110 research outputs found
Exemplar-based Linear Discriminant Analysis for Robust Object Tracking
Tracking-by-detection has become an attractive tracking technique, which
treats tracking as a category detection problem. However, the task in tracking
is to search for a specific object, rather than an object category as in
detection. In this paper, we propose a novel tracking framework based on
exemplar detector rather than category detector. The proposed tracker is an
ensemble of exemplar-based linear discriminant analysis (ELDA) detectors. Each
detector is quite specific and discriminative, because it is trained by a
single object instance and massive negatives. To improve its adaptivity, we
update both object and background models. Experimental results on several
challenging video sequences demonstrate the effectiveness and robustness of our
tracking algorithm.Comment: ICIP201
Disentangling Spatial and Temporal Learning for Efficient Image-to-Video Transfer Learning
Recently, large-scale pre-trained language-image models like CLIP have shown
extraordinary capabilities for understanding spatial contents, but naively
transferring such models to video recognition still suffers from unsatisfactory
temporal modeling capabilities. Existing methods insert tunable structures into
or in parallel with the pre-trained model, which either requires
back-propagation through the whole pre-trained model and is thus
resource-demanding, or is limited by the temporal reasoning capability of the
pre-trained structure. In this work, we present DiST, which disentangles the
learning of spatial and temporal aspects of videos. Specifically, DiST uses a
dual-encoder structure, where a pre-trained foundation model acts as the
spatial encoder, and a lightweight network is introduced as the temporal
encoder. An integration branch is inserted between the encoders to fuse
spatio-temporal information. The disentangled spatial and temporal learning in
DiST is highly efficient because it avoids the back-propagation of massive
pre-trained parameters. Meanwhile, we empirically show that disentangled
learning with an extra network for integration benefits both spatial and
temporal understanding. Extensive experiments on five benchmarks show that DiST
delivers better performance than existing state-of-the-art methods by
convincing gaps. When pre-training on the large-scale Kinetics-710, we achieve
89.7% on Kinetics-400 with a frozen ViT-L model, which verifies the scalability
of DiST. Codes and models can be found in
https://github.com/alibaba-mmai-research/DiST.Comment: ICCV2023. Code: https://github.com/alibaba-mmai-research/DiS
Improving Fairness for Data Valuation in Horizontal Federated Learning
Federated learning is an emerging decentralized machine learning scheme that
allows multiple data owners to work collaboratively while ensuring data
privacy. The success of federated learning depends largely on the participation
of data owners. To sustain and encourage data owners' participation, it is
crucial to fairly evaluate the quality of the data provided by the data owners
and reward them correspondingly. Federated Shapley value, recently proposed by
Wang et al. [Federated Learning, 2020], is a measure for data value under the
framework of federated learning that satisfies many desired properties for data
valuation. However, there are still factors of potential unfairness in the
design of federated Shapley value because two data owners with the same local
data may not receive the same evaluation. We propose a new measure called
completed federated Shapley value to improve the fairness of federated Shapley
value. The design depends on completing a matrix consisting of all the possible
contributions by different subsets of the data owners. It is shown under mild
conditions that this matrix is approximately low-rank by leveraging concepts
and tools from optimization. Both theoretical analysis and empirical evaluation
verify that the proposed measure does improve fairness in many circumstances
Phage combination alleviates bacterial leaf blight of rice (Oryza sativa L.)
Rice bacterial leaf blight (BLB) is the most destructive bacterial diseases caused by Xanthomonas oryzae pv. oryzae (Xoo). Phages have been proposed as a green and efficient strategy to kill bacterial pathogens in crops, however, the mechanism of action of phages in the control of phyllosphere bacterial diseases remain unclear. Here, the glasshouse pot experiment results showed that phage combination could reduce the disease index by up to 64.3%. High-throughput sequencing technology was used to analyze the characteristics of phyllosphere microbiome changes and the results showed that phage combinations restored the impact of pathogen invasion on phyllosphere communities to a certain extent, and increased the diversity of bacterial communities. In addition, the phage combination reduced the relative abundance of epiphytic and endophytic Xoo by 58.9% and 33.9%, respectively. In particular, Sphingomonas and Stenotrophomonas were more abundant. According to structural equation modeling, phage combination directly and indirectly affected the disease index by affecting pathogen Xoo biomass and phage resistance. In summary, phage combination could better decrease the disease index. These findings provide new insights into phage biological control of phyllosphere bacterial diseases, theoretical data support, and new ideas for agricultural green prevention and control of phyllosphere diseases
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Through-skull fluorescence imaging of the brain in a new near-infrared window
To date, brain imaging has largely relied on X-ray computed tomography and magnetic resonance angiography with limited spatial resolution and long scanning times. Fluorescence-based brain imaging in the visible and traditional near-infrared regions (400–900 nm) is an alternative but currently requires craniotomy, cranial windows and skull thinning techniques, and the penetration depth is limited to 1–2 mm due to light scattering. Here, we report through-scalp and through-skull fluorescence imaging of mouse cerebral vasculature without craniotomy utilizing the intrinsic photoluminescence of single-walled carbon nanotubes in the 1.3–1.4 micrometre near-infrared window. Reduced photon scattering in this spectral region allows fluorescence imaging reaching a depth of >2 mm in mouse brain with sub-10 micrometre resolution. An imaging rate of ~5.3 frames/s allows for dynamic recording of blood perfusion in the cerebral vessels with sufficient temporal resolution, providing real-time assessment of blood flow anomaly in a mouse middle cerebral artery occlusion stroke model
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