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- Co-occurrence Feature Learning from Skeleton Data for Action Recognition and Detection with Hierarchical Aggregation588
This paper introduces a global spatial aggregation scheme, which is able to learn superior joint co-occurrence features over local aggregation, and consistently outperforms other state-of-the-arts on action recognition and detection benchmarks like NTU RGB+D, SBU Kinect Interaction and PKU-MMD.
- Focusing Attention: Towards Accurate Text Recognition in Natural Images583
The FAN (the abbreviation of Focusing Attention Network) method is proposed that employs a focusing attention mechanism to automatically draw back the drifted attention in scene text images and substantially outperforms the existing methods.
- Skeleton-based action recognition with convolutional neural networks421
A novel convolutional neural networks (CNN) based framework for both action classification and detection of skeleton-based action recognition and a window proposal network to extract temporal segment proposals, which are further classified within the same network.
- RPVNet: A Deep and Efficient Range-Point-Voxel Fusion Network for LiDAR Point Cloud Segmentation376
This work proposes a novel range-point-voxel fusion network, namely RPVNet, and devise a deep fusion framework with multiple and mutual information interactions among these three views, and proposes a gated fusion module (termed as GFM), which can adaptively merge the three features based on concurrent inputs.
- Dynamic GCN: Context-enriched Topology Learning for Skeleton-based Action Recognition362
This paper proposes Dynamic GCN, in which a novel convolutional neural network named Context-encoding Network (CeN) is introduced to learn skeleton topology automatically, and achieves state-of-the-art performance on three large-scale benchmarks, namely NTU-RGB+D, NTU+D 120 and Skeleton-Kinetics.
- Counterfactual Samples Synthesizing for Robust Visual Question Answering356
A model-agnostic Counterfactual Samples Synthesizing (CSS) training scheme that significantly improves both visual-explainable and question-sensitive abilities of VQA models and, in return, the performance of these models is further boosted.
- Advancing Image Understanding in Poor Visibility Environments: A Collective Benchmark Study351
The UG2+ challenge Track 2 competition in IEEE CVPR 2019 is launched, aiming to evoke a comprehensive discussion and exploration about whether and how low-level vision techniques can benefit the high-level automatic visual recognition in various scenarios.
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- Forward Compatible Few-Shot Class-Incremental Learning304
Virtual prototypes are assigned to squeeze the embedding of known classes and reserve for new classes, which allow the model to accept possible updates in the future and act as proxies scattered among embedding space to build a stronger classifier during inference.
- Learning Open Set Network with Discriminative Reciprocal Points294
A new concept, Reciprocal Point, which is the potential representation of the extra-class space corresponding to each known category, which can indirectly introduce the unknown information into the learner with only known classes, so as to learn more compact and discriminative representations.
- Divide-and-Assemble: Learning Block-wise Memory for Unsupervised Anomaly Detection220
By varying the granularity of division on feature maps, this work is able to modulate the reconstruction capability of the model for both normal and abnormal samples, and achieves state-of-the-art performance on the challenging MVTec AD dataset.
- Few-Shot Class-Incremental Learning by Sampling Multi-Phase Tasks211
This work proposes a new paradigm for FSCIL based on meta-learning by LearnIng Multi-phase Incremental Tasks (Limit), which synthesizes fake F SCIL tasks from the base dataset and constructs a calibration module based on transformer, which calibrates the old class classifiers and new class prototypes into the same scale and fills in the semantic gap.
- All You Need is Beyond a Good Init: Exploring Better Solution for Training Extremely Deep Convolutional Neural Networks with Orthonormality and Modulation198
A variant of regularizer which utilizes orthonormality among different filter banks can alleviate the problem of gradient vanishing or exploding phenomenon, and a backward error modulation mechanism based on the quasi-isometry assumption between two consecutive parametric layers is designed.
- Small-Scale Pedestrian Detection Based on Topological Line Localization and Temporal Feature Aggregation197
A novel method integrated with somatic topological line localization (TLL) and temporal feature aggregation for detecting multi-scale pedestrians and a post-processing scheme based on Markov Random Field (MRF) is introduced to eliminate ambiguities in occlusion cases.
- A Free Lunch for Unsupervised Domain Adaptive Object Detection without Source Data193
Self-entropy descent (SED) is a metric proposed to search an appropriate confidence threshold for reliable pseudo label generation without using any handcrafted labels in object detection, where completely clean labels are still unattainable.
- Edit Probability for Scene Text Recognition192
A novel method called edit probability (EP) for scene text recognition tries to effectively estimate the probability of generating a string from the output sequence of probability distribution conditioned on the input image, while considering the possible occurrences of missing/superfluous characters.
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- LoRAMoE: Alleviating World Knowledge Forgetting in Large Language Models via MoE-Style Plugin186
Experimental results show that, as the instruction data increases, Lo-RAMoE can significantly improve the ability to process downstream tasks, while maintaining the world knowledge stored in the LLM.
- Spatio-Temporal Deformable Convolution for Compressed Video Quality Enhancement180
This paper proposes a fast yet effective method for compressed video quality enhancement by incorporating a novel Spatio-Temporal Deformable Fusion (STDF) scheme to aggregate temporal information and achieves the state-of-the-art performance of compressed videoquality enhancement in terms of both accuracy and efficiency.
- Counterfactual Critic Multi-Agent Training for Scene Graph Generation170
CMAT is a multi-agent policy gradient method that frames objects into cooperative agents, and then directly maximizes a graph-level metric as the reward, and uses a counterfactual baseline that disentangles the agent-specific reward by fixing the predictions of other agents.
- Scene Dynamics: Counterfactual Critic Multi-Agent Training for Scene Graph Generation.167
CMAT is a multi-agent policy gradient method that frames objects into cooperative agents, and then directly maximizes a graph-level metric as the reward, and uses a counterfactual baseline that disentangles the agent-specific reward by fixing the predictions of other agents.
- Learning Domain Adaptive Object Detection with Probabilistic Teacher144
A simple yet effective framework, termed as Probabilistic Teacher (PT), which aims to capture the uncertainty of unlabeled target data from a gradually evolving teacher and guides the learning of a student in a mutually beneficial manner, and proposes to leverage the uncertainty-guided consistency training to promote classification adaptation and localization adaptation.
- Can Abnormality be Detected by Graph Neural Networks?137
A novel Adaptive Multi-frequency Graph Neural Network (AMNet), aiming to capture both low-frequency and high-frequency signals, and adaptively combine signals of different frequencies is proposed, comparing with several state-of-the-art baseline methods.
- Text Perceptron: Towards End-to-End Arbitrary-Shaped Text Spotting132
This paper proposes an end-to-end trainable text spotting approach named Text Perceptron, which unites text detection and the following recognition part into a whole framework, and helps the whole network achieve global optimization.
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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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