259 research outputs found
Multi-shot Pedestrian Re-identification via Sequential Decision Making
Multi-shot pedestrian re-identification problem is at the core of
surveillance video analysis. It matches two tracks of pedestrians from
different cameras. In contrary to existing works that aggregate single frames
features by time series model such as recurrent neural network, in this paper,
we propose an interpretable reinforcement learning based approach to this
problem. Particularly, we train an agent to verify a pair of images at each
time. The agent could choose to output the result (same or different) or
request another pair of images to verify (unsure). By this way, our model
implicitly learns the difficulty of image pairs, and postpone the decision when
the model does not accumulate enough evidence. Moreover, by adjusting the
reward for unsure action, we can easily trade off between speed and accuracy.
In three open benchmarks, our method are competitive with the state-of-the-art
methods while only using 3% to 6% images. These promising results demonstrate
that our method is favorable in both efficiency and performance
Dataset Distillation in Latent Space
Dataset distillation (DD) is a newly emerging research area aiming at
alleviating the heavy computational load in training models on large datasets.
It tries to distill a large dataset into a small and condensed one so that
models trained on the distilled dataset can perform comparably with those
trained on the full dataset when performing downstream tasks. Among the
previous works in this area, there are three key problems that hinder the
performance and availability of the existing DD methods: high time complexity,
high space complexity, and low info-compactness. In this work, we
simultaneously attempt to settle these three problems by moving the DD
processes from conventionally used pixel space to latent space. Encoded by a
pretrained generic autoencoder, latent codes in the latent space are naturally
info-compact representations of the original images in much smaller sizes.
After transferring three mainstream DD algorithms to latent space, we
significantly reduce time and space consumption while achieving similar
performance, allowing us to distill high-resolution datasets or target at
greater data ratio that previous methods have failed. Besides, within the same
storage budget, we can also quantitatively deliver more latent codes than
pixel-level images, which further boosts the performance of our methods.Comment: Under revie
Influence of time-dependence on failure of echelon rock joints through a novel DEM model
This is an Accepted Manuscript of an article published by Taylor & Francis Group in [European Journal of Environmental and Civil Engineering] on [September 2015], available online at: http://www.tandfonline.com/10.1080/19648189.2015.1064624This article investigates the time-dependent influence on the shear failure behaviour of parallel rock joints in the echelon arrangement due to chemical weathering, which can be treated as a generalised time-dependency of the rock material. A time-dependent parameter alpha, identifying the accumulated relative mass removal of bonding material, has been implemented into a novel distinct element method bond contact model. This model is based on a series of mechanical test on bonded aluminium rods with different bond geometries. The numerical direct shear test results of echelon rock joints characterised by different values of alpha show that increasing time-dependent parameter alpha can lead to a lower crack initiation and peak stresses. This is accompanied by a growing ratio of the microscopic compressive-shear-torsional (CST) bond failure number of bond failures to the total number of failures, except for the case without weathering influence. High values of alpha render the material bridge a weaker part to be cut through, generating a large number of CST bond breakages along the central shear axis.Peer ReviewedPostprint (author's final draft
Anti-inflammatory and analgesic effects of Yaotuitong Capsules in experimental rats with chemically induced radicular neuritis
AbstractObjectiveThe aims of this study were to determine the anti-inflammatory and analgesic effects of Yaotuitong (translation: low back and leg pain) capsules, a Chinese herbal preparation, and the histological changes it induces in experimental rats with chemically induced radicular neuritis.MethodsWistar rats were randomly divided into normal, model, Western medicine, and traditional Chinese medicine groups (n=24 per group). We surgically duplicated a chemical radicular neuritis model to simulate lumbar intervertebral disc protrusion. Granuloma formation was measured on postoperative days (PODs) 3, 7, 14, and 21. Prostaglandin E2 and 5-hydroxytryptamine (inflammation mediators) levels in the surrounding tissue and the histology of the nerve root were determined on PODs 7 and 14.ResultsYaotuitong capsules significantly reduced prostaglandin E2 (P<0.01) and 5-hydroxytryptamine (P<0.01) levels in tissue surrounding the nerve root. It also inhibited granuloma formation (P<0.05).ConclusionYaotuitong capsules have anti-inflammatory and analgesic effects that can alleviate the discomfort of lumbar intervertebral disc protrusion
Few-shot Image Generation Using Discrete Content Representation
Few-shot image generation and few-shot image translation are two related
tasks, both of which aim to generate new images for an unseen category with
only a few images. In this work, we make the first attempt to adapt few-shot
image translation method to few-shot image generation task. Few-shot image
translation disentangles an image into style vector and content map. An unseen
style vector can be combined with different seen content maps to produce
different images. However, it needs to store seen images to provide content
maps and the unseen style vector may be incompatible with seen content maps. To
adapt it to few-shot image generation task, we learn a compact dictionary of
local content vectors via quantizing continuous content maps into discrete
content maps instead of storing seen images. Furthermore, we model the
autoregressive distribution of discrete content map conditioned on style
vector, which can alleviate the incompatibility between content map and style
vector. Qualitative and quantitative results on three real datasets demonstrate
that our model can produce images of higher diversity and fidelity for unseen
categories than previous methods.Comment: This paper is accepted by ACM MM 202
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