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Found 14 papers, 8 papers with code
Crowd Counting With Deep Negative Correlation Learning
Deep convolutional networks (ConvNets) have achieved unprecedented performances on many computer vision tasks.
Ranked #9 on
Crowd Counting
on WorldExpo’10
Evaluation of Algorithms for Multi-Modality Whole Heart Segmentation: An Open-Access Grand Challenge
This manuscript presents the methodologies and evaluation results for the WHS algorithms selected from the submissions to the Multi-Modality Whole Heart Segmentation (MM-WHS) challenge, in conjunction with MICCAI 2017.
Counting with Focus for Free
To assist both the density estimation and the focus from segmentation, we also introduce an improved kernel size estimator for the point annotations.
Robust Regression via Deep Negative Correlation Learning
Nonlinear regression has been extensively employed in many computer vision problems (e. g., crowd counting, age estimation, affective computing).
Ordered or Orderless: A Revisit for Video based Person Re-Identification
Specifically, with a diagnostic analysis, we show that the recurrent structure may not be effective to learn temporal dependencies than what we expected and implicitly yields an orderless representation.
Unsharp Mask Guided Filtering
The state-of-the-art leverages deep networks to estimate the two core coefficients of the guided filter.
On Measuring and Controlling the Spectral Bias of the Deep Image Prior
The deep image prior showed that a randomly initialized network with a suitable architecture can be trained to solve inverse imaging problems by simply optimizing it's parameters to reconstruct a single degraded image.
Frequency-Supervised MR-to-CT Image Synthesis
In this paper, we find that all existing approaches share a common limitation: reconstruction breaks down in and around the high-frequency parts of CT images.
Social Fabric: Tubelet Compositions for Video Relation Detection
We also propose Social Fabric: an encoding that represents a pair of object tubelets as a composition of interaction primitives.
Ranked #1 on
Video Visual Relation Detection
on VidOR
The Role of Robust Generalization in Continual Learning: Better Transfer and Less Forgetting
To the best of our knowledge, no existing technique can accomplish all of these objectives simultaneously.
Reason from Context with Self-supervised Learning
To better accommodate the object-centric nature of current downstream tasks such as object recognition and detection, various methods have been proposed to suppress contextual biases or disentangle objects from contexts.
Focus for Free in Density-Based Counting
Where density-based counting methods typically use the point annotations only to create Gaussian-density maps, which act as the supervision signal, the starting point of this work is that point annotations have counting potential beyond density map generation.
Training-free Object Counting with Prompts
However, the vanilla mask generation method of SAM lacks class-specific information in the masks, resulting in inferior counting accuracy.
Ranked #1 on
Object Counting
on PASCAL VOC
DualMatch: Robust Semi-Supervised Learning with Dual-Level Interaction
In this paper, we propose a novel SSL method called DualMatch, in which the class prediction jointly invokes feature embedding in a dual-level interaction manner.
