Unconstrained Salient and Camouflaged Object Detection

• URL: http://arxiv.org/abs/2412.10943v1
• Date: Sat, 14 Dec 2024 19:37:17 GMT
• Title: Unconstrained Salient and Camouflaged Object Detection
• Authors: Zhangjun Zhou, Yiping Li, Chunlin Zhong, Jianuo Huang, Jialun Pei, He Tang,
• Abstract summary: We introduce a benchmark called Unconstrained Salient and Camouflaged Object Detection (USCOD)<n>USCOD supports the simultaneous detection of salient and camouflaged objects in unconstrained scenes, regardless of their presence.<n>To address this challenge, we propose USCNet, a baseline model for USCOD that decouples the learning of attribute distinction from mask reconstruction.
• Score: 4.698538612738126
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Toward Realistic Camouflaged Object Detection: Benchmarks and Method [11.279532701331647]
  Camouflaged object detection (COD) primarily relies on semantic or instance segmentation methods. We propose a camouflage-aware feature refinement (CAFR) strategy to detect camouflaged objects. CAFR fully utilizes a clear perception of the current object within the prior knowledge of large models to assist detectors in deeply understanding the distinctions between background and foreground.
  arXiv  Detail & Related papers  (2025-01-13T13:04:00Z)
• CGCOD: Class-Guided Camouflaged Object Detection [19.959268087062217]
  We introduce class-guided camouflaged object detection (CGCOD), which extends traditional COD task by incorporating object-specific class knowledge. We propose a multi-stage framework, CGNet, which incorporates a plug-and-play class prompt generator and a simple yet effective class-guided detector. This establishes a new paradigm for COD, bridging the gap between contextual understanding and class-guided detection.
  arXiv  Detail & Related papers  (2024-12-25T19:38:32Z)
• Seamless Detection: Unifying Salient Object Detection and Camouflaged Object Detection [73.85890512959861]
  We propose a task-agnostic framework to unify Salient Object Detection (SOD) and Camouflaged Object Detection (COD) We design a simple yet effective contextual decoder involving the interval-layer and global context, which achieves an inference speed of 67 fps. Experiments on public SOD and COD datasets demonstrate the superiority of our proposed framework in both supervised and unsupervised settings.
  arXiv  Detail & Related papers  (2024-12-22T03:25:43Z)
• Object-Centric Multiple Object Tracking [124.30650395969126]
  This paper proposes a video object-centric model for multiple-object tracking pipelines. It consists of an index-merge module that adapts the object-centric slots into detection outputs and an object memory module. Benefited from object-centric learning, we only require sparse detection labels for object localization and feature binding.
  arXiv  Detail & Related papers  (2023-09-01T03:34:12Z)
• Camouflaged Image Synthesis Is All You Need to Boost Camouflaged Detection [65.8867003376637]
  We propose a framework for synthesizing camouflage data to enhance the detection of camouflaged objects in natural scenes. Our approach employs a generative model to produce realistic camouflage images, which can be used to train existing object detection models. Our framework outperforms the current state-of-the-art method on three datasets.
  arXiv  Detail & Related papers  (2023-08-13T06:55:05Z)
• Camouflaged Object Detection with Feature Grafting and Distractor Aware [9.791590363932519]
  We propose a novel Feature Grafting and Distractor Aware network (FDNet) to handle the Camouflaged Object Detection task. Specifically, we use CNN and Transformer to encode multi-scale images in parallel. A Distractor Aware Module is designed to explicitly model the two possible distractors in the COD task to refine the coarse camouflage map.
  arXiv  Detail & Related papers  (2023-07-08T09:37:08Z)
• Referring Camouflaged Object Detection [97.90911862979355]
  Ref-COD aims to segment specified camouflaged objects based on a small set of referring images with salient target objects. We first assemble a large-scale dataset, called R2C7K, which consists of 7K images covering 64 object categories in real-world scenarios.
  arXiv  Detail & Related papers  (2023-06-13T04:15:37Z)
• The Art of Camouflage: Few-Shot Learning for Animal Detection and Segmentation [21.047026366450197]
  We address the problem of few-shot learning for camouflaged object detection and segmentation. We propose FS-CDIS, a framework to efficiently detect and segment camouflaged instances. Our proposed method achieves state-of-the-art performance on the newly collected dataset.
  arXiv  Detail & Related papers  (2023-04-15T01:33:14Z)
• CamDiff: Camouflage Image Augmentation via Diffusion Model [83.35960536063857]
  CamDiff is a novel approach to synthesize salient objects in camouflaged scenes. We leverage the latent diffusion model to synthesize salient objects in camouflaged scenes. Our approach enables flexible editing and efficient large-scale dataset generation at a low cost.
  arXiv  Detail & Related papers  (2023-04-11T19:37:47Z)
• Unified Visual Relationship Detection with Vision and Language Models [89.77838890788638]
  This work focuses on training a single visual relationship detector predicting over the union of label spaces from multiple datasets. We propose UniVRD, a novel bottom-up method for Unified Visual Relationship Detection by leveraging vision and language models. Empirical results on both human-object interaction detection and scene-graph generation demonstrate the competitive performance of our model.
  arXiv  Detail & Related papers  (2023-03-16T00:06:28Z)
• CamoFormer: Masked Separable Attention for Camouflaged Object Detection [94.2870722866853]
  We present a simple masked separable attention (MSA) for camouflaged object detection. We first separate the multi-head self-attention into three parts, which are responsible for distinguishing the camouflaged objects from the background using different mask strategies. We propose to capture high-resolution semantic representations progressively based on a simple top-down decoder with the proposed MSA to attain precise segmentation results.
  arXiv  Detail & Related papers  (2022-12-10T10:03:27Z)
• MFFN: Multi-view Feature Fusion Network for Camouflaged Object Detection [10.04773536815808]
  We propose a behavior-inspired framework, called Multi-view Feature Fusion Network (MFFN), which mimics the human behaviors of finding indistinct objects in images. MFFN captures critical edge and semantic information by comparing and fusing extracted multi-view features. Our method performs favorably against existing state-of-the-art methods via training with the same data.
  arXiv  Detail & Related papers  (2022-10-12T16:12:58Z)
• Towards Deeper Understanding of Camouflaged Object Detection [64.81987999832032]
  We argue that the binary segmentation setting fails to fully understand the concept of camouflage. We present the first triple-task learning framework to simultaneously localize, segment and rank camouflaged objects.
  arXiv  Detail & Related papers  (2022-05-23T14:26:18Z)
• Exploiting Multi-Object Relationships for Detecting Adversarial Attacks in Complex Scenes [51.65308857232767]
  Vision systems that deploy Deep Neural Networks (DNNs) are known to be vulnerable to adversarial examples. Recent research has shown that checking the intrinsic consistencies in the input data is a promising way to detect adversarial attacks. We develop a novel approach to perform context consistency checks using language models.
  arXiv  Detail & Related papers  (2021-08-19T00:52:10Z)
• Simultaneously Localize, Segment and Rank the Camouflaged Objects [55.46101599577343]
  Camouflaged object detection aims to segment camouflaged objects hiding in their surroundings. We argue that explicitly modeling the conspicuousness of camouflaged objects against their particular backgrounds can lead to a better understanding about camouflage and evolution of animals. We present the first ranking based COD network (Rank-Net) to simultaneously localize, segment and rank camouflaged objects.
  arXiv  Detail & Related papers  (2021-03-06T02:53:36Z)
• Concealed Object Detection [140.98738087261887]
  We present the first systematic study on concealed object detection (COD) COD aims to identify objects that are "perfectly" embedded in their background. To better understand this task, we collect a large-scale dataset called COD10K.
  arXiv  Detail & Related papers  (2021-02-20T06:49:53Z)
• Synthesizing the Unseen for Zero-shot Object Detection [72.38031440014463]
  We propose to synthesize visual features for unseen classes, so that the model learns both seen and unseen objects in the visual domain. We use a novel generative model that uses class-semantics to not only generate the features but also to discriminatively separate them.
  arXiv  Detail & Related papers  (2020-10-19T12:36:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.