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- A Survey on Vision Transformer4,578
This paper reviews these vision transformer models by categorizing them in different tasks and analyzing their advantages and disadvantages, and takes a brief look at the self-attention mechanism in computer vision, as it is the base component in transformer.
- CMT: Convolutional Neural Networks Meet Vision Transformers992
A new transformer based hybrid network is proposed by taking advantage of transformers to capture long-range dependencies, and of CNNs to extract local information, obtaining much better trade-off for accuracy and efficiency than previous CNN-based and transformer-based models.
- Multimodal Token Fusion for Vision Transformers327
The design of TokenFusion allows the transformer to learn correlations among multimodal features, while the single-modal transformer architecture remains largely intact, and surpasses state-of-the-art methods in three typical vision tasks: multimodals image-to-image translation, RGB-depth semantic segmentation, and 3D object detection with point cloud and images.
- Distilling Object Detectors via Decoupled Features278
This paper presents a novel distillation algorithm via decoupled features (DeFeat) for learning a better student detector that is able to surpass the state-of-the-art distillation methods for object detection.
- CARS: Continuous Evolution for Efficient Neural Architecture Search275
This work develops an efficient continuous evolutionary approach for searching neural networks that provides a series of networks with the number of parameters ranging from 3.7M to 5.1M under mobile settings and surpasses those produced by the state-of-the-art methods on the benchmark ImageNet dataset.
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- Pose guided structured region ensemble network for cascaded hand pose estimation208
A novel approach named as pose guided structured region ensemble network (Pose-REN), which extracts regions from the feature maps of convolutional neural network and generates more optimal and representative features for hand pose estimation.
- 3D Hand Pose Estimation: From Current Achievements to Future Goals194
This paper investigates 11 state-of-the-art methods on three tasks: single frame 3D pose estimation, 3D hand tracking, and hand pose estimation during object interaction.
- Spatial-Temporal Attention Res-TCN for Skeleton-Based Dynamic Hand Gesture Recognition149
An end-to-end Spatial-Temporal Attention Residual Temporal Convolutional Network (STA-Res-TCN) for skeleton-based dynamic hand gesture recognition, which learns different levels of attention and assigns them to each spatial-temporal feature extracted by the convolution filters at each time step.
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- Hire-MLP: Vision MLP via Hierarchical Rearrangement128
Hire-MLP is presented, a simple yet competitive vision MLP architecture via Hierarchical rearrangement, which contains two levels of rearrangements to enable information communication between different regions and capture global context by circularly shifting all tokens along spatial directions.
- Hit-Detector: Hierarchical Trinity Architecture Search for Object Detection110
This work proposes a hierarchical trinity search framework to simultaneously discover efficient architectures for all components of object detector in an end-to-end manner and empirically reveals that different parts of the detector prefer different operators.
- SHPR-Net: Deep Semantic Hand Pose Regression From Point Clouds110
A deep semantic hand pose regression network (SHPR-Net) for hand pose estimation from point sets, which consists of two subnetworks: a semantic segmentation subnetwork and a hand poses regression subnetwork, which is more robust to geometric transformations.
- Region ensemble network: Towards good practices for deep 3D hand pose estimation107
A Region Ensemble Network for directly 3D coordinate regression that significantly improves the performance of ConvNet for hand pose estimation and achieves strong performance on par or better than state-of-the-art algorithms on three public hand pose datasets.
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- MFA-Net: Motion Feature Augmented Network for Dynamic Hand Gesture Recognition from Skeletal Data85
A novel motion feature augmented network (MFA-Net) is proposed for dynamic hand gesture recognition from skeletal data that exploits motion features of finger and global movements to augment features of deep network for gesture recognition.
- GeminiFusion: Efficient Pixel-wise Multimodal Fusion for Vision Transformer82
This paper first critiques prior token exchange methods which replace less informative tokens with inter-modal features, and demonstrates exchange based methods underperform cross-attention mechanisms, while the computational demand of the latter inevitably restricts its use with longer sequences.
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- Pangu Embedded: An Efficient Dual-system LLM Reasoner with Metacognition68
Experimental results on benchmarks including AIME 2024, GPQA, and LiveCodeBench demonstrate that Pangu Embedded with 7B parameters, outperforms similar-size models like Qwen3-8B and GLM4-9B.
- PPT: Token Pruning and Pooling for Efficient Vision Transformers52
This paper proposes a novel acceleration framework, namely token Pruning&Pooling Transformers (PPT), to adaptively tackle these two types of redundancy in different layers of ViTs by heuristically integrating both token pruning and token pooling techniques in ViTs without additional trainable parameters.
- SLAB: Efficient Transformers with Simplified Linear Attention and Progressive Re-parameterized Batch Normalization51
A novel method named PRepBN is proposed to progressively replace LayerNorm with re-parameterized BatchNorm in training and a simplified linear attention (SLA) module is proposed that is simple yet effective to achieve strong performance.
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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 2026-10-14. 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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