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- Learning to Compare: Relation Network for Few-Shot Learning4,817
A conceptually simple, flexible, and general framework for few-shot learning, where a classifier must learn to recognise new classes given only few examples from each, which is easily extended to zero- shot learning.
- Fully-Convolutional Siamese Networks for Object Tracking4,457
A basic tracking algorithm is equipped with a novel fully-convolutional Siamese network trained end-to-end on the ILSVRC15 dataset for object detection in video and achieves state-of-the-art performance in multiple benchmarks.
- Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers3,852
This paper deploys a pure transformer to encode an image as a sequence of patches, termed SEgmentation TRansformer (SETR), and shows that SETR achieves new state of the art on ADE20K, Pascal Context, and competitive results on Cityscapes.
- Res2Net: A New Multi-Scale Backbone Architecture3,450
This paper proposes a novel building block for CNNs, namely Res2Net, by constructing hierarchical residual-like connections within one single residual block that represents multi-scale features at a granular level and increases the range of receptive fields for each network layer.
- Conditional Random Fields as Recurrent Neural Networks2,612
A new form of convolutional neural network that combines the strengths of Convolutional Neural Networks (CNNs) and Conditional Random Fields (CRFs)-based probabilistic graphical modelling is introduced, and top results are obtained on the challenging Pascal VOC 2012 segmentation benchmark.
- Point Transformer2,467
The Point Transformer design improves upon prior work across domains and tasks and crosses the 70% mIoU threshold for the first time on the challenging S3DIS dataset.
- 2,466
- Staple: Complementary Learners for Real-Time Tracking1,846
It is shown that a simple tracker combining complementary cues in a ridge regression framework can operate faster than 80 FPS and outperform not only all entries in the popular VOT14 competition, but also recent and far more sophisticated trackers according to multiple benchmarks.
- 1,838
- Fast Online Object Tracking and Segmentation: A Unifying Approach1,528
This method improves the offline training procedure of popular fully-convolutional Siamese approaches for object tracking by augmenting their loss with a binary segmentation task, and operates online, producing class-agnostic object segmentation masks and rotated bounding boxes at 55 frames per second.
- End-to-End Representation Learning for Correlation Filter Based Tracking1,469
This work is the first to overcome this limitation by interpreting the Correlation Filter learner, which has a closed-form solution, as a differentiable layer in a deep neural network, which enables learning deep features that are tightly coupled to the Cor correlation filter.
- Riemannian Walk for Incremental Learning: Understanding Forgetting and Intransigence1,459
This work introduces two metrics to quantify forgetting and intransigence that allow for better insights into the behaviour of IL algorithms and presents RWalk, a generalization of EWC++ and Path Integral with a theoretically grounded KL-divergence based perspective.
- 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.
- DESIRE: Distant Future Prediction in Dynamic Scenes with Interacting Agents1,131
The proposed Deep Stochastic IOC RNN Encoder-decoder framework, DESIRE, for the task of future predictions of multiple interacting agents in dynamic scenes significantly improves the prediction accuracy compared to other baseline methods.
- 1,085
- 1,021
- Hypergraph convolution and hypergraph attention952
This work introduces two end-to-end trainable operators to the family of graph neural networks, i.e., hypergraph convolution and hypergraph attention, which define the basic formulation of performing convolution on a hypergraph and further enhances the capacity of representation learning by leveraging an attention module.
- GA-Net: Guided Aggregation Net for End-To-End Stereo Matching838
Two novel neural net layers, aimed at capturing local and the whole-image cost dependencies respectively are proposed, which can be used to replace the widely used 3D convolutional layer which is computationally costly and memory-consuming as it has cubic computational/memory complexity.
- The Sixth Visual Object Tracking VOT2018 Challenge Results804
The Visual Object Tracking challenge VOT2018 is the sixth annual tracker benchmarking activity organized by the VOT initiative; results of over eighty trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years.
- 790
- The Visual Object Tracking VOT2016 Challenge Results776
The Visual Object Tracking challenge VOT2016 goes beyond its predecessors by introducing a new semi-automatic ground truth bounding box annotation methodology and extending the evaluation system with the no-reset experiment.
- The Development and Comparison of Robust Methods for Estimating the Fundamental Matrix775
A variety of robust methods for the computation of the Fundamental Matrix, the calibration-free representation of camera motion, are developed from the principal categories of robust estimators, viz. case deletion diagnostics, M-estimators and random sampling, and the theory required to apply them to non-linear orthogonal regression problems is developed.
- GDumb: A Simple Approach that Questions Our Progress in Continual Learning753
A general formulation for the Continual Learning problem for classification—a learning task where a stream provides samples to a learner and the goal of the learner, depending on the samples it receives, is to continually upgrade its knowledge about the old classes and learn new ones is discussed.
- 706
- Siam R-CNN: Visual Tracking by Re-Detection678
This work presents Siam R-CNN, a Siamese re-detection architecture which unleashes the full power of two-stage object detection approaches for visual object tracking, and combines this with a novel tracklet-based dynamic programming algorithm to model the full history of both the object to be tracked and potential distractor objects.
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 2026-10-12. 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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