Look-into-Object: Self-supervised Structure Modeling for Object Recognition
Most object recognition approaches predominantly focus on learning discriminative visual patterns while overlooking the holistic object structure. Though important, structure modeling usually requires significant manual annotations and therefore is labor-intensive. In this paper, we propose to "look into object" (explicitly yet intrinsically model the object structure) through incorporating self-supervisions into the traditional framework. We show the recognition backbone can be substantially enhanced for more robust representation learning, without any cost of extra annotation and inference speed. Specifically, we first propose an object-extent learning module for localizing the object according to the visual patterns shared among the instances in the same category. We then design a spatial context learning module for modeling the internal structures of the object, through predicting the relative positions within the extent. These two modules can be easily plugged into any backbone networks during training and detached at inference time. Extensive experiments show that our look-into-object approach (LIO) achieves large performance gain on a number of benchmarks, including generic object recognition (ImageNet) and fine-grained object recognition tasks (CUB, Cars, Aircraft). We also show that this learning paradigm is highly generalizable to other tasks such as object detection and segmentation (MS COCO). Project page: https://github.com/JDAI-CV/LIO.
Code (2)
Tasks
Fine-Grained Image ClassificationImage RecognitionInstance SegmentationObjectobject-detectionObject DetectionObject RecognitionRepresentation LearningSemantic SegmentationSimilar Papers 제목 키워드 기반
Self-supervised Learning: Generative or Contrastive
Deep supervised learning has achieved great success in the last decade. However, its deficiencies of dependence on manual labels and vulnerability to attacks have driven people to explore a better solution. As an alterna…
Graph LearningRepresentation LearningSelf-Supervised LearningSurveyLook into Person: Self-supervised Structure-sensitive Learning and A New Benchmark for Human Parsing
Human parsing has recently attracted a lot of research interests due to its huge application potentials. However existing datasets have limited number of images and annotations, and lack the variety of human appearances …
Human ParsingSelf-Supervised LearningSemantic SegmentationUnsupervised Object Localization: Observing the Background to Discover Objects
Recent advances in self-supervised visual representation learning have paved the way for unsupervised methods tackling tasks such as object discovery and instance segmentation. However, discovering objects in an image wi…
Instance SegmentationObjectObject DiscoveryObject Localization+7Self-supervised 3D Point Cloud Completion via Multi-view Adversarial Learning
In real-world scenarios, scanned point clouds are often incomplete due to occlusion issues. The task of self-supervised point cloud completion involves reconstructing missing regions of these incomplete objects without t…
Point Cloud CompletionQuantitative Evidence on Overlooked Aspects of Enrollment Speaker Embeddings for Target Speaker Separation
Single channel target speaker separation (TSS) aims at extracting a speaker's voice from a mixture of multiple talkers given an enrollment utterance of that speaker. A typical deep learning TSS framework consists of an u…
Speaker IdentificationSpeaker Separation