Language-conditioned Detection Transformer

We present a new open-vocabulary detection framework. Our framework uses both image-level labels and detailed detection annotations when available. Our framework proceeds in three steps. We first train a language-conditioned object detector on fully-supervised detection data. This detector gets to see the presence or absence of ground truth classes during training, and conditions prediction on the set of present classes. We use this detector to pseudo-label images with image-level labels. Our detector provides much more accurate pseudo-labels than prior approaches with its conditioning mechanism. Finally, we train an unconditioned open-vocabulary detector on the pseudo-annotated images. The resulting detector, named DECOLA, shows strong zero-shot performance in open-vocabulary LVIS benchmark as well as direct zero-shot transfer benchmarks on LVIS, COCO, Object365, and OpenImages. DECOLA outperforms the prior arts by 17.1 AP-rare and 9.4 mAP on zero-shot LVIS benchmark. DECOLA achieves state-of-the-art results in various model sizes, architectures, and datasets by only training on open-sourced data and academic-scale computing. Code is available at https://github.com/janghyuncho/DECOLA.Comment: Code is at https://github.com/janghyuncho/DECOL

Innovation strategy of science and technology in Korea

노트 : 16th International Conference on Composite Material

AAVR-Displaying Interfaces: Serotype-Independent Adeno-Associated Virus Capture and Local Delivery Systems.

Interfacing gene delivery vehicles with biomaterials has the potential to play a key role in diversifying gene transfer capabilities, including localized, patterned, and controlled delivery. However, strategies for modifying biomaterials to interact with delivery vectors must be redesigned whenever new delivery vehicles and applications are explored. We have developed a vector-independent biomaterial platform capable of interacting with various adeno-associated viral (AAV) serotypes. A water-soluble, cysteine-tagged, recombinant protein version of the recently discovered multi-AAV serotype receptor (AAVR), referred to as cys-AAVR, was conjugated to maleimide-displaying polycaprolactone (PCL) materials using click chemistry. The resulting cys-AAVR-PCL system bound to a broad range of therapeutically relevant AAV serotypes, thereby providing a platform capable of modulating the delivery of all AAV serotypes. Intramuscular injection of cys-AAVR-PCL microspheres with bound AAV vectors resulted in localized and sustained gene delivery as well as reduced spread to off-target organs compared to a vector solution. This cys-AAVR-PCL system is thus an effective approach for biomaterial-based AAV gene delivery for a broad range of therapeutic applications

Smart Roadside System for Driver Assistance and Safety Warnings: Framework and Applications

The use of newly emerging sensor technologies in traditional roadway systems can provide real-time traffic services to drivers through Telematics and Intelligent Transport Systems (ITSs). This paper introduces a smart roadside system that utilizes various sensors for driver assistance and traffic safety warnings. This paper shows two road application models for a smart roadside system and sensors: a red-light violation warning system for signalized intersections, and a speed advisory system for highways. Evaluation results for the two services are then shown using a micro-simulation method. In the given real-time applications for drivers, the framework and certain algorithms produce a very efficient solution with respect to the roadway type features and sensor type use

Evaluation of the usefulness of three-dimensional optical coherence tomography in a guinea pig model of endolymphatic hydrops induced by surgical obliteration of the endolymphatic duct

Optical coherence tomography (OCT) has advanced significantly over the past two decades and is currently used extensively to monitor the internal structures of organs, particularly in ophthalmology and dermatology. We used ethylenediamine tetra-acetic acid (EDTA) to decalcify the bony walls of the cochlea and investigated the inner structures by deep penetration of light into the cochlear tissue using OCT on a guinea pig model of endolymphatic hydrops (EH), induced by surgical obliteration of the endolymphatic duct. The structural and functional changes associated with EH were identified using OCT and auditory brainstem response tests, respectively. We also evaluated structural alterations in the cochlea using three-dimensional reconstruction of the OCT images, which clearly showed physical changes in the cochlear structures. Furthermore, we found significant anatomical variations in the EH model and conducted graphical analysis by strial atrophy for comparison. The physical changes included damage to and flattening of the organ of Corti-evidence of Reissner's membrane distention-and thinning of the lateral wall. These results indicate that observation of EDTA-decalcified cochlea using OCT is significant in examination of gradual changes in the cochlear structures that are otherwise not depicted by hematoxylin and eosin staining © The Authorsopen0

Generalized Guidance Scheme for Low-Thrust Orbit Transfer

The authors present an orbital guidance scheme for the satellite with an electrical propulsion system using a Lyapunov feedback control. The construction of a Lyapunov candidate is based on orbital elements, which consist of angular momentum and eccentricity vectors. This approach performs orbit transfers between any two arbitrary elliptic or circular orbits without any singularity issues. These orbital elements uniquely describe a non degenerate Keplerian orbit. The authors improve the reliability of the existing Lyapunov orbital guidance scheme by considering the energy term. Additional improvement is achieved by adding the penalty function. Furthermore, it is shown that the final suggested approach is suitable for the satellite passing the earth’s shadow area

NMS Strikes Back

Detection Transformer (DETR) directly transforms queries to unique objects by using one-to-one bipartite matching during training and enables end-to-end object detection. Recently, these models have surpassed traditional detectors on COCO with undeniable elegance. However, they differ from traditional detectors in multiple designs, including model architecture and training schedules, and thus the effectiveness of one-to-one matching is not fully understood. In this work, we conduct a strict comparison between the one-to-one Hungarian matching in DETRs and the one-to-many label assignments in traditional detectors with non-maximum supervision (NMS). Surprisingly, we observe one-to-many assignments with NMS consistently outperform standard one-to-one matching under the same setting, with a significant gain of up to 2.5 mAP. Our detector that trains Deformable-DETR with traditional IoU-based label assignment achieved 50.2 COCO mAP within 12 epochs (1x schedule) with ResNet50 backbone, outperforming all existing traditional or transformer-based detectors in this setting. On multiple datasets, schedules, and architectures, we consistently show bipartite matching is unnecessary for performant detection transformers. Furthermore, we attribute the success of detection transformers to their expressive transformer architecture. Code is available at https://github.com/jozhang97/DETA.Comment: Code is available at https://github.com/jozhang97/DET