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MVDet: multi-view multi-class object detection without ground plane assumption
Although many state-of-the-art methods of object detection in a single image have achieved great success in the last few years, they still suffer...
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CAM R-CNN: End-to-End Object Detection with Class Activation Maps
Class activation maps (CAMs) have been widely used on weakly-supervised object localization, which generate attention maps for specific categories in...
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Multi-class object detection system using hybrid convolutional neural network architecture
Object detection in computer vision has been a significant research area for the past decade. Identifying objects with multiple classes from an image...
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RT-Net: replay-and-transfer network for class incremental object detection
Despite the remarkable performance achieved by DNN-based object detectors, class incremental object detection (CIOD) remains a challenge, in which...
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Object search by a concept-conditioned object detector
Object detectors are used for searching all objects belonging to a pre-defined set of categories contained in a given picture. However, users are...
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Over-sampling strategy-based class-imbalanced salient object detection and its application in underwater scene
One major branch of bottom-up salient object detection methods is machine learning-based methods which learn to classify salient object(positive) and...
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COOLer: Class-Incremental Learning for Appearance-Based Multiple Object Tracking
Continual learning allows a model to learn multiple tasks sequentially while retaining the old knowledge without the training data of the preceding...
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Comprehending Object State via Dynamic Class Invariant Learning
Maintaining software is cumbersome when method argument constraints are undocumented. To reveal them, previous work learned preconditions from...
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Exploring class-agnostic pixels for scribble-supervised high-resolution salient object detection
Successful salient object detection is largely dependent on large-scale fine-grained annotated datasets. However, pixel-level annotation is a...
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Object coverage criteria for supporting object-oriented testing
Code coverage criteria are widely used in object-oriented (OO) domains as test quality indicators. However, these criteria are based on the...
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Image Classification Using Class-Agnostic Object Detection
Human-in-the-loop interfaces for machine learning provide a promising way to reduce the annotation effort required to obtain an accurate machine...
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Cross-domain object detection by local to global object-aware feature alignment
Cross-domain object detection has attracted more and more attention recently. It reduces the gap between the two domains, where the source domain is...
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Multi-object behaviour recognition based on object detection cascaded image classification in classroom scenes
For multi-object behaviour recognition in classroom scenes, crowded objects have heavy occlusion, invisible keypoints, scale variation, which...
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Unsupervised Domain Adaptive Object Detection with Class Label Shift Weighted Local Features
Due to the high transferability of features extracted from early layers (called local features), aligning marginal distributions of local features...
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OV-DAR: Open-Vocabulary Object Detection and Attributes Recognition
In this paper, we endeavor to localize all potential objects in an image and infer their visual categories, attributes, and shapes, even in instances...
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A broader study of cross-domain few-shot object detection
Latest studies on few-shot object detection (FSOD) mainly focuses on achieving better performance in novel class through few-shot fine-tuning. This...
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CAT: A Simple yet Effective Cross-Attention Transformer for One-Shot Object Detection
Given a query patch from a novel class, one-shot object detection aims to detect all instances of this class in a target image through the semantic...
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Predicting object properties based on movement kinematics
In order to grasp and transport an object, grip and load forces must be scaled according to the object’s properties (such as weight). To select the...
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Towards Non Co-occurrence Incremental Object Detection with Unlabeled In-the-Wild Data
Deep networks have shown remarkable results in the task of object detection. However, their performance suffers critical drops when they are...
