Camouflaged Object Detection (COD) aims to detect objects well hidden in the environment. The main challenges of COD come from the high degree of texture and color overlapping between the objects and their surroundings. Inspired by that humans tend to go closer to the object and magnify it to recognize ambiguous objects more clearly, we propose a novel three-stage architecture called Search-Amplify-Recognize and design a network SARNet to address the challenges. Specifically, In the Search part, we utilize an attention-based backbone to locate the object. In the Amplify part, to obtain rich searched features and fine segmentation, we design Object Area Amplification modules (OAA) to perform cross-level and adjacent-level feature fusion and amplifying operations on feature maps. Besides, the OAA can be regarded as a simple and effective plug-in module to integrate and amplify the feature maps. The main components of the Recognize part are the Figure-Ground Conversion modules (FGC). The FGC modules alternately pay attention to the foreground and background to precisely separate the highly similar foreground and background. Extensive experiments on benchmark datasets show that our model outperforms other SOTA methods not only on COD tasks but also in COD downstream tasks, such as polyp segmentation and video camouflaged object detection. Source codes will be available at https://github.com/Haozhe-Xing/SARNet.

To alleviate the need for large-scale pixel-wise annotations, domain adaptation for semantic segmentation trains segmentation models on synthetic data (source) with computer-generated annotations, which can be then generalized to segment realistic images (target). Recently, self-supervised learning (SSL) with a combination of image-to-image translation shows great effectiveness in adaptive segmentation. The most common practice is to perform SSL along with image translation to well align a single domain (source or target). However, in this single-domain paradigm, unavoidable visual inconsistency raised by image translation may affect subsequent learning. In addition, pseudo labels generated by a single segmentation model aligned in either the source or target domain may be not accurate enough for SSL. In this paper, based on the observation that domain adaptation frameworks performed in the source and target domain are almost complementary, we propose a novel adaptive dual path learning (ADPL) framework to alleviate visual inconsistency and promote pseudo-labeling by introducing two interactive single-domain adaptation paths aligned in source and target domain respectively. To fully explore the potential of this dual-path design, novel technologies such as dual path image translation (DPIT), dual path adaptive segmentation (DPAS), dual path pseudo label generation (DPPLG) and Adaptive ClassMix are proposed. The inference of ADPL is extremely simple, only one segmentation model in the target domain is employed. Our ADPL outperforms the state-of-the-art methods by large margins on GTA5 $\rightarrow$ Cityscapes, SYNTHIA $\rightarrow$ Cityscapes and GTA5 $\rightarrow$ BDD100K scenarios. Code and models are available at https://github.com/royee182/DPL .

Deep neural networks (DNNs) have been showed to be highly vulnerable to imperceptible adversarial perturbations. As a complementary type of adversary, patch attacks that introduce perceptible perturbations to the images have attracted the interest of researchers. Existing patch attacks rely on the architecture of the model or the probabilities of predictions and perform poorly in the decision-based setting, which can still construct a perturbation with the minimal information exposed – the top-1 predicted label. In this work, we first explore the decision-based patch attack. To enhance the attack efficiency, we model the patches using paired key-points and use targeted images as the initialization of patches, and parameter optimizations are all performed on the integer domain. Then, we propose a differential evolutionary algorithm named DevoPatch for query-efficient decision-based patch attacks. Experiments demonstrate that DevoPatch outperforms the state-of-the-art black-box patch attacks in terms of patch area and attack success rate within a given query budget on image classification and face verification. Additionally, we conduct the vulnerability evaluation of ViT and MLP on image classification in the decision-based patch attack setting for the first time. Using DevoPatch, we can evaluate the robustness of models to black-box patch attacks. We believe this method could inspire the design and deployment of robust vision models based on various DNN architectures in the future.

Video object segmentation (VOS) aims to distinguish and track target objects in a video. Despite the excellent performance achieved by off-the-shelf VOS models, part of the existing VOS benchmarks mainly focuses on short-term videos, where objects remain visible most of the time. However, these benchmarks may not fully capture challenges encountered in practical applications, and the absence of long-term datasets restricts further investigation of VOS in realistic scenarios. Thus, we propose a novel benchmark named LVOS, comprising 720 videos with 296,401 frames and 407,945 high-quality annotations. Videos in LVOS last 1.14 minutes on average. Each video includes various attributes, especially challenges encountered in the wild, such as long-term reappearing and cross-temporal similar objects. Compared to previous benchmarks, our LVOS better reflects VOS models’ performance in real scenarios. Based on LVOS, we evaluate 15 existing VOS models under 3 different settings and conduct a comprehensive analysis. On LVOS, these models suffer a large performance drop, highlighting the challenge of achieving precise tracking and segmentation in real-world scenarios. Attribute-based analysis indicates that one of the significant factors contributing to accuracy decline is the increased video length, interacting with complex challenges such as long-term reappearance, cross-temporal confusion, and occlusion, which emphasize LVOS’s crucial role. We hope our LVOS can advance development of VOS in real scenes.