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1. Low-Power Multi-Camera Object Re-Identification using Hierarchical Neural Networks

   https://doi.org/10.1109/ISLPED52811.2021.9502480  

   Goel, Abhinav; Tung, Caleb; Hu, Xiao; Wang, Haobo; Davis, James C.; Thiruvathukal, George K.; Lu, Yung-Hsiang (July 2021, ACM/IEEE International Symposium on Low Power Electronics and Design)

   null (Ed.)

   Low-power computer vision on embedded devices has many applications. This paper describes a low-power technique for the object re-identification (reID) problem: matching a query image against a gallery of previously-seen images. State-of-the-art techniques rely on large, computationally-intensive Deep Neural Networks (DNNs). We propose a novel hierarchical DNN architecture that uses attribute labels in the training dataset to perform efficient object reID. At each node in the hierarchy, a small DNN identifies a different attribute of the query image. The small DNN at each leaf node is specialized to re-identify a subset of the gallery---only the images with the attributes identified along the path from the root to a leaf. Thus, a query image is re-identified accurately after processing with a few small DNNs. We compare our method with state-of-the-art object reID techniques. With a ~4% loss in accuracy, our approach realizes significant resource savings: 74% less memory, 72% fewer operations, and 67% lower query latency, yielding 65% less energy consumption. 

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2. Density Map Guided Object Detection in Aerial Images

   Li, Changlin; Yang, Taojiannan; Zhu, Sijie; Chen, Chen; Guan, Shanyue (January 2020, IEEE Conference on Computer Vision and Pattern Recognition Workshop)

   Object detection in high-resolution aerial images is a challenging task because of 1) the large variation in object size, and 2) non-uniform distribution of objects. A common solution is to divide the large aerial image into small (uniform) crops and then apply object detection on each small crop. In this paper, we investigate the image cropping strategy to address these challenges. Specifically, we propose a Density-Map guided object detection Network (DMNet), which is inspired from the observation that the object density map of an image presents how objects distribute in terms of the pixel intensity of the map. As pixel intensity varies, it is able to tell whether a region has objects or not, which in turn provides guidance for cropping images statistically. DMNet has three key components: a density map generation module, an image cropping module and an object detector. DMNet generates a density map and learns scale information based on density intensities to form cropping regions. Extensive experiments show that DMNet achieves state-of-the-art performance on two popular aerial image datasets, i.e. VisionDrone and UAVDT. 

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3. Image Retrieval with Similar Object Detection and Local Similarity to Detected Objects

   Hanif, Sidra; Li, Chao; Alazzawe, Anis; Latecki, Longin Jan (August 2019, Pacific Rim Int. Conf. on Artificial Intelligence)

   Commercial image search applications like eBay and Pinterest allow users to select the focused area as bounding box over the query images, which improves the retrieval accuracy. The focused area image retrieval strategy motivated our research, but our system has three main advantages over the existing works. (1) Given a query focus area, our approach localizes the most similar region in the database image and only this region is used for computing image similarity. This is done in a unified network whose weights are adjusted both for localization and similarity learning in an end-to-end manner. (2) This is achieved using fewer than five proposals extracted from a saliency map, which speedups the pairwise similarity computation. Usually hundreds or even thousands of proposals are used for localization. (3) For users, our system explains the relevance of the retrieved results by locating the regions in database images most similar to query object. Our method achieves significantly better retrieval performance than the off-the-shelf object localization-based retrieval methods and end-to-end trained triplet method with a region proposal network. Our experimental results demonstrate 86% retrieval rate as compared to 73% achieved by the existing methods on PASCAL VOC07 and VOC12 datasets. Extensive experiments are also conducted on the instance retrieval databases Oxford5k and INSTRE, wherewe exhibit competitive performance. Finally, we provide both quantitative and qualitative results of our retrieval method demonstrating its superiority over commercial image search systems. 

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4. Koios: Top-k Semantic Overlap Set Search

   https://doi.org/10.1109/ICDE55515.2023.00121  

   Mundra, Pranay; Zhang, Jianhao; Nargesian, Fatemeh; Augsten, Nikolaus (April 2023, 2023 IEEE 39th International Conference on Data Engineering (ICDE))

   We study the top-k set similarity search problem using semantic overlap. While vanilla overlap requires exact matches between set elements, semantic overlap allows elements that are syntactically different but semantically related to increase the overlap. The semantic overlap is the maximum matching score of a bipartite graph, where an edge weight between two set elements is defined by a user-defined similarity function, e.g., cosine similarity between embeddings. Common techniques like token indexes fail for semantic search since similar elements may be unrelated at the character level. Further, verifying candidates is expensive (cubic versus linear for syntactic overlap), calling for highly selective filters. We propose Koios, the first exact and efficient algorithm for semantic overlap search. Koios leverages sophisticated filters to minimize the number of required graph-matching calculations. Our experiments show that for medium to large sets less than 5% of the candidate sets need verification, and more than half of those sets are further pruned without requiring the expensive graph matching. We show the efficiency of our algorithm on four real datasets and demonstrate the improved result quality of semantic over vanilla set similarity search. 

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5. In-Memory Object Graph Stores

   https://doi.org/10.4230/LIPIcs.ECOOP.2025.30  

   Thimmaiah, Aditya; Yi, Zijian; Kenis, Joseph; Rossbach, Christopher J; Gligoric, Milos (January 2025, Schloss Dagstuhl – Leibniz-Zentrum für Informatik)

   Aldrich, Jonathan; Silva, Alexandra (Ed.)

   We present a design and implementation of an in-memory object graph store, dubbed εStore. Our key innovation is a storage model - epsilon store - that equates an object on the heap to a node in a graph store. Thus any object on the heap (without changes) can be a part of one, or multiple, graph stores, and vice versa, any node in a graph store can be accessed like any other object on the heap. Specifically, each node in a graph is an object (i.e., instance of a class), and its properties and its edges are the primitive and reference fields declared in its class, respectively. Necessary classes, which are instantiated to represent nodes, are created dynamically by εStore. εStore uses a subset of the Cypher query language to query the graph store. By design, the result of any query is a table (ResultSet) of references to objects on the heap, which users can manipulate the same way as any other object on the heap in their programs. Moreover, a developer can include (transitively) an arbitrary object to become a part of a graph store. Finally, εStore introduces compile-time rewriting of Cypher queries into imperative code to improve the runtime performance. εStore can be used for a number of tasks including implementing methods for complex in-memory structures, writing complex assertions, or a stripped down version of a graph database that can conveniently be used during testing. We implement εStore in Java and show its application using the aforementioned tasks. 

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