27,888 research outputs found
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
Weak ergodicity breaking of receptor motion in living cells stemming from random diffusivity
Molecular transport in living systems regulates numerous processes underlying
biological function. Although many cellular components exhibit anomalous
diffusion, only recently has the subdiffusive motion been associated with
nonergodic behavior. These findings have stimulated new questions for their
implications in statistical mechanics and cell biology. Is nonergodicity a
common strategy shared by living systems? Which physical mechanisms generate
it? What are its implications for biological function? Here, we use single
particle tracking to demonstrate that the motion of DC-SIGN, a receptor with
unique pathogen recognition capabilities, reveals nonergodic subdiffusion on
living cell membranes. In contrast to previous studies, this behavior is
incompatible with transient immobilization and therefore it can not be
interpreted according to continuous time random walk theory. We show that the
receptor undergoes changes of diffusivity, consistent with the current view of
the cell membrane as a highly dynamic and diverse environment. Simulations
based on a model of ordinary random walk in complex media quantitatively
reproduce all our observations, pointing toward diffusion heterogeneity as the
cause of DC-SIGN behavior. By studying different receptor mutants, we further
correlate receptor motion to its molecular structure, thus establishing a
strong link between nonergodicity and biological function. These results
underscore the role of disorder in cell membranes and its connection with
function regulation. Due to its generality, our approach offers a framework to
interpret anomalous transport in other complex media where dynamic
heterogeneity might play a major role, such as those found, e.g., in soft
condensed matter, geology and ecology.Comment: 27 pages, 5 figure
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