Just a Hint: Point-Supervised Camouflaged Object Detection

The Point-Supervised Camouflaged Object Detection (COD) method addresses the challenge of identifying objects that blend seamlessly into their surroundings, a task complicated by the subtle differences and ambiguous boundaries of camouflaged objects. To reduce annotation burden, the proposed method uses only one point supervision, which is adaptively expanded to a hint area. An attention regulator is introduced to scatter model attention across the entire object, avoiding partial localization around discriminative parts. Unsupervised contrastive learning is performed on differently augmented image pairs to overcome unstable feature representation issues under point-based annotation. This method outperforms several weakly-supervised approaches on three mainstream COD benchmarks, demonstrating its effectiveness across various metrics. The paper introduces a novel point-based learning paradigm for camouflaged object detection tasks, called Point-Supervised Camouflaged Object Detection (PSCOD). It proposes a weakly-supervised COD dataset, P-COD, with point annotations, containing 3040 images from COD10K and 1000 from CAMO. The method involves three key components: hint area generator, attention regulator, and representation optimizer. The hint area generator creates a hypothetical area from a single point within the camouflaged object, enhancing supervision. The attention regulator focuses the model's attention on the hint area, while the representation optimizer learns stable feature representations under point supervision. Unsupervised Contrastive Learning (UCL) is applied to improve feature representations. The method outperforms existing weakly-supervised COD methods and surpasses some fully-supervised methods. When adapted for scribble-supervised COD and semantic object detection (SOD) tasks, it achieves competitive results. The text discusses the exploration of weakly supervised camouflage object detection, focusing on using point labels instead of scribble labels for training. Point annotation has been studied in weakly-supervised segmentation and instance segmentation tasks. However, these methods are not suitable for camouflage object detection (COD) due to the lack of saliency in camouflaged objects. The text highlights the use of contrastive learning, a technique that attracts positive sample pairs and repels negative sample pairs, to improve model representation. The core of contrastive learning has shifted to using only positive samples, achieving state-of-the-art results without negative samples. The proposed framework introduces unsupervised contrastive learning to point-supervised COD, aiming to strengthen the model's representation. The paper introduces a method called Point-Supervised Camouflaged Object Detection, which comprises three main components: a hint area generator, an attention regulator, and a representation optimizer. The hint area generator expands a single point label into a small hint region to avoid model collapse and ensure the model focuses on the object as a whole. The attention regulator prevents the model from being stuck in local discriminative parts, enhancing its ability to detect camouflaged objects. The representation optimizer uses unsupervised contrastive learning to learn a stable feature representation, enabling the disentanglement of camouflaged objects from the background. This method aims to improve object detection in challenging scenarios, such as when objects blend into their surroundings. The text describes a method for enhancing object detection in camouflaged scenes using a pseudo area size estimation and an attention regulator module. The pseudo area size is calculated using an encoder trained on images, providing a rough hint for the object area. The hint area is represented as a circular region, and its radius is determined based on the number of objects in the scene. A hyperparameter, α, adjusts the radius to ensure it stays within the object boundaries. The original point supervision is expanded into several small regions around the object, minimizing incorrect supervision. The attention regulator module addresses partial detection issues by guiding the model to distribute attention across the entire object area, rather than focusing on discriminative regions. This is achieved by generating a mask that inhibits the model's response on the directly supervised discriminative part and encourages attention to the whole object area. The paper introduces a method called Point-Supervised Camouflaged Object Detection (COD) for identifying camouflaged objects in images. The process starts by creating a logical matrix, Z, with zeros and ones that matches the image's shape. This matrix is used to mask the annotated discriminative area of the image, I, through element-wise multiplication. During training, the model must recognize the surrounding foreground areas to restore the masked area, promoting attention to other regions within the object. To address the similarity between foreground and background, the Representation Optimizer is proposed. This strategy uses unsupervised contrastive learning to optimize the feature space, aiming to make the foreground object distinct from the background. Two visual transformations, T1 and T2, are applied to the image, resulting in transformed images I1 and I2. These images are encoded into prediction maps P1 and P2, which are then compared to minimize the distance between them. This process narrows the prediction gap, making the learned feature representation robust. The network's encoder structure is simpler than previous works, focusing on capturing long-distance feature dependencies. The text presents a quantitative comparison of various methods for object detection, focusing on four benchmarks: CAMO, COD10K, NC4K, and MAE. The methods are categorized based on supervision type: fully-supervised (F), unsupervised (U), scribble-supervised (S), and point-supervised (P). The best results are highlighted in bold. The table lists the methods, their supervision type, and their performance metrics, including Mean Absolute Error (MAE), S-measure (Sm), Efficiency (Em), and Feature Weight (Fw) with β values. F3Net, CSNet, ITSD, MINet, PraNet, UCNet, SINet, MGL-R, PFNet, UJSC, UGTR, ZoomNet, DUSD, USPS, SAM, SS, SCSOD, and CRNet are some of the methods discussed. The text also mentions the use of PVT as the backbone for the proposed method, which processes input images to generate multi-scale features. The features are then downsampled, unified, and combined through concatenation. The output map is obtained using a 3×3 convolution layer. The method employs two losses: contrastive loss and partial cross-entropy loss, which are used for training and are generalizable to other weakly-supervised object detection models. The text describes experiments on point-supervised camouflaged object detection, focusing on three benchmarks: CAMO, COD10K, and NC4K. A new dataset, Point-supervised Dataset (P-COD), was created for training by relabeling 4040 images. The dataset simulates the hunting process, allowing for easy and natural annotation of only one point per camouflaged object without ambiguous boundaries. Four evaluation metrics were used: mean absolute error (MAE), S-measure (Sm), E-measure (Em), and weighted F-measure (Fwβ). The method was implemented using PyTorch on a GeForce RTX4090 GPU, with a stochastic gradient descent optimizer, a momentum of 0.9, a weight decay of 5e-4, and a triangle learning rate schedule. The batch size was 8, and the training epoch was 60, taking approximately 7 hours to complete. The text compares the proposed method, Ours, with state-of-the-art COD methods, focusing on parameters, MACs, and performance metrics. Ours, a point-supervised COD approach, outperforms the fully-supervised SINet and the scribble-supervised CRNet across multiple datasets. It achieves lower MAE, higher Sm, Em, and Fw, indicating better accuracy and precision. The method's parameter complexity is lower, with fewer MACs, making it computationally efficient. An ablation study on contrastive losses further validates the effectiveness of the proposed approach. Qualitatively, Ours produces clearer, more complete object regions and sharper contours, outperforming CRNet in various challenging scenarios. The text discusses a study on point-supervised camouflaged object detection, focusing on the effectiveness of various components and augmentations. The study uses the CAMO dataset, known for its challenging nature. The ablation study examines the impact of different elements, such as the hint area generator, representation optimizer, and attention regulator, on the model's performance. The hint area generator shows significant improvement over direct point usage. Augmentations like scale, crop, flip, translate, and attention regulator are found to enhance model performance, with the attention regulator being particularly effective. The study also highlights the model's lightweight performance, emphasizing its efficiency. The ablation experiments on point supervision demonstrate the model's ability to outperform center and random annotations, attributing this to the utilization of priors in point annotations that simulate human cognitive processes. The results indicate that increasing the number of points beyond one yields diminishing returns. The text discusses the effectiveness of different settings in an attention regulator and key components in an optimizer for weakly-supervised object detection. The attention regulator, when applied with UCL, significantly improves results, as shown in Table 7. Comparisons with previous mask approaches, HaS and Cutout, in Table 8, further demonstrate the superiority of the regulator in weakly-supervised object detection. The representation optimizer, tested with various contrastive losses and data augmentation techniques, shows that L1 loss and color-texture, size augmentation improve performance, as detailed in Table 9. Table 10 presents transferability studies on scribble datasets, with the proposed method outperforming others. The text also highlights the importance of supervision quality over quantity, as the init square area outperforms prediction maps in WSCOD. Fig. 6, 7, and 8 visually demonstrate the effectiveness of the attention regulator and representation