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Works266 from public data
- Aggregated Residual Transformations for Deep Neural Networks11,971
On the ImageNet-1K dataset, it is empirically show that even under the restricted condition of maintaining complexity, increasing cardinality is able to improve classification accuracy and is more effective than going deeper or wider when the authors increase the capacity.
- Holistically-Nested Edge Detection3,991
HED performs image-to-image prediction by means of a deep learning model that leverages fully convolutional neural networks and deeply-supervised nets, and automatically learns rich hierarchical representations that are important in order to resolve the challenging ambiguity in edge and object boundary detection.
- Deeplysupervised nets2,565
The proposed deeply-supervised nets (DSN) method simultaneously minimizes classification error while making the learning process of hidden layers direct and transparent, and extends techniques from stochastic gradient methods to analyze the algorithm.
- Similarity network fusion for aggregating data types on a genomic scale2,022
Similarity network fusion substantially outperforms single data type analysis and established integrative approaches when identifying cancer subtypes and is effective for predicting survival.
- Rethinking Spatiotemporal Feature Learning: Speed-Accuracy Trade-offs in Video Classification1,527
It is shown that it is possible to replace many of the 3D convolutions by low-cost 2D convolution, suggesting that temporal representation learning on high-level “semantic” features is more useful.
- Deeply Supervised Salient Object Detection with Short Connections1,458
This paper proposes a new salient object detection method by introducing short connections to the skip-layer structures within the HED architecture, which takes full advantage of multi-level and multi-scale features extracted from FCNs, providing more advanced representations at each layer, a property that is critically needed to perform segment detection.
- Integral Channel Features1,350
It is demonstrated that when designed properly, integral channel features not only outperform other features including histogram of oriented gradient (HOG), they also result in fast detectors when coupled with cascade classifiers.
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- Robust Point Matching via Vector Field Consensus566
This paper proposes an efficient algorithm, called vector field consensus, for establishing robust point correspondences between two sets of points, and suggests a two-stage strategy, where the nonparametric model is used to reduce the size of the putative set and a parametric variant of the approach to estimate the geometric parameters.
- Image segmentation by data-driven markov chain monte carlo565
The DDMCMC paradigm provides a unifying framework in which the role of many existing segmentation algorithms are revealed as either realizing Markov chain dynamics or computing importance proposal probabilities and generalizes these segmentation methods in a principled way.
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- Co-Scale Conv-Attentional Image Transformers481
Co-scale conv-attentional image Transformers (CoaT), a Transformer-based image classifier equipped with co-scale and convolution-like mechanisms, empowers image Transformers with enriched multi- scale and contextual modeling capabilities.
- Cluster-Based Co-Saliency Detection427
This paper introduces a new cluster-based algorithm for co-saliency detection that is mostly bottom-up without heavy learning, and outperforms most the state-of-the-art saliency detection methods.
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- Weakly supervised histopathology cancer image segmentation and classification313
This paper embeds the clustering concept into the multiple instance learning (MIL) setting and derive a principled solution to performing the above three tasks in an integrated framework and introduces contextual constraints as a prior for MCIL, which further reduces the ambiguity in MIL.
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- Pose Recognition with Cascade Transformers292
This paper utilizes the encoder-decoder structure in Transformers to perform regression-based person and keypoint detection that is general-purpose and requires less heuristic design compared with the existing approaches.
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- MeMOT: Multi-Object Tracking with Memory275
An online tracking algorithm that performs the object detection and data association under a common framework, capable of linking objects after a long time span is proposed, by preserving a large spatio-temporal memory to store the identity embeddings of the tracked objects.
- Rethinking Spatiotemporal Feature Learning For Video Understanding.272
Interestingly, it was found that 3D convolutions at the top layers of the network contribute more than 3D Convolutional networks at the bottom layers, while also being computationally more efficient, indicating that I3D is better at capturing high-level temporal patterns than low-level motion signals.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-14. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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