Zero-shot Object Detection via Semantic Embedding Alignment
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Solution Overview
Problem
Zero-shot object detection faces challenges in accurately identifying objects and background classes due to unclear background definitions and the inclusion of unseen object classes, which complicates the discrimination between objects and backgrounds.
Innovation Solution
A method for creating a semantic embedding space where word vectors and feature vectors of object bounding boxes are embedded together, allowing for the alignment of object features with class label embeddings, enabling the detection of unseen object classes without explicit training, and defining background classes to improve detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a background object class is added to discriminate between objects and backgrounds in fully supervised detection, then object detection accuracy is improved, but the meaning of background becomes unclear in zero-shot detection leading to identification of both background stuff and unseen objects as background
Solution Approach 1:
The patent segments the background into two distinct categories: known background classes (e.g., sky, wall, road) and unknown object classes. This segmentation allows the system to differentiate between actual background elements and unseen objects, resolving the ambiguity in background definition while maintaining detection accuracy for both categories.
Solution Approach 2:
The patent introduces an intermediary mechanism using semantic embedding space and class-conditional GANs that mediate between the detector and the ambiguous background regions. This intermediary allows the system to infer whether a region represents background or unseen objects based on semantic relationships and generated appearances, rather than relying on clear-cut background definitions.
2Adaptability or versatility
If zero-shot detection is performed without training on unseen object classes, then adaptability to new objects is improved, but detection accuracy for unseen classes deteriorates due to lack of training data
Solution Approach 1:
The patent performs preliminary action by pre-training the class-conditional GANs on known object classes and embedding spaces before encountering unseen objects. This pre-training establishes the semantic embedding space and generative models that enable accurate detection of unseen classes without direct training data, as the system can infer appearances and characteristics from the pre-trained semantic relationships.
Solution Approach 2:
The patent uses copying by generating synthetic appearances of unseen objects through class-conditional GANs that copy the semantic embedding space and stylistic characteristics from known objects. This allows the system to create virtual training examples and infer object appearances without actual training data for the unseen classes, maintaining high detection accuracy through synthesized representations.
3Reliability
If contextual cues are used for object classification, then classification reliability is improved, but object detection localization precision deteriorates due to contextual correlations interfering with exact bounding box localization
Solution Approach 1:
The patent segments the detection task into two independent components: a classification component that uses contextual cues for reliable object identification, and a localization component that uses object-specific appearance features and class-conditional GANs for precise bounding box localization. This segmentation allows each component to optimize for its specific function without interference from contextual correlations.
Solution Approach 2:
The patent introduces semantic embedding space as an intermediary that mediates between contextual classification cues and precise localization requirements. The semantic embedding space captures both contextual relationships for classification and object-specific visual features for localization, allowing the system to transition smoothly from contextual inference to precise bounding box prediction without the interference of contextual correlations.
Data Source
AI summary
A method, apparatus and system for zero shot object detection includes, in a semantic embedding space having embedded object class labels, training the space by embedding extracted features of bounding boxes and object class labels of labeled bounding boxes of known object classes into the space, determining regions in an image having unknown object classes on which to perform object detection as proposed bounding boxes, extracting features of the proposed bounding boxes, projecting the extracted features of the proposed bounding boxes into the space, computing a similarity measure between the projected features of the proposed bounding boxes and the embedded, extracted features of the bounding boxes of the known object classes in the space, and predicting an object class label for proposed bounding boxes by determining a nearest embedded object class label to the projected features of the proposed bounding boxes in the space based on the similarity measures.