Cascade R-CNN: Delving into High Quality Object Detection

In object detection, an intersection over union (IoU) threshold is required to define positives and negatives. An object detector, trained with low IoU threshold, e.g. 0.5, usually produces noisy detections. However, detection performance tends to degrade with increasing the IoU thresholds. Two main factors are responsible for this: 1) overfitting during training, due to exponentially vanishing positive samples, and 2) inference-time mismatch between the IoUs for which the detector is optimal and those of the input hypotheses. A multi-stage object detection architecture, the Cascade R-CNN, is proposed to address these problems. It consists of a sequence of detectors trained with increasing IoU thresholds, to be sequentially more selective against close false positives. The detectors are trained stage by stage, leveraging the observation that the output of a detector is a good distribution for training the next higher quality detector. The resampling of progressively improved hypotheses guarantees that all detectors have a positive set of examples of equivalent size, reducing the overfitting problem. The same cascade procedure is applied at inference, enabling a closer match between the hypotheses and the detector quality of each stage. A simple implementation of the Cascade R-CNN is shown to surpass all single-model object detectors on the challenging COCO dataset. Experiments also show that the Cascade R-CNN is widely applicable across detector architectures, achieving consistent gains independently of the baseline detector strength. The code will be made available at https://github.com/zhaoweicai/cascade-rcnn

Semantically Consistent Regularization for Zero-Shot Recognition

The role of semantics in zero-shot learning is considered. The effectiveness of previous approaches is analyzed according to the form of supervision provided. While some learn semantics independently, others only supervise the semantic subspace explained by training classes. Thus, the former is able to constrain the whole space but lacks the ability to model semantic correlations. The latter addresses this issue but leaves part of the semantic space unsupervised. This complementarity is exploited in a new convolutional neural network (CNN) framework, which proposes the use of semantics as constraints for recognition.Although a CNN trained for classification has no transfer ability, this can be encouraged by learning an hidden semantic layer together with a semantic code for classification. Two forms of semantic constraints are then introduced. The first is a loss-based regularizer that introduces a generalization constraint on each semantic predictor. The second is a codeword regularizer that favors semantic-to-class mappings consistent with prior semantic knowledge while allowing these to be learned from data. Significant improvements over the state-of-the-art are achieved on several datasets.Comment: Accepted to CVPR 201

Investigating the rotational evolution of young, low mass stars using Monte Carlo simulations

We investigate the rotational evolution of young stars through Monte Carlo simulations. We simulate 280,000 stars, each of which is assigned a mass, a rotational period, and a mass accretion rate. The mass accretion rate depends on mass and time, following power-laws indices 1.4 and -1.5, respectively. A mass-dependent accretion threshold is defined below which a star is considered as diskless, which results in a distribution of disk lifetimes that matches observations. Stars are evolved at constant angular spin rate while accreting and at constant angular momentum when they become diskless. We recover the bimodal period distribution seen in several young clusters. The short period peak consists mostly of diskless stars and the long period one is mainly populated by accreting stars. Both distributions present a long tail towards long periods and a population of slowly rotating diskless stars is observed at all ages. We reproduce the observed correlations between disk fraction and spin rate, as well as between IR excess and rotational period. The period-mass relation we derive from the simulations exhibits the same global trend as observed in young clusters only if we release the disk locking assumption for the lowest mass stars. We find that the time evolution of median specific angular momentum follows a power law index of -0.65 for accreting stars and of -0.53 for diskless stars, a shallower slope that results from a wide distribution of disk lifetimes. Using observationally-documented distributions of disk lifetimes, mass accretion rates, and initial rotation periods, and evolving an initial population from 1 to 12 Myr, we reproduce the main characteristics of pre-main sequence angular momentum evolution, which supports the disk locking hypothesis. (abridged)Comment: 11 pages, 14 figures, accepted for publication in A&