Title: CamSAM2: Segment Anything Accurately in Camouflaged Videos
Venue: NeurIPS 2025
Accepted: yes
Keywords: SAM2

Video camouflaged object segmentation
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

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 Cam ouflaged 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 highquality 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.

1 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-rescue 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 2 (SAM2) [15], marks a significant advancement in video segmentation. SAM2 has learned rich and generalizable representations for natural scenes on the SA-1B [16] (11M images, 1B masks) and SA-V [15] (50.9K videos, 35.5M masks) datasets. Therefore, its 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, or mask prompts on the first frame of each video. Specifically, we randomly

Image

encoder

Memory

attention

IOF

Prompt encoder

Mask

decoder

Memory module

EOF

OPG

Prompt

Decam. token

Image

encoder

Memory

attention

Prompt encoder

Mask

decoder

Memory module

Prompt

M

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 a 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 a 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 a memory module containing a memory encoder and a memory bank. However, only low-resolution 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 Cam ouflaged 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-grained features of the frame, we propose the Implicit 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 on MoCA-Mask for

Hiera-T/Hiera-S backbones, and 19.6 mDice gains with mask prompt on SUN-SEG-Hard for Hiera-T backbone. When directly evaluating CamSAM2 on CAD without further finetuning, we observe strong zero-shot ability. Since all SAM2's weights remain unchanged, CamSAM2 totally inherits SAM2's capability on segmenting natural videos. In 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 in 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-grained 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-art 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-Mask-Pseudo [4], which laid the foundation for CSU. Traditional COD methods extract foreground-background 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-based models like CamoFormer [9] leverage multi-scale feature extraction with masked separable attention, and WSSCOD [5] employs a frequency transformer and noisy pseudo 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-temporal attention, and advanced segmentation techniques to maintain object coherence across frames. Motion-guided models enhance segmentation by leveraging motion cues. IMEX [30] integrates 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 a 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. Static-Dynamic-Interpretability [12] quantifies static and dynamic 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.

2.3 Segment Anything Model 2

SAM2[15] is a vision foundation model for promptable segmentation across images and videos. Compared to SAM [16], which is limited to image segmentation, SAM2 offers a 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 fine-tuning, or integrating SAM2 with multimodal large language models. However, they did not address the fundamental architectural challenges of adapting SAM2 to VCOS,

Image

Enc.

Prompt Enc.

prompt

Memory

Enc.

Memory

Attn.

Memory

Bank

Mask Dec.

Implicit Object-aware Fusion

Explicit Object-aware Fusion

Object Prototype Generation

Conv.

Conv.

Conv.

IOF

Ft

iof

❄

❄

❄

❄

❄

❄

MLP

Attn.

MLP

2x

Trans.

Conv.

T2I

Attn.

T2I Attn.

I2T Attn.

x2

Ft

mem

Mask Dec.

Decam. token (

T '

)

IoU token

Occlusion token

Output token per mask

Ft

mask

Rt

Output tokens

Prompt tokens

Decam. token (

T

)

❄

❄

❄

❄

❄

❄

❄

❄

Ft

eof'

©

Conv.

Conv.

EOF

Cross Attn.

Ft

iof

Zt

Ft

eof

Ft

mask

Ft

attn

�t

MLP

T '

Rt

c

OPG

FPS

Clustering

Pt

Ft

eof

Rt

c

❄

Addition

Product

© Concat

Training

Frozen

Memory retrieval

(b)

(c)

(d)

(a)

Ft

1

Ft

0

Ft

mem

leaving a significant performance gap compared to other VOS tasks, especially when using simple prompts.

3 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 to §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, as 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 a point or box prompt is given, the prompt encoder encodes it into sparse or dense embeddings, then segments the prompted frame; if a mask prompt is given, SAM2 directly uses the mask as the current frame output. Exploiting memory-conditioned features and prompt embeddings, the 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].

3.2 Decamouflaged token

Given a video clip containing m frames, we denote all frames as { I t -m +1 , · · · , I i , · · · , I t } with ground-truth segmentation masks of { S t -m +1 , · · · , S i , · · · , S t } . Especially, I t is the current frame for the purpose of easy explanation. We use the image encoder to extract features for all frames, denoted as { F t -m +1 , · · · , F i , · · · , F t } . Here, F i can be further represented as { F 0 i , · · · , F j i , · · · , F L -1 i } , containing feature maps extracted from L different intermediate layers, where F j i ∈ R c j × h j × w j , with c j , h j , and w j representing channels, height, and width, respectively.

SAM2's output tokens include an object score (occlusion) token, an IoU token, and mask tokens. To enhance SAM2's ability of segmenting camouflaged objects, we introduce a new learnable decamouflaged token T ∈ R 1 × 256 , 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 is denoted as T ′ . This token is updated through back-propagated gradients, while SAM2'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.

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 implicit 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, memory-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-conditioned features.

For SAM2, three feature maps from the Hiera image encoder [45] are extracted for each frame, i.e. , L = 3 . We have F 0 t , F 1 t , and F 2 t for the current frame I t . We denote the memory-conditioned feature as F mem t , encoded by the memory-attention module on F 2 t , as in [15]. The high-resolution features F 0 t and F 1 t are fused with F mem t via compression modules and point-wise addition to create a refined feature representation F iof t ∈ R c 0 × h 0 × w 0 , where a compression module C ( · ) consists of two convolutional layers, followed by an upsampling layer. This process is given by:

3.4 Explicit object-aware fusion

After obtaining F iof t , 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 F iof t , with shape R c 0 × h 0 × w 0 , is directly concatenated with SAM2's mask logits R t , which has shape R 1 × h 0 × w 0 . This concatenated feature is then processed through a convolutional layer to reduce the channels back to c 0 , resulting in the output with the original shape R c 0 × h 0 × w 0 , denoted as:

Second , F eof t 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 F eof t as a query, and leverages these prototypes as keys and values, effectively exploiting the information within the object prototypes to refine F eof t . This design 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 less to the output. This cross-attention mechanism provides robustness and avoids overfitting to specific regions. Formally, we update F eof t by conducting cross-attention with prototypes P t = { P 0 , P 1 , . . . , P t -1 } from previous frames, given by:

Third , the attention-refined feature F attn t is combined with the upscaled mask feature F mask t from SAM2 mask decoder. The upscaled mask feature is first processed through a convolutional layer and then fused with F attn t via point-wise addition, as follows:

Finally , we calculate the mask logits R c t of CamSAM2, by processing the output decamouflaged token T ′ through an MLP layer, then performing point-wise product with the F eof ′ t , as shown below:

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 k 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 k clusters by conducting one-iteration k -means, using the sampled k points as initial centers. The prototype of each cluster is represented as the mean of the spatial features of the points in the cluster. This prototype generation process is denoted as F p , as shown in:

where P t represents the camouflaged object prototypes extracted from high-resolution and detailed features for the frame I t . 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

Datasets. Our experiments are conducted on three video datasets: two popular camouflaged animal datasets, MoCA-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-Pseudo dataset, which contains pseudo masks for unlabeled frames with a bidirectional optical-flow-based consistency check strategy. The Camouflaged Animal Dataset (CAD) includes 9 short videos in total that have 181 hand-labeled masks on every five frames. SUN-SEG is the largest benchmark for video polyp segmentation, derived from SUN-database [48]. It consists of a training set with 112 clips (19,544 frames) and two test sets: SUN-SEG-Easy, containing 119 clips (17,070 frames), and SUN-SEG-Hard, comprising 54 clips (12,522 frames).

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 mask logits R i and the CamSAM2's mask logits R c i , compared with the ground-truth mask S i of frame I i , as follows:

Model

Backbone

Params (M)

Prompt

S

m

↑

F

ω

β

↑

MAE

↓

F

β

↑

E

m

↑

mDice

↑

mIoU

↑

EGNet

2019

[55]

ResNet-50

111.7

-

54.7

11.0

3.5

13.6

57.4

14.3

9.6

BASNet

2019

[56]

ResNet-50

87.1

-

56.1

15.4

4.2

17.3

59.8

19.0

13.7

CPD

2019

[57]

ResNet-50

47.9

-

56.1

12.1

4.1

15.2

61.3

16.2

11.3

PraNet

2020

[58]

ResNet-50

32.6

-

61.4

26.6

3.0

29.6

67.4

31.1

23.4

SINet

2020

[22]

ResNet-50

48.9

-

59.8

23.1

2.8

25.6

69.9

27.7

20.2

SINet-V2

2021

[26]

Res2Net-50

27.0

-

58.8

20.4

3.1

22.9

64.2

24.5

18.0

PNS-Net

2021

[59]

ResNet-50

142.9

-

52.6

5.9

3.5

8.4

53.0

8.4

5.4

RCRNet

2019

[60]

ResNet-50

53.8

-

55.5

13.8

3.3

15.9

52.7

17.1

11.6

MG

2021

[61]

VGG

4.8

-

53.0

16.8

6.7

19.5

56.1

18.1

12.7

SLT-Net-LT

2022

[4]

PVTv2-B5

82.3

-

63.1

31.1

2.7

33.1

75.9

36.0

27.2

ZoomNeXt

2024

[3]

PVTv2-B5

84.8

-

73.4

47.6

1.0

49.7

73.6

49.7

42.2

2024

[15]

Hiera-T

38.9

1-click

68.2

50.7

7.7

52.5

73.6

52.1

44.8

Hiera-T

38.9

1-click

70.2

54.5

7.2

56.0

76.8

55.2

48.1

Hiera-T

39.4

1-click

75.2

61.7

7.3

63.7

82.0

64.3

54.6

2024

[15]

Hiera-T

38.9

box

81.5

69.9

0.6

70.9

89.4

72.7

62.3

Hiera-T

38.9

box

82.1

71.6

0.5

72.8

90.8

73.6

63.4

Hiera-T

39.4

box

82.9

72.4

0.6

73.2

94.2

75.5

64.8

2024

[11]

ViT-L

303.0

mask

72.8

56.7

0.9

-

81.3

59.4

50.2

2024

[15]

Hiera-T

38.9

mask

84.7

76.0

0.4

76.9

91.9

77.1

67.9

Hiera-T

38.9

mask

84.7

76.2

0.4

77.1

91.9

77.2

68.0

Hiera-T

39.4

mask

86.2

78.7

0.4

79.6

96.2

80.2

70.5

where L is the final loss for our approach, summing the BCE loss L C and the dice loss L D .

During inference, we provide a prompt at the first frame of a video, following [15, 11]. Our final output is 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 is initialized with the parameters of 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 of 1e-3. We train CamSAM2 on 4 NVIDIA RTX 4090 GPUs for 10 epochs. For camouflaged animal segmentation, we train the model using the MoCA-Mask-Pseudo training set and evaluate it on the MoCA-Mask test set and CAD. During inference, we apply the 1-click, box, and mask prompts only on the first frame of each video. For camouflaged polyp segmentation, we train the model using the SUN-SEG training set and perform inference using the mask prompt on the first frame of each video on the SUN-SEG-Easy and SUN-SEG-Hard test sets.

Evaluation metrics. We adopt seven evaluation metrics to measure the quality of predicted pixelwise masks: S-measure ( S m ) [50], F-measure ( F β ) [51], weighted F-measure ( F ω β ) [52], mean absolute error (MAE) [53], E-measure ( E m ) [54], mean Dice (mDice), and mean IoU (mIoU).

4.2 Experimental results

Results on MoCA-Mask. Tab. 1 compares four promptable methods on MoCA-Mask. For a fair comparison, we fine-tune the mask decoder (4.2M parameters) of SAM2 on MoCA-Mask. CamSAM2

Frames

GT

clearly outperforms SAM-PM, SAM2, and the fine-tuned SAM2. Even with the 1-click prompt, CamSAM2 still outperforms SAM-PM, which uses the mask prompt. The promptable methods clearly outperform other non-promptable models. Tab. 2 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/mIoU gains of 12.2/9.8 with Hiera-T backbone and 13.1/12.1 with Hiera-S backbone, and further improves mIoU by 2.5/2.3 (box) and 2.6/1.0 (mask) over SAM2 on Hiera-T and Hiera-S backbones, respectively.

Fig. 3 presents qualitative comparisons on two MoCA-Mask video clips using 1-click prompts with Hiera-T as the backbone. Fig. 4 shows attention maps from the last token-to-image crossattention 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 to 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 introducing only a marginal parameter increase of 0.5M, it delivers substantial improvements while keeping all SAM2 parameters frozen, fully preserving SAM2's original ability to segment and track objects in natural scenes.

Results on CAD. We evaluate the zero-shot per-

flower crab spider

hedgehog

sand cat

Frame

GT

copperhead snake

formance of CamSAM2 and SAM2 on the CAD using Hiera-T backbone with point and box prompts, as shown in Tab. 3. CamSAM2 consistently outperforms SAM2, especially with the 1-click prompt,

achieving improvements of 3.4 mDice and 1.8 mIoU. With the box prompt, it also shows clear gains of 1.8 mDice and 2.5 mIoU. 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-Easy 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 to 80.6), demonstrating 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 MoCAMask 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 to its high performance. Starting from baseline (SAM2), adding the decamouflaged token alone improves mDice from 52.1 to 54.9 and mIoU from 44.8 to 47.0. Adding IOF further raises mDice to 55.2 and mIoU to 47.5. Using EOF brings mDice to 55.9 and mIoU to 47.9. With all components included, the model performs the best, achieving 64.3 on mDice and 54.6 on mIoU.

Effect of distance metric. We compare different distance metrics for k -means clustering in OPG, as 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 of number of prototypes k . We examine the impact of the number of prototypes k , as shown in Tab. 7. The results show that both fewer or higher numbers of prototypes will reduce the performance due to under-representation or redundancy , respectively. It is observed that k = 5 is 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 is 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, as well as three key modules: IOF for enhancing memory-conditioned features with implicitly object-aware high-resolution features, EOF for refining features with explicit object details, and OPG for abstracting high-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 zero-shot capability. By setting new state-of-the-art performance, CamSAM2 offers a more practical and effective solution for real-world VCOS applications.

Acknowledgments

This project was 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 Technology).

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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 × 256 since the raw predicted logits are 1/4 of the original size. We train CamSAM2 with a batch size of 4. The predicted final mask is interpolated to 1024 × 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 backbones and prompt types

Tab. 8 compares the performance of SAM2 and CamSAM2 on 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 backbones such as Hiera-T and Hiera-S. For instance, with Hiera-T, CamSAM2 achieves a significant improvement of 12.2 mDice and 9.8 mIoU, while with Hiera-S, the gains are even larger at 13.1

mDice and 12.1 mIoU. 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 mIoU. With Hiera-L backbone, it 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 mIoU, while with Hiera-B+, the improvements are 3.2 mDice and 2.7 mIoU.

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 a 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 mIoU with the Hiera-T backbone. Similarly, with the Hiera-S backbone, it achieves gains of 6.8 mDice and 5.9 mIoU, 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 a 1.9 mDice gain with the Hiera-S backbone. These results showcase 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 an improvement of 2.2 mDice and 1.9 mIoU, 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 a fair and comprehensive comparison, we evaluate CamSAM2 alongside SAM2Adapter [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 are trained, while the rest of the image encoder is kept frozen. Since the official SAM2-Adapter 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, CamSAM2 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 a learned prompt injected into different stages in the image encoder at every image during inference. In contrast, CamSAM2 follows the original SAM2 design

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 over time. While our usage of ground-truth-derived prompts may raise fairness concerns, this is 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 a 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 a 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 i and our CamSAM2 mask logits R c i . 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 i are no longer guaranteed to be identical to those of the original SAM2. By supervising them, we ensure that SAM2'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 the final mask prediction is obtained by averaging SAM2's mask logits R t and the CamSAM2 mask logits R c t , which is calculated by the SAM2 mask token and the decamouflaged token. This strategy integrates SAM2's global representation with the fine-grained, camouflage-aware features extracted by our proposed

modules. Despite its simplicity, this approach effectively unifies global generalization and fine-grained object sensitivity.

Tab. 11 reports mIoU 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.

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 a self-attention layer and different cross-attention layers. Second, SAM2's architecture conditions the current frame's feature F mem t 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 a 1-click point prompt with Hiera-T as the backbone. This setting isolates the model's ability to handle camouflaged object segmentation, as 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.

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 of 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 over 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 a representative 106-frame camouflaged video from the MoCA-Mask test set. Tab. 13

reports the average runtime breakdown and compares CamSAM2 against the baseline SAM2. Overall, CamSAM2introduces only 6 ms (7.2%) additional latency per frame relative to 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 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. Moreover, 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.

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. Weconduct 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 1 to 3 results in a comparable performance in mIoU. This indicates that adding more tokens does not structurally improve the representation quality, but may introduce redundancy and additional computational cost.

Sampling and Clustering Strategies. To evaluate the robustness and generality of the OPG module, we experiment with different strategies for sampling and clustering. Specifically, we compare (i)

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 to more representative prototypes.

These findings show that OPG is 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

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To better illustrate the relationship between CamSAM2 and the original SAM2 pipeline, Fig. 5 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 all SAM2 parameters are 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 a 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

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.

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A.12 More qualitative result visualization

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Fig. 7 presents two qualitative example video clips of 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-Mask, comparing the segmentation performance of SAM2 and CamSAM2 using 1-click point prompts with the Hiera-T backbone. The results clearly demonstrate CamSAM2's advantages in segmenting camouflaged objects in videos. For instance, in the first ( stick insect ) and second ( rusty spotted cat ) examples from Fig. 8, SAM2 is able to segment parts of 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

correct regions of the object. These findings highlight CamSAM2's ability to both enhance accuracy in 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 in tackling diverse and complex tasks in VCOS.

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ONATURE

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Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\n1. 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.\n\nQUESTIONS:\n1. 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.\n\nLIMITATIONS:\nThe authors should provide the computational complexity of various algorithms and inference speed to more comprehensively evaluate the method proposed in this paper.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-22"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nIn 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrength:\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.\n\nQUESTIONS:\n1) 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.\n\nLIMITATIONS:\nWhile 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.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths: \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.\n\nQUESTIONS:\nSome 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?\n\nLIMITATIONS:\nLimitations are in supplementary document.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\n**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.\n\nQUESTIONS:\nPlease see **Weaknesses**.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 2, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-20"}