SAM2, demonstrating that the method remains efficient. The OPG module, which consists of FPS sampling and 1-iter k-means clustering, accounts for the majority of the runtime overhead. All other added components, including IOF, EOF, and associated processing, together contribute approximately 1.4 ms (1.7%) per frame.
We profile the computational complexity in Tab. 14. CamSAM2 introduces an additional 14.4 GFLOPs, which is independent of different backbone architectures. Based the Hiera-T backbone, the total FLOPs increase from 137.2 GFLOPs to 151.5 GFLOPs, which corresponds to approximately 10% increase. This added cost results in significant performance gain of 9.8 mloU with point prompts. Moreover, when using the Hiera-L backbone, which image encoder requires 810 GFLOPs, the same 14.4 GFLOPs overhead accounts for only 1.7% increase total FLOPs. Even with such small relative increase, CamSAM2 still achieves notable 7.5 mIoU gain.
Table 14: FLOPs comparison between SAM2 and CamSAM2.
Module
FLOPs (G)
Image encoder
103.0
Memory encoder
5.0
Memory attention
27.4
Mask decoder
1.8
SAM2
137.2
CamSAMP extra
14.4
CamSAM2
151.5
These findings confirm that CamSAM2 remains efficient and is suitable for practical deployment, even in time-sensitive VCOS applications.
A.8 Additional analysis and ablation studies
Number of Decamouflaged Tokens. We conduct an ablation study on the number of decamouflaged tokens to evaluate its effect on representation quality. As shown in Tab, 15, increasing the number of tokens from I to results in a comparable performance mloU, This indicates that adding more tokens does not structurally improve the representation quality, but may introduce redundancy and additional computational cost.
Table 15: Impact of using different numbers of decamouflaged tokens.
Model
Number of Decam. Token
mloU
SAM2
44.8
CamSAM2
3
53.3
CamSAM2
54.6
Sampling and Clustering Strategies. evaluate the robustness and generality of the OPG module, experiment with different strategies for sampling and clustering. Specifically, compare (i)
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mloU Farthest Point Sampling (FPS) versus Average Sampling for selecting initial cluster centers, and (ii) k-means versus Gaussian Mixture Model (GMM) for clustering. Results are reported in Tab. 16. All combinations yield reasonable performance, confirming the flexibility of OPG. Notably, FPS better preserves spatial diversity among selected features, leading more representative prototypes.
Table 16: Impact of different sampling and clustering strategies in OPG.
Sampling
Clustering
Average Sampling
k-means
52.1
Average Sampling
GMM
52.7
FPS
GMM
53.6
FPS
k-means
54.6
These findings show that OPG not overly sensitive to specific implementation choices, while FPS sampling in combination with k-means clustering offers the best trade-off between representativeness and segmentation accuracy.
A.9
Architecture toggle between CamSAM2 and SAM2
video
Inpul
Prompt
Prompe
Image
Image eacoder
Memory stiention
Prompt encoder
Memory attention
Prompt cncoder
Memory module
Memory module
IOF
IOF
Decam token
Decam. taken
OPG
EOF
OPG
presh "EOE
Mask
Mask
decoder
CamSAM2 octput
SAM2 outpul
Figure 5: Illustration of the architecture toggle. The toggle switch enables or disables the proposed modules for VCOS containing the decamouflaged token, IOF, EOF, and OPG. Modules and flows in dashed lines indicate the disabled state.
To better illustrate the relationship between CamSAM2 and the original SAM2 pipeline, Fig. presents a side-by-side comparison. When the toggle is set to OFF, the proposed modules are completely bypassed, and the pipeline reverts to the SAM2 architecture. Importantly, since SAM2 parameters fixed during the training of CamSAM2, the model behaves identically to SAM2 in this mode, preserving its original structure and performance across various tasks. This design ensures compatibility and flexibility, allowing seamless integration without disrupting SAM2's performance.
A.10 Limitation
CamSAM2 is built upon the SAM2 architecture, inheriting both its strengths and limitations. While our proposed modifications significantly improve SAM2's performance in VCOS tasks, we do not alter the core architecture of SAM2. As result, CamSAM2 retains several of SAM2's known limitations. Specifically, it does not address SAM2's challenges in handling shot changes and long occlusions. Future work may consider integrating explicit motion information or relational modeling to further improve the robustness of the model.
A.11 Interpreting prototypes via cosine similarity maps
To improve interpretability, we visualize the cosine similarity between the object prototype from preceding frames and the current-frame feature map of two selected videos. As shown in Fig. 6, the
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similarity maps consistently highlight semantically meaningful object regions while suppressing the background, indicating that the prototypes abstract object-aware semantics and remain temporally consistent rather than drifting to spurious parts.
Figure 6: The cosine similarity map between the preceding frames prototype and the current frame feature map.
A.12 More qualitative result visualization
Frames
CamSAM2 SAM2
GT
Figure 7: Qualitative comparisons between SAM2 and CamSAM2 using mask prompt with the Hiera-T backbone on two video clips of SUN-SEG-Hard. From top to bottom: the input frames, SAM2's results, CamSAM2's results, and ground-truth masks. CamSAM2 demonstrates improved accuracy in segmenting camouflaged polyps, as shown by the circles. Best viewed in color.
Fig. 7 presents two qualitative example video clips SUN-SEG-Hard, comparing the segmentation performance of SAM2 and CamSAM2 using mask prompts with the Hiera-T backbone. Compared with SAM2, CamSAM2 can segment polyps more accurately, including more precise regions and boundaries.
Fig. 8 and Fig. 9 present more comprehensive qualitative results for six video clips in MoCA-Mase comparing the segmentation performance of SAM2 and CamS using 1-click point prompts with the Hiera-T backbone. The results clearly demonstrate CamSAM2's advantages segmenting camouflaged objects in videos. For instance, in the first (stick insect) and second (rusty spotted cat) examples from Fig. SAM2 able to segment parts the camouflaged objects; however, CamSAM2 significantly outperforms by successfully segmenting larger and more complete regions of the objects. Furthermore, in the third (pygmy seahorse) example, SAM2 produces an incorrect segmentation, failing to segment the camouflaged object accurately, while CamSAM2 still manages to segment the
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correct regions of the object. These findings highlight CamSAM2's ability to both enhance accuracy cases where SAM2 performs moderately well and to maintain reliable segmentation performance in challenging scenarios where SAM2 fails. This underscores CamSAM2's robustness and effectiveness tackling diverse and complex tasks VCOS.
Frames
SAM2
CamSAM2
GT
Frames
SAM2
CamSAM2
GT
Frames
SAM2
CamSAM2
GT
Figure 8: More qualitative comparisons between SAM2 and CamSAM2 using 1-click point prompt with the Hiera-T backbone on three video clips. From top bottom: the input frames, SAM2's results, AM2's results, and ground-truth masks. CamSAM2 demonstrates improved accuracy in segmenting camouflaged objects, as shown by the circles. Best viewed in color.
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Frames
SAM2
CamSAM2
GT
Frames
SAM2
CamSAM2
GT
Frames
SAM2
CamSAM2
GT
Figure 9: More qualitative comparisons between SAM2 and CamSAM2 using 1-click point prompt with the Hiera-T backbone on three video clips. Best viewed in color.
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Reviews:
[{"review_id": "qYLFh1qjQY", "reviewer": "Reviewer_o2wz", "summary": "This paper proposes the CamSAM2 model, which enhances SAM2 for video camouflaged object segmentation (VCOS) by introducing several key components: a learnable de-camouflaging token, an Implicit Object-aware Fusion (IOF) module, an Explicit Object-aware Fusion (EOF) module, and an Object Prototype Generation (OPG) module. The model retains all original SAM2 parameters and introduces only a small number of learnable parameters. Despite this, CamSAM2 achieves strong performance on multiple datasets (MoCA-Mask, CAD, SUN-SEG), while preserving SAM2’s zero-shot generalization and natural video segmentation capabilities.", "questions": "Please see **Weaknesses**.", "limitations": "yes", "rating": 2, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "**Strengths**\n\n- Strong empirical results: Demonstrated superior performance compared to SAM2 on several standard benchmarks.\n- Good generalization: Evaluation on multiple datasets shows consistent improvements and robustness across diverse scenarios.\n\n---\n\n**Major Weaknesses**\n\n1. Limited novelty: The main contributions are combinations of existing techniques:\n- IOF performs cross-layer feature fusion, a well-known strategy.\n- The de-camouflaging token is similar to virtual tokens used in fine-tuning.\n- EOF employs explicit prototype refinement, while OPG relies on standard clustering techniques (e.g., farthest point sampling, k-means).\n\nOverall, the proposed components are adaptations or recombinations of established ideas, limiting the conceptual novelty.\n\n2. Potential inefficiency: The introduction of multiple modules, especially OPG with FPS and k-means clustering, may introduce considerable inference overhead, raising concerns about deployment in time-sensitive applications.\n\n3. Insufficient analysis and ablations:  \n\n- No exploration of different numbers of de-camouflaging tokens.\n- Lack of analysis on the impact of different sampling and clustering strategies.\n- No prototype visualization or interpretability discussion, which would help understand the model's internal behavior.\n\n---\n\n**Minor Weaknesses**\n\n- Figure 2: The component labeled \"I\" is undefined, and the de-camouflaging token visualization is confusing.\n      \n- Unsupported claims: The model is claimed to retain natural image segmentation performance, but no experimental results are provided to validate this.\n      \n- Figure 3: Ground truth (GT) visualizations contain potential errors or unclear rendering that could mislead interpretation.", "was_revised": true, "final_justification": "Thank you to the authors for the detailed and thoughtful rebuttal. I appreciate the effort and clarity with which the response was written.\n\nHowever, after carefully considering the rebuttal and revisiting the paper, I find that several of my core concerns remain unaddressed. I elaborate on them below:\n\n**Lack of Conceptual Novelty**\n\nThe core components of CamSAM2—namely the decamouflaged token, Implicit/Explicit Object-aware Fusion (IOF/EOF), and Object Prototype Generation (OPG)—are essentially adaptations or recombinations of widely established techniques, commonly used in segmentation and video object tracking:\n\n- **IOF**, which fuses multi-scale features, follows a standard design pattern found in numerous architectures such as *UPerNet* and *CamoFormer* (TPAMI 2024), both of which target camouflaged object detection.\n- The proposed **decamouflaged token** functions similarly to **virtual tokens** or **learnable prompts**, as explored in recent works such as *SAM-PM* [1] and *SAM-Adapter* [2]. These prompt-based fine-tuning strategies are now commonplace in SAM-derived models.\n- The **OPG** module, based on *Farthest Point Sampling* (FPS) and *k-means clustering*, is highly reminiscent of prototype-based memory techniques seen in models like *AOT* [3] and *XMem* [4], which have been extensively studied in video object segmentation and memory-augmented frameworks.\n\nIn essence, the method does not introduce novel algorithmic principles or modeling paradigms. Rather, it assembles previously known components into a task-specific pipeline. While this engineering effort may be useful in practice, it does not meet the NeurIPS bar for conceptual or methodological innovation.\n\nReferences\n\n [1] *SAM-PM: Enhancing Video Camouflaged Object Detection using Spatio-Temporal Attention*, CVPRW 2024\n\n [2] *SAM-Adapter: Adapting SAM in Underperformed Scenes*, arXiv 2023\n\n [3] *AOT: Associating Objects with Transformers for Video Object Segmentation*, NeurIPS 2021\n\n [4] *XMem: Long-Term Video Object Segmentation with an Atkinson-Shiffrin Memory Model*, ECCV 2022\n\n\n\n**Misleading Claim of Being “First SAM2 for VCOS”**\n\nThe submission claims to be the *first* to adapt SAM2 for video camouflaged object segmentation (VCOS). However, this is contradicted by existing literature:\n\n- [5] *When SAM2 Meets Video Camouflaged Object Segmentation*, which presents a comprehensive evaluation and adaptation of SAM2 for VCOS.\n- [6] Tang & Li, *Evaluating SAM2’s Role in Camouflaged Object Detection: From SAM to SAM2*, which also proposes SAM2 adaptations for COD and VCOS.\n\nBoth works predate CamSAM2 and are cited in the submission. The “first” claim is therefore factually inaccurate and weakens the originality and positioning of the paper.\n\n\n\n**Trivial Task-Specific Design**\n\nThe proposed modifications are narrowly tailored for VCOS and show limited signs of generality:\n\n- There is no evidence that the approach extends to other fine-grained segmentation tasks or that the components (e.g., decamouflaged token, OPG) offer improvements beyond the VCOS setting.\n\nThus, the framework appears to be an ad hoc design for a specific downstream task, assembled from task-specific heuristics and lacking broader impact or general-purpose applicability.\n\n\n\nDespite solid empirical performance on VCOS benchmarks, the paper’s contributions remain derivative, task-specific, and lacking in conceptual depth. The empirical results alone do not compensate for the absence of methodological novelty or general insight.  Accordingly, I have decided to lower my score.", "rebuttal": "Thank you for your valuable suggestions and for recognizing the strengths of our work, including the strong empirical performance and the generalization ability of CamSAM2 across diverse VCOS scenarios. Below, we respond in detail to your concerns.\n\n---\n\n## W1. Novelty clarification\nCamSAM2 introduces a cohesive and task-specific redesign tailored for the challenging VCOS task. Concretely, CamSAM2 addresses two key limitations of SAM2 in the VCOS scenarios: (i) SAM2 is optimized for natural scene segmentation and struggles with the fine-grained, low-contrast textures characteristic of camouflaged objects; (ii) SAM2's memory module stores only coarse, low-resolution features, which impairs its ability to maintain temporal consistency across time.\n\nTo tackle these, we introduce a set of interdependent modules, which are co-designed rather than independently stacked. This integration in a frozen SAM2 pipeline is novel and non-trivial. To the best of our knowledge, CamSAM2 is the first work to successfully and efficiently adapt SAM2 to the VCOS task, achieving both architectural innovation and practical performance gains. \n\n---\n\n## W2. Efficiency concerns of OPG (FPS and k-means)\nThank you for highlighting the potential runtime overhead introduced by our proposed modules, especially the OPG. To address this, we profile CamSAM2 with hiera-T as the backbone on a 106-frame camouflaged video from the MoCA-Mask test set, and report the average per-frame latency in Table 1. For comparison, we also include the original SAM2 per-frame runtime.\n\n| Component   | Avg Time (ms) | Relative Percentage |\n|:-------------|:---------------:|:---------------------:|\n| SAM2        | 83.7          | 100.0%              |\n| FPS     | 3.5           | 4.2%                |\n| k-means | 1.0           | 1.2%                |\n| OPG         | 4.6           | 5.5%                |\n| CamSAM2     | 89.7          | 107.2%              |\n\n**Table 1. Average runtime per frame and overhead breakdown of CamSAM2 compared to original SAM2.**\n\nThese results confirm that CamSAM2 introduces only 6 ms (7.2%) additional latency per frame compared to SAM2. As you correctly noted, the OPG module (comprising both FPS and 1-iter *k*-means clustering) is indeed the dominant component of overhead. Apart from OPG, all other components, including IOF, EOF, and associated processing, together contribute only approximately 1.4 ms (1.7%) on average per frame. These results demonstrate that CamSAM2 remains efficient and is suitable for practical deployment, even in time-sensitive VCOS applications.\n\n---\n\n## W3. Insufficient analysis and ablations\n\n### 1. The number of the decamouflaged token  \nThanks for your suggestion. We conduct an ablation study by increasing the number of tokens to 3 and observed a comparable result in mIoU, as shown in Table 2. Adding more decamouflaged tokens does not structurally improve the representation quality but may introduce redundancy and computational cost.\n\n\n| Model    | Number of Decam. Token | mIoU |\n|:-------------|:---------------:|:---------------------:|\n| SAM2     | -                       | 44.8 |\n| CamSAM2  | 3                       | 53.3 |\n| CamSAM2  | 1                       | **54.6** |\n\n**Table 2. Impact of using different numbers of decamouflaged tokens.**\n\n---\n\n### 2. Sampling and clustering strategies\nTo assess the robustness and generality of OPG, we experiment on different sampling and clustering strategies:\n(i) Farthest Point Sampling (FPS) vs. Average Sampling for selecting initial cluster centers, and (ii) *k*-means vs. Gaussian Mixture Model (GMM) as the clustering algorithm.\n\nAs shown in Table 3, all combinations yield reasonable results. Compared to average sampling, FPS better preserves spatial diversity among selected features, leading to more representative and discriminative prototypes. These results still confirm that OPG is flexible and not overly sensitive to specific implementation choices, which enhances its applicability.\n\n\n| Sampling         | Clustering  | mIoU |\n|:-------------|:---------------:|:---------------------:|\n| Average Sampling | *k*-means   | 52.1 |\n| Average Sampling | GMM         | 52.7 |\n| FPS              | GMM         | 53.6 |\n| **FPS**          | ***k*-means** | **54.6** |\n\n**Table 3. Impact of different sampling and clustering strategies in OPG.**\n\n---\n\n### 3. Prototype visualization and discussion\nThanks for pointing this out. Following your suggestion, we compute the similarity map between each prototype of previous frames and the feature map of the current frame. These maps reflect how each prototype activates spatially over the image.\n\nOur visualizations show that different prototypes consistently activate on semantically meaningful object regions. For example, the prototype shows strong activations (high similarity) precisely on the camouflaged object, while suppressing the background. This suggests that the prototypes successfully abstract object-aware semantics, supporting their effectiveness in guiding the cross-attention in EOF. Although visual examples cannot be included in this rebuttal, we will add these visualizations and related analysis in the final version of the paper.\n\n---\n\n## Minor Weaknesses\n\n1. Thanks for your suggestions, we will refine the figures in the final version.\n\n2. We appreciate your attention to this point. To clarify, in Sec. 7 of the Supple. Material, we describe the mode switch between CamSAM2 and SAM2 at inference. When the toggle is off, all parameters introduced by CamSAM2 are deactivated, and the model functions identically to SAM2 without any parameter modifications. We also test on the SA-V test set and get the same J\\&F result. This design ensures full architectural compatibility and serves as the basis for our claim about retaining the original segmentation capabilities on natural images and videos.\n\n3. Thank you for pointing this out. The camouflaged objects in our benchmark are indeed highly challenging to perceive due to their nature. The ground-truth masks in Fig. 3 in the main paper are sourced directly from the MoCA-Mask dataset, which provides high-quality human-annotated segmentation masks. While the visualization may appear unclear in certain cases because the objects are highly camouflaged, the annotations are accurate and widely accepted in the community."}, {"review_id": "nPVPXrT2EC", "reviewer": "Reviewer_TQ6w", "summary": "This paper proposes a method to adapt SAM2 for video camouflaged object segmentation by using a learnable de-camouflaged token to optimize features for this task without modifying the base architecture's parameters. The method also utilizes high-resolution features from the visual encoder to capture fine details. Additionally, it leverages high-resolution information from previous frames through a newly introduced object prototype generation module. Empirical validation is conducted on three datasets to support the proposed claims.", "questions": "Some suggestions:\n\n* To better highlight the novelty of the contribution, the major components (e.g., Decam. tokens, OPG) and minor components (e.g., IOF, EOF) could be clearly distinguished. Clarifying this distinction would help readers appreciate the significance of the major contributions without having them overshadowed by the minor ones.\n\n* The rationale behind certain design choices could be further elaborated. For instance, was the use of 1×256 Decam. tokens determined through ablation studies or based on intuition? Similarly, was the value of k=5 tuned using MoCA-Mask and then applied uniformly across all datasets?", "limitations": "Limitations are in supplementary document.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths: \n\n* The paper is well-presented, with a clear and logical flow that makes it easy to follow. Figure 1 effectively illustrates the main architectural modifications at a high level, providing an intuitive understanding of the proposed approach. Figure 2 clearly explains the detailed architecture. Overall, the writing is coherent and contributes to the paper’s presentation.\n\n* The paper provided mathematical formulations with appropriate notations to explain the proposed components.\n\n* The paper supported the claims with comprehensive experiments on three benchmark datasets and more ablation experiments. The results showed noticeable improvement over SOTA approaches and SAM2. \n\n\nWeaknesses: \n\n* Based on Figure 2 and Equation 1, the proposed IOF module appears to be a customization of the Feature Pyramid Network, which is widely used in segmentation models as an additional component to generate high-resolution features. I might be missing something, but I could not identify the novelty of this component.\n\n* Certain design choices that could be important for reproducing or extending the work are not explained or justified in the main text—such as the shape of the Decam. tokens, whether the number of prototypes is fixed or adaptive across datasets, and how these values are determined.\n\n* The comparison between the CamSAM2 mask, the SAM2 mask, and their average is crucial for understanding the specific improvements or capabilities introduced by CamSAM2. However, this analysis is missing from the main text and is only presented in the supplementary material.\n\n* Minor typos, e.g., line 109: promotable -> promptable.", "was_revised": true, "final_justification": "The authors' response has adequately addressed the concerns raised in my initial review. Accordingly, I am pleased to revise my rating in recognition of their efforts. I extend my best wishes for the success of their work.", "rebuttal": "We are glad to hear that our paper is well-presented, with clear architectural diagrams, coherent mathematical formulations, and strong empirical validation across multiple datasets. Below, we address your concerns point by point. \n\n## W1: IOF novelty clarification\nWe appreciate your comment. The IOF module is designed to address a key limitation in SAM2, where the early-stage high-resolution features are not fully utilized during the segmentation process. These features contain fine-grained texture and boundary details are especially crucial for VCOS. To exploit this, IOF fuses early-layer high-resolution features with memory-conditioned semantic features, thereby enhancing the model's ability to perceive subtle camouflage cues. While IOF may share structural similarities with general fusion mechanisms (e.g., FPN), its motivation, integration strategy, and application context are specifically tailored for VCOS. To the best of our knowledge, this is the first work to incorporate early-layer high-resolution features into SAM2 for VCOS. Our ablation study (Table 5 in the main paper) shows that adding IOF brings a non-trivial performance gain (e.g., +0.5 mIoU), highlighting both its necessity and effectiveness.\n\n---\n\n## W2: Explanation of design choices\nThanks for pointing this out. We clarify these design decisions as follows:  \n\n1. Decamouflaged token: The decamouflaged token is designed with a shape of 1 × 256. As illustrated in Fig. 2 in the main paper, this decamouflaged token extends SAM2's token structure, aligning with the default embedding dimension of output tokens in SAM2. This ensures compatibility with the existing self-attention structure without modifying SAM2's architecture.  \n2. Number of prototypes in OPG: The number of prototypes in OPG is fixed (set to k = 5) across all datasets. We initially tuned this value on MoCA-Mask and found it to generalize well, as shown in our ablation study in the main paper Table 7. Our goal is not precise clustering, but to abstract object-aware prototypes to guide cross-attention in EOF. A fixed k also simplifies inference and avoids introducing dataset-specific hyperparameters.  \n\nWe will clearly add these explanations in the final version. In addition, we will publicly release our code to support reproducibility and transparency.\n\n---\n\n## W3: On the placement of mask comparisons\nThanks for the suggestion. In the final version, we will consider moving this analysis into the main paper to better showcase how our design benefits segmentation accuracy.\n\n---\n\n## W4: Minor typo\nThanks for pointing out our typo. We will fix it and carefully proofread the entire manuscript to eliminate other minor errors in the final version.\n\n---\n\n## Q1: Some suggestions\nThank you for the suggestion. We agree that distinguishing major and minor components can help clarify our contributions. We will emphasize this distinction more clearly in the revised version.\n\n---\n\n## Q2: Explanation of design choices\nThanks for your question. Please refer to W2 in detail."}, {"review_id": "Im9ptJeLEv", "reviewer": "Reviewer_rr2s", "summary": "In this paper, the authors address the suboptimal performance of SAM2 in Video Camouflaged Object Segmentation (VCOS), particularly when provided with simple prompts like points or boxes. They propose Camouflaged SAM2 (CamSAM2), a method that enhances SAM2’s capabilities for camouflaged scenes without altering its original parameters. Specifically, CamSAM2 introduces an implicit object-aware fusion (IOF) module, an explicit object-aware fusion (EOF) module, and an object prototype generation (OPG) mechanism. Extensive experiments are presented to demonstrate the effectiveness of the proposed approach.", "questions": "1) I question why the authors compute loss twice in their training strategy. If CAMSAM2's prediction map can directly calculate loss with the ground truth, what is the benefit of computing an additional loss with SAM2? The authors have not discussed this design choice in their ablation study.\n2) The authors discuss using box and 1-click prompts in their experiments. However, we know that camouflaged object detection aims to find camouflaged targets without prior location information. Providing such prompts essentially tells the model the target's location, which fundamentally contradicts the core challenge and assumption of camouflaged object detection. This design choice may render the experimental results unconvincing.", "limitations": "While I understand the authors propose prototypes to avoid occlusion and missing issues, I'm concerned whether the model might \"overfit\" to specific prototypes, thereby segmenting only parts of the target in subsequent frames rather than the complete object.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strength:\n1) The proposed architecture is well-illustrated with clear figures that effectively communicate the method's design.\n2) The authors furnish a comprehensive comparison with state-of-the-art models, offering an intuitive overview.\n\nWeakness:\n\nAlthough this paper achieves good performance, it is based on box and 1-clip prompts, which may contradict the inherent nature of COD. Please refer to the questions below for more details.", "was_revised": true, "final_justification": "The authors have addressed all my concerns.", "rebuttal": "We sincerely thank you for the detailed and constructive feedback. We greatly appreciate your recognition of the clarity of our architectural design and the comprehensive comparisons provided. Now we address each of your concerns below. \n\n---\n\n## W1: Usage of prompts on VCOS\nThank you for raising this important point. We would like to clarify that our work specifically addresses the task of VCOS, which has distinct goals from COD. COD typically focuses on detecting camouflaged objects in static images without any prior cues, whereas VCOS requires accurate and temporally consistent segmentation of camouflaged objects across entire video sequences. Although our method uses prompts, this design choice is aligned with the nature of SAM and SAM2 as promptable segmentation models. In fact, recent works such as COMPrompter[1], DSAM[2], and SAM-PM[3] also incorporate box or mask prompts to adapt SAM to the COD or VCOS task. Thus, using prompts is a common and well-accepted practice in recent work and does not contradict the objectives of the task.\n\nWhat's more, these two paradigms, COD for detection and VCOS for segmentation, can be effectively combined. For example, one could apply a COD model to the first frame of a video to generate a bounding box, which then serves as a box prompt to CamSAM2 for video segmentation. This integration not only makes CamSAM2 compatible with existing COD methods and fully automatic pipelines but also highlights the practical value and significance of our work in advancing VCOS. In real-world wildlife monitoring, search and rescue scenarios, a simple user interaction (e.g. clicking on the target object) can trigger CamSAM2 to segment and track it across time. \n\n\n\n---\n\n## Q1: Loss design clarification\nThank you for this insightful question. While the original SAM2 parameters are frozen during training, we still apply supervision to its output $\\mathbf{R}_i$ for the following reasons: Although SAM2's weights are fixed, the introduction of our learnable decamouflaged token changes the self-attention dynamics in the mask decoder. This token is concatenated with the original output tokens and jointly participates in self-attention and cross-attention layers (see Fig. 2(a) in the main paper). As a result, the outputs $\\mathbf{R}_i$ are no longer identical to the original SAM2 outputs. Supervising them ensures that SAM2's outputs remain aligned with the segmentation intent, rather than being perturbed by the new token interactions. As shown in the Supple. Materials Table 4, we report the mIoU scores evaluated with the mask predicted by the SAM2 mask token and the decamouflaged token, the results also demonstrate the effectiveness of our design.\n\n---\n\n## Q2: Usage of prompts on VCOS\nThanks for your question. Please refer to W1 in detail.\n\n---\n\n## Limitation: On prototype potential overfitting\nThank you for raising this insightful concern. Our OPG and EOF modules are carefully designed to mitigate this risk. First, prototypes are dynamically generated from the predicted camouflaged regions using FPS and 1-iter $k$-means. This ensures that the prototypes are abstracted from the entire object masks, and are continually updated across frames and adapt to object appearance changes. Second, the EOF module uses a cross-attention mechanism, where the current frame's features query the prototype memory. This structure naturally suppresses outdated or irrelevant prototypes: if a prototype is inconsistent or suboptimal (e.g., due to occlusion or missing), it receives lower attention and contributes minimally to the output. This mechanism provides robustness and avoids overfitting to specific regions. Our Fig. 3 in the main paper further demonstrates this, showing that the segmentation results remain stable across time and do not overfit to a specific local region.\n\n---\n\nReferences  \n[1] COMPrompter: Reconceptualized Segment Anything Model with Multiprompt Network for Camouflaged Object Detection  \n[2] Exploring Deeper! Segment Anything Model with Depth Perception for Camouflaged Object Detection  \n[3] SAM-PM: Enhancing Video Camouflaged Object Detection using Spatio-Temporal Attention"}, {"review_id": "u1Tf43kzIt", "reviewer": "Reviewer_L184", "summary": "This paper proposes a segmentation method based on SAM2 for camouflaged object segmentation in videos, named CamSAM2. Specifically, the authors introduce a decamouflaged token to provide the flexibility of feature adjustment for VCOS. To make full use of fine-grained and high-resolution features from the current frame and previous frames, the authors propose implicit object-aware fusion (IOF) and explicit object-aware fusion (EOF) modules, respectively. Extensive experimental results validate the effectiveness of the CamSAM2.", "questions": "1. Although the paper has already been compared with several existing methods, the baseline methods for the comparison mainly focus on specific covert video segmentation tasks. For some general video segmentation methods (such as Mask R-CNN, DeepLab, etc.), although they may perform poorly in the covert video segmentation task, comparing these methods can more comprehensively demonstrate the advantages of CamSAM2.\n2. From the ablation experiments, it can be seen that compared with the IOF and EOF modules, the addition of the Decam. Token and OPG modules in the model result in a significant performance improvement. However, the key roles of these components are not mentioned in the paper.", "limitations": "The authors should provide the computational complexity of various algorithms and inference speed to more comprehensively evaluate the method proposed in this paper.", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "1. Strengths:\n(1) The research motivation is clear, and the proposed method is easy to follow.\n(2) Based on the SAM2 framework, the authors designed the IOF and EOF modules to segment camouflaged objects in videos.\n\n2. Weaknesses:\n(1) The description of some key modules (such as IOF, EOF, and OPG) in the paper is relatively concise, and some details may not be clear enough. In the OPG module, although Farthest Point Sampling and k-means are mentioned, there is no detailed explanation on how to select the value of k and how to handle the prototype consistency between different frames.\n(2) Although CamSAM2 has achieved significant performance improvements, the paper does not mention the real-time capability of the model. For some practical applications (such as wildlife monitoring, search and rescue, etc.), real-time performance is an important consideration factor.\n(3) The third chapter is dedicated to the methods section, but 3.6 (training and inference strategies) and 3.7 (experimental setup) are related to the experiments.\n(4) For Table 1, it is suggested that the years of the comparative test models be listed.", "was_revised": true, "final_justification": "After carefully evaluating the author's rebuttal, I recommend Accept.", "rebuttal": "We sincerely thank you for the positive feedback and thoughtful suggestions. We are glad that you found our research motivation to be clear and our method design to be easy to follow. We also appreciate your recognition of the architectural clarity and the integration of the IOF and EOF modules based on the SAM2 framework. Your comments encourage us to further refine and clarify the technical aspects of our work, and we address your insightful questions and concerns below.\n\n---\n\n## W1: OPG details clarification\nThanks for your thoughtful questions. \n1. Regarding the number of prototypes ($k$), we have investigated its impact in Table 7 in the main paper. The results show that both fewer or higher numbers of prototypes will reduce the performance due to under-representation or redundancy, but are still effective in abstracting prototypes. \n2. Regarding the consistency of prototypes across time, these prototypes remain consistent in a semantic sense, as they are all abstracted from the previously predicted camouflaged regions. Since these regions represent the same object over time, the resulting prototypes serve as temporally coherent and semantically aligned for guiding the segmentation in subsequent frames. \n\nTo improve interpretability, we calculate cosine similarity maps between each prototype of previous frames and the feature map of the current frame. The visualizations reveal that different prototypes consistently activate on semantically meaningful object regions. For example, the prototype shows strong activations (high similarity) precisely on the camouflaged object, while suppressing the background. This suggests that the prototypes successfully abstract object-aware semantics and are consistent across time. We will present detailed visualizations and analysis in the final version of the paper to further support interpretability.\n\n---\n\n## W2: Real-time capability concern\nThanks for raising this practical concern. We profile the runtime overhead of CamSAM2 on a 106-frame video and report the average per-frame latency in Table 1. CamSAM2 adds only 6 ms latency per frame on top of SAM2, resulting in a total per-frame time of 89.7 ms. This demonstrates the model's practicality in real-time applications such as wildlife monitoring, search and rescue.\n\n\n| Component   | Avg Time (ms) | Relative Percentage |\n|:-------------|:---------------:|:---------------------:|\n| SAM2        | 83.7          | 100.0%              |\n| FPS     | 3.5           | 4.2%                |\n| k-means | 1.0           | 1.2%                |\n| OPG         | 4.6           | 5.5%                |\n| CamSAM2     | 89.7          | 107.2%              |\n\n**Table 1. Average runtime per frame and overhead breakdown of CamSAM2 compared to original SAM2.**\n\n---\n\n## W3: Structural issue with Section 3\nThanks for your suggestion. We agree that Sections 3.6 and 3.7 are more appropriately arranged under the Experiments section. We will move them into Section 4 in the final version to improve structural consistency.\n\n---\n\n## W4: Publication year in Table\nThanks for your advice. We will add the publication year in the final version.\n\n---\n\n## Q1: More VOS models comparisons\nThanks for your suggestion. We compare CamSAM2 with two recent and representative SOTA video object segmentation (VOS) models: Cutie [1] and XMem++ [2], both widely used in VOS tasks. For fairness, we evaluate all models using the ground truth of the first frame as the mask prompt on the MoCA-Mask. As shown in Table 2, CamSAM2 achieves 70.5 mIoU, surpassing all baselines with comparable model sizes. This demonstrates the strong performance of CamSAM2 over both VOS-specific models and its SAM2-based baseline.\n\n\n\n| Method      | Params (M) | mIoU |\n|:-------------|:---------------:|:---------------------:|\n| Cutie-small | 28.7       | 60.8 |\n| XMem++      | 62.2       | 61.4 |\n| SAM2        | 38.9       | 67.9 |\n| CamSAM2 | 39.4       | **70.5** |\n\n**Table 2. Comparison with VOS models.**\n\n---\n\n## Q2: Highlighting the roles of components\nWe appreciate your suggestion. In CamSAM2, the decamouflaged token and OPG module are the core components that enable our method to adapt SAM2 for the VCOS task. The decamouflaged token allows the model to learn camouflaged features for camouflaged objects and is used to generate the decamouflaged mask without modifying SAM2's architecture, while OPG abstracts object-aware prototypes from previous frames to guide segmentation across time. The IOF and EOF modules play supporting roles, further enhancing spatial and temporal feature representation. To make this distinction clearer, we will revise the final version to more explicitly highlight the relative importance and motivation behind each module.\n\n---\n\n## Limitation\nThanks for raising this important point. We agree that both computational complexity and inference speed are critical considerations. As mentioned in W2, inference speed has already been addressed. Besides, we profile the computational complexity in Table 3. CamSAM2 introduces an additional 14.4 GFLOPs, which is independent of different backbone architectures. Based on the Hiera-T backbone, the total FLOPs increase from 137.2 GFLOPs to 151.5 GFLOPs, which corresponds to approximately a 10% increase. This added cost results in a significant performance gain of 9.8 mIoU with point prompts.  \nMoreover, when using the Hiera-L backbone, which image encoder requires 810 GFLOPs, the same 14.4 GFLOPs overhead accounts for only a 1.7% increase in total FLOPs. Even with such a small relative increase, CamSAM2 still achieves a notable 7.5 mIoU gain. This highlights the efficiency of our design: the added modules contribute strong segmentation improvements at minimal computational cost.\n\n\n\n| Module          | FLOPs (G) |\n|:-------------|:---------------:|\n| Image Encoder   | 103.0     |\n| Memory Encoder  | 5.0       |\n| Memory Attention| 27.4      |\n| Mask Decoder    | 1.8       |\n| **SAM2**        | **137.2** |\n| CamSAM2 modules| 14.4      |\n| **CamSAM2**     | **151.5** |\n\n**Table 3. FLOPs comparison between SAM2 and CamSAM2.**\n\n---\n\nReferences  \n[1] Putting the object back into video object segmentation.  \n[2] Xmem++: Production-level video segmentation from few annotated frames."}]

Author rebuttal:

Meta-review: This paper proposes **CamSAM2**, an adaptation of SAM2 for camouflaged object segmentation in videos. 

The submission initially received **2 × Borderline Accept** and **2 × Borderline Reject** ratings. During the rebuttal period, the authors and reviewers engaged actively, with the authors providing additional analyses and ablations. After the discussion, **three reviewers raised their scores to Accept**, while **one reviewer lowered their score to Reject**, citing concerns around novelty and positioning.  

While the AC acknowledges these concerns, the overall framework and integration underlying CamSAM2 are considered **meritorious**. Given its strong performance gains and practical impact, the AC finds the paper to be an **impactful contribution** and therefore recommends **Accept**. Congratulations.  

The authors are encouraged to **revise the framing in the final version** by:  
1. Clearly positioning CamSAM2 with respect to prior work on camouflaged object segmentation.  
2. Incorporating the additional results and discussions provided during the rebuttal period to further strengthen the paper.
Decision: Accept (poster)