Object representations in long-term memory as feature sets derived from affordances

Eren, Selda


Object Augmentation for Out-of-Context Object Recognition
Eryüksel, Oğul Can; Kalkan, Sinan; Department of Computer Engineering (2022-5-18)
The visual context in an image contains rich information about and between foreground objects and the background. Deep learning models learn contextual information implicitly in general. However, since training datasets generally do not include all possible contexts, deep models tend to memorize contextual details. This can lead to poor recognition performance when models are deployed in real-world applications since objects may appear in unexpected contexts or places. These types of objects are called out-...
Object extraction from ımages/videos using a genetic algorithm based approach
Yılmaz, Turgay; Yazıcı, Adnan; Department of Computer Engineering (2008)
The increase in the use of digital video/image has showed the need for modeling and querying the semantic content in them. Using manual annotation techniques for defining the semantic content is both costly in time and have limitations on querying capabilities. So, the need for content based information retrieval in multimedia domain is to extract the semantic content in an automatic way. The semantic content is usually defined with the objects in images/videos. In this thesis, a Genetic Algorithm based obj...
Halici, Eren; Alatan, Abdullah Aydın (2018-10-10)
Object localization can be defined as the task of finding the bounding boxes of objects in a scene. Most of the state-of-the-art approaches utilize meticulously handcrafted training datasets. In this work, we are aiming to create a generative adversarial reinforcement learning framework, which can work without having any explicit bounding box information. Instead of relying on bounding boxes, our framework uses tightly cropped object images as training data. Our image localization framework consists of two ...
Object-first orders in Turkish do not pose a challenge during processing
Özge, Duygu; Zeyrek Bozşahin, Deniz (2013-01-15)
Object Detection with Convolutional Context Features
Kaya, Emre Can; Alatan, Abdullah Aydın (2017-01-01)
A novel extension to Huh B-ESA object detection algorithm is proposed in order to learn convolutional context features for determining boundaries of objects better. For input images, the hypothesis windows and their context around those windows are learned through convolutional layers as two parallel networks. The resulting object and context feature maps are combined in such a way that they preserve their spatial relationship. The proposed algorithm is trained and evaluated on PASCAL VOC 2007 detection ben...
Citation Formats
S. Eren, “Object representations in long-term memory as feature sets derived from affordances,” 2008, vol. 43, p. 59, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/63438.