382 research outputs found
Tris(propionitrile-κN)[1,4,7-tris(cyanomethyl)-1,4,7-triazacyclononane-κ3 N 1,N 4,N 7]copper(II) bis(perchlorate) dihydrate
In the title compound, [Cu(C3H5N)3(C12H18N6)](ClO4)2·2H2O, the CuII atom lies on a threefold rotation axis and is coordinated in a distorted N6 octahedral environment by three tertiary amines from the tridentate chelating azamacrocyclic ligand and three propionitrile molecules. Intermolecular non-classical C—H⋯N hydrogen bonding interlinks the [Cu(C3H5N)3(C12H18N6)]2+ cations into a two-dimensional supramolecular sheet extending along the ab plane. The crystal packing also exhibits weak C—H⋯O interactions
ECL: Class-Enhancement Contrastive Learning for Long-tailed Skin Lesion Classification
Skin image datasets often suffer from imbalanced data distribution,
exacerbating the difficulty of computer-aided skin disease diagnosis. Some
recent works exploit supervised contrastive learning (SCL) for this long-tailed
challenge. Despite achieving significant performance, these SCL-based methods
focus more on head classes, yet ignoring the utilization of information in tail
classes. In this paper, we propose class-Enhancement Contrastive Learning
(ECL), which enriches the information of minority classes and treats different
classes equally. For information enhancement, we design a hybrid-proxy model to
generate class-dependent proxies and propose a cycle update strategy for
parameters optimization. A balanced-hybrid-proxy loss is designed to exploit
relations between samples and proxies with different classes treated equally.
Taking both "imbalanced data" and "imbalanced diagnosis difficulty" into
account, we further present a balanced-weighted cross-entropy loss following
curriculum learning schedule. Experimental results on the classification of
imbalanced skin lesion data have demonstrated the superiority and effectiveness
of our method
TFormer: A throughout fusion transformer for multi-modal skin lesion diagnosis
Multi-modal skin lesion diagnosis (MSLD) has achieved remarkable success by
modern computer-aided diagnosis technology based on deep convolutions. However,
the information aggregation across modalities in MSLD remains challenging due
to severity unaligned spatial resolution (dermoscopic image and clinical image)
and heterogeneous data (dermoscopic image and patients' meta-data). Limited by
the intrinsic local attention, most recent MSLD pipelines using pure
convolutions struggle to capture representative features in shallow layers,
thus the fusion across different modalities is usually done at the end of the
pipelines, even at the last layer, leading to an insufficient information
aggregation. To tackle the issue, we introduce a pure transformer-based method,
which we refer to as ``Throughout Fusion Transformer (TFormer)", for sufficient
information intergration in MSLD. Different from the existing approaches with
convolutions, the proposed network leverages transformer as feature extraction
backbone, bringing more representative shallow features. We then carefully
design a stack of dual-branch hierarchical multi-modal transformer (HMT) blocks
to fuse information across different image modalities in a stage-by-stage way.
With the aggregated information of image modalities, a multi-modal transformer
post-fusion (MTP) block is designed to integrate features across image and
non-image data. Such a strategy that information of the image modalities is
firstly fused then the heterogeneous ones enables us to better divide and
conquer the two major challenges while ensuring inter-modality dynamics are
effectively modeled. Experiments conducted on the public Derm7pt dataset
validate the superiority of the proposed method. Our TFormer outperforms other
state-of-the-art methods. Ablation experiments also suggest the effectiveness
of our designs
RTrack: Accelerating Convergence for Visual Object Tracking via Pseudo-Boxes Exploration
Single object tracking (SOT) heavily relies on the representation of the
target object as a bounding box. However, due to the potential deformation and
rotation experienced by the tracked targets, the genuine bounding box fails to
capture the appearance information explicitly and introduces cluttered
background. This paper proposes RTrack, a novel object representation baseline
tracker that utilizes a set of sample points to get a pseudo bounding box.
RTrack automatically arranges these points to define the spatial extents and
highlight local areas. Building upon the baseline, we conducted an in-depth
exploration of the training potential and introduced a one-to-many leading
assignment strategy. It is worth noting that our approach achieves competitive
performance to the state-of-the-art trackers on the GOT-10k dataset while
reducing training time to just 10% of the previous state-of-the-art (SOTA)
trackers' training costs. The substantial reduction in training costs brings
single-object tracking (SOT) closer to the object detection (OD) task.
Extensive experiments demonstrate that our proposed RTrack achieves SOTA
results with faster convergence
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