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- CenterNet: Keypoint Triplets for Object Detection3,591
This paper presents an efficient solution that explores the visual patterns within individual cropped regions with minimal costs, and builds the framework upon a representative one-stage keypoint-based detector named CornerNet, which improves both precision and recall.
- CenterNet: Object Detection with Keypoint Triplets3,563
This paper presents an efficient solution that explores the visual patterns within individual cropped regions with minimal costs, and builds the framework upon a representative one-stage keypoint-based detector named CornerNet, which improves both precision and recall.
- Improving Transferability of Adversarial Examples With Input Diversity1,524
This work proposes to improve the transferability of adversarial examples by creating diverse input patterns by applying random transformations to the input images at each iteration, and shows that the proposed attack method can generate adversarialExamples that transfer much better to different networks than existing baselines.
- 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.
- Asymmetric Non-Local Neural Networks for Semantic Segmentation712
Asymmetric Pyramid Non- local Block (APNB) and Asymmetric Fusion Non-local Block (AFNB) are presented, which has two prominent components: APNB leverages a pyramid sampling module into the non-local block to largely reduce the computation and memory consumption without sacrificing the performance.
- DanceTrack: Multi-Object Tracking in Uniform Appearance and Diverse Motion467
A large-scale dataset for multi-human tracking, where humans have sim-ilar appearance, diverse motion and extreme articulation, and is named “DanceTrack”, which is expected to provide a better platform to develop more MOT algorithms that rely less on visual discrimination and depend more on motion analysis.
- Is synthetic data from generative models ready for image recognition?443
This work extensively study whether and how synthetic images generated from state-of-the-art text-to-image generation models can be used for image recognition tasks, and focuses on two perspectives: synthetic data for improving classification models in data-scarce settings (i.e. zero-shot and few-shot), and syntheticData for large-scale model pre-training for transfer learning.
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- PCL: Proposal Cluster Learning for Weakly Supervised Object Detection422
This paper first shows that instances can be assigned object or background labels directly based on proposal clusters for instance classifier refinement, and then shows that treating each cluster as a small new bag yields fewer ambiguities than the directly assigning label method.
- DragDiffusion: Harnessing Diffusion Models for Interactive Point-Based Image Editing393
This work introduces two additional techniques, namely identity-preserving fine-tuning and reference-latent-control, to further preserve the identity of the original image and presents a challenging benchmark dataset called DRAGBENCH─ the first benchmark to evaluate the performance of interactive point-based image editing methods.
- Triplet-Center Loss for Multi-view 3D Object Retrieval385
This paper study variants of deep metric learning losses for 3D object retrieval, which did not receive enough attention from this area, and proposes a novel loss named triplet-center loss, which can further enhance the discriminative power of the features.
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- Scalable Person Re-identification on Supervised Smoothed Manifold342
An unconventional manifold-preserving algorithm is proposed, which can make best use of supervision from training data, whose label information is given as pairwise constraints, scale up to large repositories with low on-line time complexity, and be plunged into most existing algorithms, serving as a generic postprocessing procedure to further boost the identification accuracies.
- End-to-End Temporal Action Detection With Transformer341
TadTR is an end-to-end Transformer-based method for temporal action detection that achieves state-of-the-art performance on THUMOS14 and HACS Segments, and requires lower computation cost than previous detectors, while preserving remarkable performance.
- MOSE: A New Dataset for Video Object Segmentation in Complex Scenes307
The experiments show that current VOS algorithms cannot well perceive objects in complex scenes, and there are unresolved challenges under complex scenes and more efforts are desired to explore these challenges in the future.
- GIFT: A Real-Time and Scalable 3D Shape Search Engine298
The proposed 3D shape search engine, which combines GPU acceleration and Inverted File (Twice), is named as GIFT, which outperforms the state-of-the-art methods significantly in retrieval accuracy on various shape benchmarks and competitions.
- PLA: Language-Driven Open-Vocabulary 3D Scene Understanding240
This work proposes to distill knowledge encoded in pretrained vision-language (VL) foundation models through captioning multi-view images from 3D, which allows explicitly associating 3D and semantic-rich captions in open-vocabulary captions.
- Occluded Video Instance Segmentation: A Benchmark229
A simple plug-and-play module that performs temporal feature calibration to complement missing object cues caused by occlusion is presented, and a remarkable AP improvement on the OVIS dataset is obtained.
- Prior-Aware Neural Network for Partially-Supervised Multi-Organ Segmentation204
PaNN achieves state-of-the-art performance on the MICCAI2015 challenge ``Multi-Atlas Labeling Beyond the Cranial Vault'', a competition on organ segmentation in the abdomen, and is reformulated as a min-max form and optimized via the stochastic primal-dual gradient algorithm.
- XingGAN for Person Image Generation197
A novel Generative Adversarial Network (XingGAN or CrossingGAN) for person image generation tasks, i.e., translating the pose of a given person to a desired one to mutually improve each other is proposed.
- SwiftNet: Real-time Video Object Segmentation190
This work presents SwiftNet for real-time semisupervised video object segmentation (one-shot VOS), which reports 77.8% $\mathcal{J}\& \mathcal {F}$ and 70 FPS on DAVIS 2017 validation dataset, leading all present solutions in overall accuracy and speed performance.
- Deep Learning Representation using Autoencoder for 3D Shape Retrieval187
This work projects 3D shapes into 2D space and uses autoencoder for feature learning on the 2D images and shows the proposed deep learning feature is complementary to conventional local image descriptors, which can obtain the state-of-the-art performance on 3D shape retrieval benchmarks.
- Learning Transferable Adversarial Examples via Ghost Networks171
Ghost Networks is proposed to improve the transferability of adversarial examples by reproducing the NeurIPS 2017 adversarial competition, and outperforms the No.1 attack submission by a large margin, demonstrating its effectiveness and efficiency.
- Learning Attraction Field Representation for Robust Line Segment Detection169
A region-partition based attraction field dual representation for line segment maps, which poses the problem of line segment detection (LSD) as the region coloring problem and harnesses the best practices developed in ConvNets based semantic segmentation methods such as the encoder-decoder architecture and the a-trous convolution.
- Symmetry-Constrained Rectification Network for Scene Text Recognition166
A Symmetry-constrained Rectification Network (ScRN) based on local attributes of text instances, such as center line, scale and orientation, which enables ScRN to generate better rectification results than existing methods and thus lead to higher recognition accuracy.
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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