Deep Self-Taught Learning for Weakly Supervised Object Localization

Most existing weakly supervised localization (WSL) approaches learn detectors by finding positive bounding boxes based on features learned with image-level supervision. However, those features do not contain spatial location related information and usually provide poor-quality positive samples for training a detector. To overcome this issue, we propose a deep self-taught learning approach, which makes the detector learn the object-level features reliable for acquiring tight positive samples and afterwards re-train itself based on them. Consequently, the detector progressively improves its detection ability and localizes more informative positive samples. To implement such self-taught learning, we propose a seed sample acquisition method via image-to-object transferring and dense subgraph discovery to find reliable positive samples for initializing the detector. An online supportive sample harvesting scheme is further proposed to dynamically select the most confident tight positive samples and train the detector in a mutual boosting way. To prevent the detector from being trapped in poor optima due to overfitting, we propose a new relative improvement of predicted CNN scores for guiding the self-taught learning process. Extensive experiments on PASCAL 2007 and 2012 show that our approach outperforms the state-of-the-arts, strongly validating its effectiveness.Comment: Accepted as spotlight paper by CVPR 201

Cubic vertex-transitive non-Cayley graphs of order 12p

A graph is said to be {\em vertex-transitive non-Cayley} if its full automorphism group acts transitively on its vertices and contains no subgroups acting regularly on its vertices. In this paper, a complete classification of cubic vertex-transitive non-Cayley graphs of order $12p$, where $p$ is a prime, is given. As a result, there are $11$ sporadic and one infinite family of such graphs, of which the sporadic ones occur when $p=5$, $7$ or $17$, and the infinite family exists if and only if $p\equiv1\ (\mod 4)$, and in this family there is a unique graph for a given order.Comment: This paper has been accepted for publication in SCIENCE CHINA Mathematic

Giant X-ray Bump in GRB 121027A: Evidence for Fall-back Disk Accretion

A particularly interesting discovery in observations of GRB 121027A is that of a giant X-ray bump detected by the Swift/X-Ray Telescope. The X-ray afterglow re-brightens sharply at about 1000 s after the trigger by more than two orders of magnitude in less than 200 s. This X-ray bump lasts for more than 10 ks. It is quite different from typical X-ray flares. In this Letter we propose a fall-back accretion model to interpret this X-ray bump within the context of the collapse of a massive star for a long-duration gamma-ray burst. The required fall-back radius of about 3.5e10 cm and mass of about 0.9-2.6 solar masses imply that a significant part of the helium envelope should survive through the mass loss during the last stage of the massive progenitor of GRB 121027A.Comment: 5 pages, 3 figures, 2013, ApJL, 767:L3

Physical origin of multi-wavelength emission of GRB 100418A and implications for its progenitor

GRB 100418A is a long burst at z=0.624 without detection of any associated supernova (SN). Its lightcurves in both the prompt and afterglow phases are similar to GRB 060614, a nearby long GRB without an associated SN. We analyze the observational data of this event and discuss the possible origins of its multi-wavelength emission. We show that its joint lightcurve at 1 keV derived from Swift BAT and XRT observations is composed of two distinguished components. The first component, whose spectrum is extremely soft (\Gamma = 4.32), ends with a steep decay segment, indicating the internal origin of this component. The second component is a slowly-rising, broad bump which peaks at ~10^5 seconds post the BAT trigger. Assuming that the late bump is due to onset of the afterglow, we derive the initial Lorentz factor (Gamma_0) of the GRB fireball and find that it significantly deviates from the relation between the Gamma_0 and Eiso of typical GRBs. We also check whether it follows the same anti-correlation between X-ray luminosity and the break time observed in the shallow decay phase of many typical GRBs, which is usually regarded as a signal of late energy injection from the GRB central engine. However, we find that it does not obey this correlation. We propose that the late bump could be contributed by a two-component jet. We fit the second component with an off-axis jet model for a constant medium density and find the late bump can be represented by the model. The derived jet half-opening angle is 0.30 rad and the viewing angle is 0.315 rad. The medium density is 0.05 cm^-3, possibly suggesting that it may be from a merger of compact stars. The similarity between GRBs 060614 and 100418A may indicate that the two GRBs are from the same population and the late bump observed in the two GRBs may be a signal of a two-component jet powered by the GRB central engine.Comment: 8 pages, 3 figures, accepted for publication in Research in Astron. Astrophy

Deep Learning with S-shaped Rectified Linear Activation Units

Rectified linear activation units are important components for state-of-the-art deep convolutional networks. In this paper, we propose a novel S-shaped rectified linear activation unit (SReLU) to learn both convex and non-convex functions, imitating the multiple function forms given by the two fundamental laws, namely the Webner-Fechner law and the Stevens law, in psychophysics and neural sciences. Specifically, SReLU consists of three piecewise linear functions, which are formulated by four learnable parameters. The SReLU is learned jointly with the training of the whole deep network through back propagation. During the training phase, to initialize SReLU in different layers, we propose a "freezing" method to degenerate SReLU into a predefined leaky rectified linear unit in the initial several training epochs and then adaptively learn the good initial values. SReLU can be universally used in the existing deep networks with negligible additional parameters and computation cost. Experiments with two popular CNN architectures, Network in Network and GoogLeNet on scale-various benchmarks including CIFAR10, CIFAR100, MNIST and ImageNet demonstrate that SReLU achieves remarkable improvement compared to other activation functions.Comment: Accepted by AAAI-1