Multi-Label Image Classification via Knowledge Distillation from Weakly-Supervised Detection

Multi-label image classification is a fundamental but challenging task towards general visual understanding. Existing methods found the region-level cues (e.g., features from RoIs) can facilitate multi-label classification. Nevertheless, such methods usually require laborious object-level annotations (i.e., object labels and bounding boxes) for effective learning of the object-level visual features. In this paper, we propose a novel and efficient deep framework to boost multi-label classification by distilling knowledge from weakly-supervised detection task without bounding box annotations. Specifically, given the image-level annotations, (1) we first develop a weakly-supervised detection (WSD) model, and then (2) construct an end-to-end multi-label image classification framework augmented by a knowledge distillation module that guides the classification model by the WSD model according to the class-level predictions for the whole image and the object-level visual features for object RoIs. The WSD model is the teacher model and the classification model is the student model. After this cross-task knowledge distillation, the performance of the classification model is significantly improved and the efficiency is maintained since the WSD model can be safely discarded in the test phase. Extensive experiments on two large-scale datasets (MS-COCO and NUS-WIDE) show that our framework achieves superior performances over the state-of-the-art methods on both performance and efficiency.Comment: accepted by ACM Multimedia 2018, 9 pages, 4 figures, 5 table

On the selection and design of proteins and peptide derivatives for the production of photoluminescent, red-emitting gold quantum clusters

Novel pathways of the synthesis of photoluminescent gold quantum clusters (AuQCs) using biomolecules as reactants provide biocompatible products for biological imaging techniques. In order to rationalize the rules for the preparation of red-emitting AuQCs in aqueous phase using proteins or peptides, the role of different organic structural units was investigated. Three systems were studied: proteins, peptides, and amino acid mixtures, respectively. We have found that cysteine and tyrosine are indispensable residues. The SH/S-S ratio in a single molecule is not a critical factor in the synthesis, but on the other hand, the stoichiometry of cysteine residues and the gold precursor is crucial. These observations indicate the importance of proper chemical behavior of all species in a wide size range extending from the atomic distances (in the AuI-S semi ring) to nanometer distances covering the larger sizes of proteins assuring the hierarchical structure of the whole self-assembled system

Hysteresis of Electronic Transport in Graphene Transistors

Graphene field effect transistors commonly comprise graphene flakes lying on SiO2 surfaces. The gate-voltage dependent conductance shows hysteresis depending on the gate sweeping rate/range. It is shown here that the transistors exhibit two different kinds of hysteresis in their electrical characteristics. Charge transfer causes a positive shift in the gate voltage of the minimum conductance, while capacitive gating can cause the negative shift of conductance with respect to gate voltage. The positive hysteretic phenomena decay with an increase of the number of layers in graphene flakes. Self-heating in helium atmosphere significantly removes adsorbates and reduces positive hysteresis. We also observed negative hysteresis in graphene devices at low temperature. It is also found that an ice layer on/under graphene has much stronger dipole moment than a water layer does. Mobile ions in the electrolyte gate and a polarity switch in the ferroelectric gate could also cause negative hysteresis in graphene transistors. These findings improved our understanding of the electrical response of graphene to its surroundings. The unique sensitivity to environment and related phenomena in graphene deserve further studies on nonvolatile memory, electrostatic detection and chemically driven applications.Comment: 13 pages, 6 Figure

End-to-end Alternating Optimization for Real-World Blind Super Resolution

Blind Super-Resolution (SR) usually involves two sub-problems: 1) estimating the degradation of the given low-resolution (LR) image; 2) super-resolving the LR image to its high-resolution (HR) counterpart. Both problems are ill-posed due to the information loss in the degrading process. Most previous methods try to solve the two problems independently, but often fall into a dilemma: a good super-resolved HR result requires an accurate degradation estimation, which however, is difficult to be obtained without the help of original HR information. To address this issue, instead of considering these two problems independently, we adopt an alternating optimization algorithm, which can estimate the degradation and restore the SR image in a single model. Specifically, we design two convolutional neural modules, namely \textit{Restorer} and \textit{Estimator}. \textit{Restorer} restores the SR image based on the estimated degradation, and \textit{Estimator} estimates the degradation with the help of the restored SR image. We alternate these two modules repeatedly and unfold this process to form an end-to-end trainable network. In this way, both \textit{Restorer} and \textit{Estimator} could get benefited from the intermediate results of each other, and make each sub-problem easier. Moreover, \textit{Restorer} and \textit{Estimator} are optimized in an end-to-end manner, thus they could get more tolerant of the estimation deviations of each other and cooperate better to achieve more robust and accurate final results. Extensive experiments on both synthetic datasets and real-world images show that the proposed method can largely outperform state-of-the-art methods and produce more visually favorable results. The codes are rleased at \url{https://github.com/greatlog/RealDAN.git}.Comment: Extension of our previous NeurIPS paper. Accepted to IJC

Learning the Degradation Distribution for Blind Image Super-Resolution

Synthetic high-resolution (HR) \& low-resolution (LR) pairs are widely used in existing super-resolution (SR) methods. To avoid the domain gap between synthetic and test images, most previous methods try to adaptively learn the synthesizing (degrading) process via a deterministic model. However, some degradations in real scenarios are stochastic and cannot be determined by the content of the image. These deterministic models may fail to model the random factors and content-independent parts of degradations, which will limit the performance of the following SR models. In this paper, we propose a probabilistic degradation model (PDM), which studies the degradation $\mathbf{D}$ as a random variable, and learns its distribution by modeling the mapping from a priori random variable $\mathbf{z}$ to $\mathbf{D}$. Compared with previous deterministic degradation models, PDM could model more diverse degradations and generate HR-LR pairs that may better cover the various degradations of test images, and thus prevent the SR model from over-fitting to specific ones. Extensive experiments have demonstrated that our degradation model can help the SR model achieve better performance on different datasets. The source codes are released at \url{[email protected]:greatlog/UnpairedSR.git}.Comment: Accepted to CVRP202

First observation of ψ(2S)→pnˉπ−+c.c.\psi(2S) \to p \bar{n} \pi^- +c.c.

Using 14 million $\psi(2S)$ events collected with the Beijing Spectrometer (BESII) at the Beijing Electron-Positron Collider, the branching fractions of $\psi(2S)$ decays to $p \bar{n} \pi^-$ and $\bar{p} n \pi^+$ and the branching fractions of the main background channels $\psi(2S) \to p \bar{n} \pi^-\pi^0$, $\psi(2S) \to \gamma\chi_{c0} \to \gamma p \bar{n} \pi^-$, $\psi(2S) \to \gamma\chi_{c2} \to \gamma p \bar{n} \pi^-$, and $\psi(2S) \to \gamma \chi_{cJ} \to \gamma p \bar{n} \pi^- \pi^0$ are determined. The contributions of the $N^{\ast}$ resonances in $\psi(2S) \to p \bar{n} \pi^- +c.c.$ are also discussed.Comment: 19 pages, 8 figures, add vertex requirement systematic erro

Experimental study of ψ(2S)\psi(2S) decays to \K^+ K^- \pi^+ \pi^- \pi^0 final states

$K^+K^-\pi^+\pi^-\pi^0$ final states are studied using a sample of $14\times10^6$ $\psi(2S)$ decays collected with the Beijing Spectrometer (BESII) at the Beijing Electron-Position Collider. The branching fractions of $\psi(2S)$ decays to $K^+K^-\pi^+\pi^-\pi^0$, $\omega K^+ K^-$, $\omega f_0(1710)$, $K^{\ast}(892)^0 K^- \pi^+\pi^0+c.c.$, $K^{\ast}(892)^{+} K^- \pi^+\pi^- +c.c.$, $K^{\ast}(892)^{+} K^- \rho^0+c.c.$ and $K^{\ast}(892)^0 K^-\rho^+ + c.c.$ are determined. The first two agree with previous measurements, and the last five are first measurements.Comment: 19 pages, 9 figure

Search for psi(3770)\ra\rho\pi at the BESII detector at the Beijing Electron-Positron Collider

Non-$D\bar{D}$ decay \psppto \rhopi is searched for using a data sample of $(17.3\pm 0.5) pb^{-1}$ taken at the center-of-mass energy of 3.773 GeV by the BESII detector at the BEPC. No \rhopi signal is observed, and the upper limit of the cross section is measured to be \sigma(\EETO \rhopi)<6.0 pb at 90% C. L. Considering the interference between the continuum amplitude and the \pspp resonance amplitude, the branching fraction of \pspp decays to $\rho\pi$ is determined to be \BR(\pspp\ra\rho\pi)\in(6.0\times10^{-6}, 2.4\times10^{-3}) at 90% C. L. This is in agreement with the prediction of the $S$- and $D$-wave mixing scheme of the charmonium states for solving the ``\rhopi puzzle'' between \jpsi and \psp decays.Comment: 15 pages, 5 figure