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Found 15 papers, 5 papers with code
VastTrack: Vast Category Visual Object Tracking
The rich annotations of VastTrack enables development of both the vision-only and the vision-language tracking.
Stereographic Spherical Sliced Wasserstein Distances
Comparing spherical probability distributions is of great interest in various fields, including geology, medical domains, computer vision, and deep representation learning.
A Comparative Analysis of the COVID-19 Infodemic in English and Chinese: Insights from Social Media Textual Data
This paper presents a comparative analysis of the COVID-19 infodemic in the English and Chinese languages, utilizing textual data extracted from social media platforms.
LCOT: Linear circular optimal transport
The optimal transport problem for measures supported on non-Euclidean spaces has recently gained ample interest in diverse applications involving representation learning.
Towards General Low-Light Raw Noise Synthesis and Modeling
To address this issue, we introduce a new perspective to synthesize the signal-independent noise by a generative model.
Ranked #2 on
Image Denoising
on SID SonyA7S2 x300
PT$\mathrm{L}^{p}$: Partial Transport $\mathrm{L}^{p}$ Distances
In this paper, we introduce partial transport $\mathrm{L}^{p}$ distances as a new family of metrics for comparing generic signals, benefiting from the robustness of partial transport distances.
Equivariant vs. Invariant Layers: A Comparison of Backbone and Pooling for Point Cloud Classification
Our findings reveal that: 1) complex pooling methods, such as transport-based or attention-based poolings, can significantly boost the performance of simple backbones, but the benefits diminish for more complex backbones, 2) even complex backbones can benefit from pooling layers in low data scenarios, 3) surprisingly, the choice of pooling layers can have a more significant impact on the model's performance than adjusting the width and depth of the backbone, and 4) pairwise combination of pooling layers can significantly improve the performance of a fixed backbone.
Sharing Lifelong Reinforcement Learning Knowledge via Modulating Masks
The key idea is that the isolation of specific task knowledge to specific masks allows agents to transfer only specific knowledge on-demand, resulting in robust and effective distributed lifelong learning.
PlanarTrack: A Large-scale Challenging Benchmark for Planar Object Tracking
In addition, we further derive a variant named PlanarTrack$_{\mathbf{BB}}$ for generic object tracking from PlanarTrack.
Wasserstein Task Embedding for Measuring Task Similarities
Lastly, we leverage the 2-Wasserstein embedding framework to embed tasks into a vector space in which the Euclidean distance between the embedded points approximates the proposed 2-Wasserstein distance between tasks.
Teaching Networks to Solve Optimization Problems
In addition, we propose two alternative approaches for learning such parametric functions, with and without a solver in the LOOP.
DDU-Net: Dual-Decoder-U-Net for Road Extraction Using High-Resolution Remote Sensing Images
Extracting roads from high-resolution remote sensing images (HRSIs) is vital in a wide variety of applications, such as autonomous driving, path planning, and road navigation.
SLOSH: Set LOcality Sensitive Hashing via Sliced-Wasserstein Embeddings
This paper focuses on non-parametric and data-independent learning from set-structured data using approximate nearest neighbor (ANN) solutions, particularly locality-sensitive hashing.
Deep Scene Text Detection with Connected Component Proposals
The second task aims to output a bundle of character candidates used later to verify the word proposals.
Generating Binary Tags for Fast Medical Image Retrieval Based on Convolutional Nets and Radon Transform
The present work introduces a new image retrieval method for medical applications that employs a convolutional neural network (CNN) with recently introduced Radon barcodes.
