Papers Semantic Part Detection
“Semantic Part Detection” 태그가 달린 논문 11편 · 필터 해제
Split, Merge, and Refine: Fitting Tight Bounding Boxes via Over-Segmentation and Iterative Search
Achieving tight bounding boxes of a shape while guaranteeing complete boundness is an essential task for efficient geometric operations and unsupervised semantic part detection. But previous methods fail to achieve both …
SegmentationSemantic Part DetectionSensitivityAttention-based Joint Detection of Object and Semantic Part
In this paper, we address the problem of joint detection of objects like dog and its semantic parts like face, leg, etc. Our model is created on top of two Faster-RCNN models that share their features to perform a novel …
Objectobject-detectionObject Detectionregression+1Fine-Grained 3D Shape Classification with Hierarchical Part-View Attentions
Fine-grained 3D shape classification is important for shape understanding and analysis, which poses a challenging research problem. However, the studies on the fine-grained 3D shape classification have rarely been explor…
3D Shape ClassificationGeneral ClassificationRegion ProposalSemantic Part DetectionPose-Guided Knowledge Transfer for Object Part Segmentation
Object part segmentation is an important problem for many applications, but generating the annotations to train a part segmentation model is typically quite labor-intensive. Recently, Fang et al. augmented object part se…
ObjectSegmentationSemantic Part DetectionTransfer LearningSemantic Part Detection via Matching: Learning to Generalize to Novel Viewpoints from Limited Training Data
Detecting semantic parts of an object is a challenging task in computer vision, particularly because it is hard to construct large annotated datasets due to the difficulty of annotating semantic parts. In this paper we p…
ClusteringObjectSemantic Part DetectionVisual Concepts and Compositional Voting
It is very attractive to formulate vision in terms of pattern theory \cite{Mumford2010pattern}, where patterns are defined hierarchically by compositions of elementary building blocks. But applying pattern theory to real…
ClusteringSemantic Part DetectionDeepVoting: A Robust and Explainable Deep Network for Semantic Part Detection under Partial Occlusion
In this paper, we study the task of detecting semantic parts of an object, e.g., a wheel of a car, under partial occlusion. We propose that all models should be trained without seeing occlusions while being able to trans…
Semantic Part DetectionDetecting Semantic Parts on Partially Occluded Objects
In this paper, we address the task of detecting semantic parts on partially occluded objects. We consider a scenario where the model is trained using non-occluded images but tested on occluded images. The motivation is t…
ClusteringSemantic Part DetectionObjects as context for detecting their semantic parts
We present a semantic part detection approach that effectively leverages object information.We use the object appearance and its class as indicators of what parts to expect. We also model the expected relative location o…
ObjectSemantic Part DetectionSPDA-CNN: Unifying Semantic Part Detection and Abstraction for Fine-Grained Recognition
Most convolutional neural networks (CNNs) lack midlevel layers that model semantic parts of objects. This limits CNN-based methods from reaching their full potential in detecting and utilizing small semantic parts in rec…
General ClassificationObject RecognitionSemantic Part DetectionDetect What You Can: Detecting and Representing Objects using Holistic Models and Body Parts
Detecting objects becomes difficult when we need to deal with large shape deformation, occlusion and low resolution. We propose a novel approach to i) handle large deformations and partial occlusions in animals (as examp…
ObjectSemantic Part Detection