Unconstrained Salient and Camouflaged Object Detection

Visual Salient Object Detection (SOD) and Camouflaged Object Detection (COD) are two interrelated yet distinct tasks. Both tasks model the human visual system's ability to perceive the presence of objects. The traditional SOD datasets and methods are designed for scenes where only salient objects are present, similarly, COD datasets and methods are designed for scenes where only camouflaged objects are present. However, scenes where both salient and camouflaged objects coexist, or where neither is present, are not considered. This simplifies the existing research on SOD and COD. In this paper, to explore a more generalized approach to SOD and COD, we introduce a benchmark called Unconstrained Salient and Camouflaged Object Detection (USCOD), which supports the simultaneous detection of salient and camouflaged objects in unconstrained scenes, regardless of their presence. Towards this, we construct a large-scale dataset, CS12K, that encompasses a variety of scenes, including four distinct types: only salient objects, only camouflaged objects, both, and neither. In our benchmark experiments, we identify a major challenge in USCOD: distinguishing between salient and camouflaged objects within the same scene. To address this challenge, we propose USCNet, a baseline model for USCOD that decouples the learning of attribute distinction from mask reconstruction. The model incorporates an APG module, which learns both sample-generic and sample-specific features to enhance the attribute differentiation between salient and camouflaged objects. Furthermore, to evaluate models' ability to distinguish between salient and camouflaged objects, we design a metric called Camouflage-Saliency Confusion Score (CSCS). The proposed method achieves state-of-the-art performance on the newly introduced USCOD task. The code and dataset will be publicly available.

Evaluation Study on SAM 2 for Class-agnostic Instance-level Segmentation

Segment Anything Model (SAM) has demonstrated powerful zero-shot segmentation performance in natural scenes. The recently released Segment Anything Model 2 (SAM2) has further heightened researchers' expectations towards image segmentation capabilities. To evaluate the performance of SAM2 on class-agnostic instance-level segmentation tasks, we adopt different prompt strategies for SAM2 to cope with instance-level tasks for three relevant scenarios: Salient Instance Segmentation (SIS), Camouflaged Instance Segmentation (CIS), and Shadow Instance Detection (SID). In addition, to further explore the effectiveness of SAM2 in segmenting granular object structures, we also conduct detailed tests on the high-resolution Dichotomous Image Segmentation (DIS) benchmark to assess the fine-grained segmentation capability. Qualitative and quantitative experimental results indicate that the performance of SAM2 varies significantly across different scenarios. Besides, SAM2 is not particularly sensitive to segmenting high-resolution fine details. We hope this technique report can drive the emergence of SAM2-based adapters, aiming to enhance the performance ceiling of large vision models on class-agnostic instance segmentation tasks.

Synthetic Data in AI: Challenges, Applications, and Ethical Implications

In the rapidly evolving field of artificial intelligence, the creation and utilization of synthetic datasets have become increasingly significant. This report delves into the multifaceted aspects of synthetic data, particularly emphasizing the challenges and potential biases these datasets may harbor. It explores the methodologies behind synthetic data generation, spanning traditional statistical models to advanced deep learning techniques, and examines their applications across diverse domains. The report also critically addresses the ethical considerations and legal implications associated with synthetic datasets, highlighting the urgent need for mechanisms to ensure fairness, mitigate biases, and uphold ethical standards in AI development.

Unite-Divide-Unite: Joint Boosting Trunk and Structure for High-accuracy Dichotomous Image Segmentation

High-accuracy Dichotomous Image Segmentation (DIS) aims to pinpoint category-agnostic foreground objects from natural scenes. The main challenge for DIS involves identifying the highly accurate dominant area while rendering detailed object structure. However, directly using a general encoder-decoder architecture may result in an oversupply of high-level features and neglect the shallow spatial information necessary for partitioning meticulous structures. To fill this gap, we introduce a novel Unite-Divide-Unite Network (UDUN} that restructures and bipartitely arranges complementary features to simultaneously boost the effectiveness of trunk and structure identification. The proposed UDUN proceeds from several strengths. First, a dual-size input feeds into the shared backbone to produce more holistic and detailed features while keeping the model lightweight. Second, a simple Divide-and-Conquer Module (DCM) is proposed to decouple multiscale low- and high-level features into our structure decoder and trunk decoder to obtain structure and trunk information respectively. Moreover, we design a Trunk-Structure Aggregation module (TSA) in our union decoder that performs cascade integration for uniform high-accuracy segmentation. As a result, UDUN performs favorably against state-of-the-art competitors in all six evaluation metrics on overall DIS-TE, i.e., achieving 0.772 weighted F-measure and 977 HCE. Using 1024*1024 input, our model enables real-time inference at 65.3 fps with ResNet-18.