It is challenging to accurately detect camouflaged objects from their highly similar surroundings. Existing methods mainly leverage a single-stage detection fashion, while neglecting small objects with low-resolution fine edges requires more operations than the larger ones. To tackle camouflaged object detection (COD), we are inspired by humans attention coupled with the coarse-to-fine detection strategy, and thereby propose an iterative refinement framework, coined SegMaR, which integrates Segment, Magnify and Reiterate in a multi-stage detection fashion. Specifically, we design a new discriminative mask which makes the model attend on the fixation and edge regions. In addition, we leverage an attention-based sampler to magnify the object region progressively with no need of enlarging the image size. Extensive experiments show our SegMaR achieves remarkable and consistent improvements over other state-of-the-art methods. Especially, we surpass two competitive methods 7.4% and 20.0% respectively in average over standard evaluation metrics on small camouflaged objects. Additional studies provide more promising insights into Seg-MaR, including its effectiveness on the discriminative mask and its generalization to other network architectures. Code is available at https://github.com/dlut-dimt/SegMaR.

Segment, Magnify and Reiterate: Detecting Camouflaged Objects the Hard Way

We propose a new video camouflaged object detection (VCOD) framework that can exploit both short-term dynamics and long-term temporal consistency to detect camouflaged objects from video frames. An essential property of camouflaged objects is that they usually exhibit patterns similar to the background and thus make them hard to identify from still images. Therefore, effectively handling temporal dynamics in videos becomes the key for the VCOD task as the camouflaged objects will be noticeable when they move. However, current VCOD methods often leverage homography or optical flows to represent motions, where the detection error may accumulate from both the motion estimation error and the segmentation error. On the other hand, our method unifies motion estimation and object segmentation within a single optimization framework. Specifically, we build a dense correlation volume to implicitly capture motions between neighbouring frames and utilize the final segmentation supervision to optimize the implicit motion estimation and segmentation jointly. Furthermore, to enforce temporal consistency within a video sequence, we jointly utilize a spatio-temporal transformer to refine the short-term predictions. Extensive experiments on VCOD benchmarks demonstrate the architectural effectiveness of our approach. We also provide a large-scale VCOD dataset named MoCA-Mask with pixel-level handcrafted ground-truth masks and construct a comprehensive VCOD bench-mark with previous methods to facilitate research in this direction. Dataset Link: https://xueliancheng.github.io/SLT-Net-project.

Implicit Motion Handling for Video Camouflaged Object Detection

Camouflaged object detection (COD) aims to identify the objects that conceal themselves in natural scenes. Accurate COD suffers from a number of challenges associated with low boundary contrast and the large variation of object appearances, e.g., object size and shape. To address these challenges, we propose a novel Context-aware Cross-level Fusion Network (<inline-formula> <tex-math notation="LaTeX">$\text{C}^{2}\text{F}$ </tex-math></inline-formula>-Net), which fuses context-aware cross-level features for accurately identifying camouflaged objects. Specifically, we compute informative attention coefficients from multi-level features with our Attention-induced Cross-level Fusion Module (ACFM), which further integrates the features under the guidance of attention coefficients. We then propose a Dual-branch Global Context Module (DGCM) to refine the fused features for informative feature representations by exploiting rich global context information. Multiple ACFMs and DGCMs are integrated in a cascaded manner for generating a coarse prediction from high-level features. The coarse prediction acts as an attention map to refine the low-level features before passing them to our Camouflage Inference Module (CIM) to generate the final prediction. We perform extensive experiments on three widely used benchmark datasets and compare <inline-formula> <tex-math notation="LaTeX">$\text{C}^{2}\text{F}$ </tex-math></inline-formula>-Net with state-of-the-art (SOTA) models. The results show that <inline-formula> <tex-math notation="LaTeX">$\text{C}^{2}\text{F}$ </tex-math></inline-formula>-Net is an effective COD model and outperforms SOTA models remarkably. Further, an evaluation on polyp segmentation datasets demonstrates the promising potentials of our <inline-formula> <tex-math notation="LaTeX">$\text{C}^{2}\text{F}$ </tex-math></inline-formula>-Net in COD downstream applications. Our code is publicly available at: <uri>https://github.com/Ben57882/C2FNet-TSCVT</uri>

Camouflaged Object Detection via Context-Aware Cross-Level Fusion

Camouflaged object detection (COD) aims to detect/segment camouflaged objects embedded in the environment, which has attracted increasing attention over the past decades. Although several COD methods have been developed, they still suffer from unsatisfactory performance due to the intrinsic similarities between the foreground objects and background surroundings. In this paper, we propose a novel Feature Aggregation and Propagation Network (FAP-Net) for camouflaged object detection. Specifically, we propose a Boundary Guidance Module (BGM) to explicitly model the boundary characteristic, which can provide boundary-enhanced features to boost the COD performance. To capture the scale variations of the camouflaged objects, we propose a Multi-scale Feature Aggregation Module (MFAM) to characterize the multi-scale information from each layer and obtain the aggregated feature representations. Furthermore, we propose a Cross-level Fusion and Propagation Module (CFPM). In the CFPM, the feature fusion part can effectively integrate the features from adjacent layers to exploit the cross-level correlations, and the feature propagation part can transmit valuable context information from the encoder to the decoder network via a gate unit. Finally, we formulate a unified and end-to-end trainable framework where cross-level features can be effectively fused and propagated for capturing rich context information. Extensive experiments on three benchmark camouflaged datasets demonstrate that our FAP-Net outperforms other state-of-the-art COD models. Moreover, our model can be extended to the polyp segmentation task, and the comparison results further validate the effectiveness of the proposed model in segmenting polyps. The source code and results will be released at https://github.com/taozh2017/FAPNet.

Feature Aggregation and Propagation Network for Camouflaged Object Detection

Aleatoric uncertainty captures noise within the observations. For camouflaged object detection, due to similar appearance of the camouflaged foreground and the back-ground, it’s difficult to obtain highly accurate annotations, especially annotations around object boundaries. We argue that training directly with the "noisy" camouflage map may lead to a model of poor generalization ability. In this paper, we introduce an explicitly aleatoric uncertainty estimation technique to represent predictive uncertainty due to noisy labeling. Specifically, we present a confidence-aware camouflaged object detection (COD) framework using dynamic supervision to produce both an accurate camouflage map and a reliable "aleatoric uncertainty". Different from existing techniques that produce deterministic prediction following the point estimation pipeline, our framework formalises aleatoric uncertainty as probability distribution over model output and the input image. We claim that, once trained, our confidence estimation network can evaluate the pixel-wise accuracy of the prediction without relying on the ground truth camouflage map. Extensive results illustrate the superior performance of the proposed model in explaining the camouflage prediction. Our codes are available at https://github.com/Carlisle-Liu/OCENet

Modeling Aleatoric Uncertainty for Camouflaged Object Detection

The objective of this paper is to design a computational architecture that discovers camouflaged objects in videos, specifically by exploiting motion information to perform object segmentation. We make the following three contributions: (i) We propose a novel architecture that consists of two essential components for breaking camouflage, namely, a differentiable registration module to align consecutive frames based on the background, which effectively emphasises the object boundary in the difference image, and a motion segmentation module with memory that discovers the moving objects, while maintaining the object permanence even when motion is absent at some point. (ii) We collect the first large-scale Moving Camouflaged Animals (MoCA) video dataset, which consists of over 140 clips across a diverse range of animals (67 categories). (iii) We demonstrate the effectiveness of the proposed model on MoCA, and achieve competitive performance on the unsupervised segmentation protocol on DAVIS2016 by only relying on motion.

/pdf/betrayed-by-motion-camouflaged-object-discovery-via-motion-3j06xx67zx.pdf

Betrayed by Motion: Camouflaged Object Discovery via Motion Segmentation

Animals have evolved highly functional visual systems to
understand motion, assisting perception even under complex environments. In this paper, we work towards developing a computer vision system able to segment objects by exploiting motion cues, i.e. motion segmentation. We make the
following contributions: First, we introduce a simple variant of the Transformer to segment optical flow frames into
primary objects and the background. Second, we train the
architecture in a self-supervised manner, i.e. without using
any manual annotations. Third, we analyze several critical
components of our method and conduct thorough ablation
studies to validate their necessity. Fourth, we evaluate the
proposed architecture on public benchmarks (DAVIS2016,
SegTrackv2, and FBMS59). Despite using only optical flow
as input, our approach achieves superior or comparable results to previous state-of-the-art self-supervised methods,
while being an order of magnitude faster. We additionally evaluate on a challenging camouflage dataset (MoCA),
significantly outperforming the other self-supervised approaches, and comparing favourably to the top supervised
approach, highlighting the importance of motion cues, and
the potential bias towards visual appearance in existing
video segmentation models.

/pdf/self-supervised-video-object-segmentation-by-motion-grouping-5fl5baei4s.pdf

Self-Supervised Video Object Segmentation by Motion Grouping

The solar wind consists of charged particles ejected from the Sun into interplanetary space and towards Earth. Understanding the magnetic field of the solar wind is crucial for predicting future space weather and planetary atmospheric loss. Compared to large-scale magnetic events, smaller-scale structures like magnetic discontinuities are hard to detect but entail important information on the evolution of the solar wind. A lack of labeled data makes an automated detection of these discontinuities challenging. We propose Deep-SWIM, an approach leveraging advances in contrastive learning, pseudo-labeling and online hard example mining to robustly identify discontinuities in solar wind magnetic field data. Through a systematic ablation study, we show that we can accurately classify discontinuities despite learning from only limited labeled data. Additionally, we show that our approach generalizes well and produces results that agree with expert hand-labeling. Fourth Workshop on Machine Learning and the Physical Sciences (NeurIPS 2021).

Deep-SWIM: A few-shot learning approach to classify Solar WInd Magnetic field structures

Animals have evolved highly functional visual systems to understand motion, assisting perception even under complex environments. In this paper, we work towards developing a computer vision system able to segment objects by exploiting motion cues, i.e. motion segmentation. We make the following contributions: First, we introduce a simple variant of the Transformer to segment optical flow frames into primary objects and the background. Second, we train the architecture in a self-supervised manner, i.e. without using any manual annotations. Third, we analyze several critical components of our method and conduct thorough ablation studies to validate their necessity. Fourth, we evaluate the proposed architecture on public benchmarks (DAVIS2016, SegTrackv2, and FBMS59). Despite using only optical flow as input, our approach achieves superior or comparable results to previous state-of-the-art self-supervised methods, while being an order of magnitude faster. We additionally evaluate on a challenging camouflage dataset (MoCA), significantly outperforming the other self-supervised approaches, and comparing favourably to the top supervised approach, highlighting the importance of motion cues, and the potential bias towards visual appearance in existing video segmentation models.

Hala Lamdouar

Papers

Betrayed by Motion: Camouflaged Object Discovery via Motion Segmentation

Self-Supervised Video Object Segmentation by Motion Grouping

Deep-SWIM: A few-shot learning approach to classify Solar WInd Magnetic field structures

Self-supervised Video Object Segmentation by Motion Grouping

Betrayed by Motion: Camouflaged Object Discovery via Motion Segmentation