Agglomerative Transformer for Human-Object Interaction Detection

We propose an agglomerative Transformer (AGER) that enables Transformer-based human-object interaction (HOI) detectors to flexibly exploit extra instance-level cues in a single-stage and end-to-end manner for the first time. AGER acquires instance tokens by dynamically clustering patch tokens and aligning cluster centers to instances with textual guidance, thus enjoying two benefits: 1) Integrality: each instance token is encouraged to contain all discriminative feature regions of an instance, which demonstrates a significant improvement in the extraction of different instance-level cues and subsequently leads to a new state-of-the-art performance of HOI detection with 36.75 mAP on HICO-Det. 2) Efficiency: the dynamical clustering mechanism allows AGER to generate instance tokens jointly with the feature learning of the Transformer encoder, eliminating the need of an additional object detector or instance decoder in prior methods, thus allowing the extraction of desirable extra cues for HOI detection in a single-stage and end-to-end pipeline. Concretely, AGER reduces GFLOPs by 8.5% and improves FPS by 36%, even compared to a vanilla DETR-like pipeline without extra cue extraction.Comment: Accepted by ICCV'2

Saliency in Augmented Reality

With the rapid development of multimedia technology, Augmented Reality (AR) has become a promising next-generation mobile platform. The primary theory underlying AR is human visual confusion, which allows users to perceive the real-world scenes and augmented contents (virtual-world scenes) simultaneously by superimposing them together. To achieve good Quality of Experience (QoE), it is important to understand the interaction between two scenarios, and harmoniously display AR contents. However, studies on how this superimposition will influence the human visual attention are lacking. Therefore, in this paper, we mainly analyze the interaction effect between background (BG) scenes and AR contents, and study the saliency prediction problem in AR. Specifically, we first construct a Saliency in AR Dataset (SARD), which contains 450 BG images, 450 AR images, as well as 1350 superimposed images generated by superimposing BG and AR images in pair with three mixing levels. A large-scale eye-tracking experiment among 60 subjects is conducted to collect eye movement data. To better predict the saliency in AR, we propose a vector quantized saliency prediction method and generalize it for AR saliency prediction. For comparison, three benchmark methods are proposed and evaluated together with our proposed method on our SARD. Experimental results demonstrate the superiority of our proposed method on both of the common saliency prediction problem and the AR saliency prediction problem over benchmark methods. Our data collection methodology, dataset, benchmark methods, and proposed saliency models will be publicly available to facilitate future research

Molecularly Imprinted Polymer-Based Plasmonic Immunosandwich Assay for Fast and Ultrasensitive Determination of Trace Glycoproteins in Complex Samples

Glycoproteins play significant roles in many biological processes. Assays of glycoproteins have significant biological importance and clinical values, for which immunoassay has been the workhorse tool. However, immunoassay suffers from some disadvantages, such as poor availability of high-specificity antibodies and limited stability of biological reagents. Herein, we present an antibody-free and enzyme-free approach, called molecularly imprinted polymer (MIP)-based plasmonic immunosandwich assay (PISA), for fast and ultrasensitive detection of trace glycoproteins in complex samples. A gold-based boronate affinity MIP array was used to specifically extract the target glycoprotein from complex samples. After washing away unwanted species, the captured glycoprotein was labeled with boronate affinity silver-based Raman nanotags. Thus, sandwich-like complexes were formed on the array. Upon being shined with a laser beam, the gold-based array generated a surface plasmon wave, which significantly enhanced the surface-enhanced Raman scattering (SERS) signal of the silver-based Raman nanotags. The MIP ensured the specificity of the assay, while the plasmonic detection provided ultrahigh sensitivity. Erythropoietin (EPO), a glycoprotein hormone that controls erythropoiesis or red blood cell production, was employed as a test glycoprotein in this study. Specific detection of EPO in solution down to 2.9 × 10^–14 M was achieved. Using a novel strategy to accommodate the method of standard addition to a logarithmic dose–response relationship, EPO in human urine was quantitatively determined by this approach. The analysis time required only 30 min in total. This approach holds promising application prospects in many areas, such as biochemical research, clinical diagnosis, and antidoping analysis

Core-independent approach for polymer brush-functionalised nanomaterials with a fluorescent tag for RNA delivery.

Here we describe a core-independent approach enabling the grafting of polymer brushes from the surface of nanomaterials and microparticles for oligonucleotide delivery. This method is based on the adsorption of a polyelectrolyte macroinitiator (MI) combined with a fluorescent conjugated polymer for efficient and stable labelling. This allows dense brushes to be generated, with growth kinetics comparable to those observed from mono-functional initiators, for the imaging of nanomaterial cellular localisation and uptake. We also study the impact of brush chemistry on interactions with cell membranes and on transfection efficiency. The method we report offers a unique freedom of design of the core size and shape as well as surface chemistry, whilst enabling tagging, for the study of transfection processes or theragnostic applications