Towards High Performance Video Object Detection

There has been significant progresses for image object detection in recent years. Nevertheless, video object detection has received little attention, although it is more challenging and more important in practical scenarios. Built upon the recent works, this work proposes a unified approach based on the principle of multi-frame end-to-end learning of features and cross-frame motion. Our approach extends prior works with three new techniques and steadily pushes forward the performance envelope (speed-accuracy tradeoff), towards high performance video object detection

The SCO at ten: Time for Europe to engage? EUCAM Commentary No. 15, July 2011

The Shanghai Cooperation Organisation (SCO) held its 10th anniversary summit in Kazakhstan’s capital, Astana, on 15 June to celebrate its achievements over the last decade and guide its future development. Contrary to the negative predictions that it would prove to be a paper tiger, over the past ten years the SCO has developed into a full-fledged organisation with a structure capable of managing its wide-ranging cooperation on security, economy, transportation, disaster relief, law enforcement, culture, etc

Relation Networks for Object Detection

Although it is well believed for years that modeling relations between objects would help object recognition, there has not been evidence that the idea is working in the deep learning era. All state-of-the-art object detection systems still rely on recognizing object instances individually, without exploiting their relations during learning. This work proposes an object relation module. It processes a set of objects simultaneously through interaction between their appearance feature and geometry, thus allowing modeling of their relations. It is lightweight and in-place. It does not require additional supervision and is easy to embed in existing networks. It is shown effective on improving object recognition and duplicate removal steps in the modern object detection pipeline. It verifies the efficacy of modeling object relations in CNN based detection. It gives rise to the first fully end-to-end object detector

Flow-Guided Feature Aggregation for Video Object Detection

Extending state-of-the-art object detectors from image to video is challenging. The accuracy of detection suffers from degenerated object appearances in videos, e.g., motion blur, video defocus, rare poses, etc. Existing work attempts to exploit temporal information on box level, but such methods are not trained end-to-end. We present flow-guided feature aggregation, an accurate and end-to-end learning framework for video object detection. It leverages temporal coherence on feature level instead. It improves the per-frame features by aggregation of nearby features along the motion paths, and thus improves the video recognition accuracy. Our method significantly improves upon strong single-frame baselines in ImageNet VID, especially for more challenging fast moving objects. Our framework is principled, and on par with the best engineered systems winning the ImageNet VID challenges 2016, without additional bells-and-whistles. The proposed method, together with Deep Feature Flow, powered the winning entry of ImageNet VID challenges 2017. The code is available at https://github.com/msracver/Flow-Guided-Feature-Aggregation

Deformable Convolutional Networks

Convolutional neural networks (CNNs) are inherently limited to model geometric transformations due to the fixed geometric structures in its building modules. In this work, we introduce two new modules to enhance the transformation modeling capacity of CNNs, namely, deformable convolution and deformable RoI pooling. Both are based on the idea of augmenting the spatial sampling locations in the modules with additional offsets and learning the offsets from target tasks, without additional supervision. The new modules can readily replace their plain counterparts in existing CNNs and can be easily trained end-to-end by standard back-propagation, giving rise to deformable convolutional networks. Extensive experiments validate the effectiveness of our approach on sophisticated vision tasks of object detection and semantic segmentation. The code would be released

A class of global large solutions to the magnetohydrodynamic equations with fractional dissipation

Abstract(#br)The global existence and regularity problem on the magnetohydrodynamic (MHD) equations with fractional dissipation is not well understood for many ranges of fractional powers. This paper examines this open problem from a different perspective. We construct a class of large solutions to the d -dimensional ( $$d=2,3$$ d = 2 , 3 ) MHD equations with any fractional power. The process presented here actually assesses that an initial data near any function whose Fourier transform lives in a compact set away from the origin always leads to a unique and global solution

Fluid flow-induced modulation of viability and osteodifferentiation of periodontal ligament stem cell spheroids-on-chip

Developing physiologically relevant in vitro models for studying periodontitis is crucial for understanding its pathogenesis and developing effective therapeutic strategies. In this study, we aimed to integrate the spheroid culture of periodontal ligament stem cells (PDLSCs) within a spheroid-on-chip microfluidic perfusion platform and to investigate the influence of interstitial fluid flow on morphogenesis, cellular viability, and osteogenic differentiation of PDLSC spheroids. PDLSC spheroids were seeded onto the spheroid-on-chip microfluidic device and cultured under static and flow conditions. Computational analysis demonstrated the translation of fluid flow rates of 1.2 μl min-1 (low-flow) and 7.2 μl min-1 (high-flow) to maximum fluid shear stress of 59 μPa and 360 μPa for low and high-flow conditions, respectively. The spheroid-on-chip microfluidic perfusion platform allowed for modulation of flow conditions leading to larger PDLSC spheroids with improved cellular viability under flow compared to static conditions. Modulation of fluid flow enhanced the osteodifferentiation potential of PDLSC spheroids, demonstrated by significantly enhanced alizarin red staining and alkaline phosphatase expression. Additionally, flow conditions, especially high-flow conditions, exhibited extensive calcium staining across both peripheral and central regions of the spheroids, in contrast to the predominantly peripheral staining observed under static conditions. These findings highlight the importance of fluid flow in shaping the morphological and functional properties of PDLSC spheroids. This work paves the way for future investigations exploring the interactions between PDLSC spheroids, microbial pathogens, and biomaterials within a controlled fluidic environment, offering insights for the development of innovative periodontal therapies, tissue engineering strategies, and regenerative approaches.</p