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- Detecting Objects from Space: An Evaluation of Deep-Learning Modern Approaches34
An evaluation of state-of-the-art deep-learning detectors including Faster R-CNN (Faster Regional CNN), RFCN (Region-based Fully Convolutional Networks), SNIPER (Scale Normalization for Image Pyramids with Efficient Resampling), Single-Shot Detector (SSD), YOLO (You Only Look Once), RetinaNet, and CenterNet for the object detection in videos captured by drones.
- Robust Frame-to-Frame Camera Rotation Estimation in Crowded Scenes5
A novel generalization of the Hough transform on SO(3) is introduced to efficiently and robustly find the camera rotation most compatible with optical flow, and is more accurate than any method, irrespective of speed.
- Token Compression Meets Compact Vision Transformers: a Survey and Comparative Evaluation for Edge AI3
It is revealed that while token compression methods are effective for general-purpose ViTs, they often underperform when directly applied to compact designs.
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- Learned Prior Information for Image Compression1
A method for image compression by integrating a deep neural network (DNN) with the better portable graphics (BPG) codec and achieves a good visual quality for the decompressed image.
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- Joint Architecture-Token-Bitwidth Multi-Axis Optimization of Vision Transformers for Semiconductor IC Packaging–
This paper presents one of the first holistic frameworks that jointly optimizes three complementary axes: architecture, token, and bit-width in ViTs and is among the earliest works to jointly optimize architecture, token, and bit-width dimensions in ViTs.
Publication data from OpenAlex; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-11. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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