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- Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet2,580
A new Tokens-To-Token Vision Transformer (T2T-VTT), which incorporates an efficient backbone with a deep-narrow structure for vision transformer motivated by CNN architecture design after empirical study and reduces the parameter count and MACs of vanilla ViT by half.
- Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks With Octave Convolution734
This work proposes to factorize the mixed feature maps by their frequencies, and design a novel Octave Convolution (OctConv) operation to store and process feature maps that vary spatially “slower” at a lower spatial resolution reducing both memory and computation cost.
- STC: A Simple to Complex Framework for Weakly-Supervised Semantic Segmentation588
A simple to complex (STC) framework in which only image-level annotations are utilized to learn DCNNs for semantic segmentation, which demonstrates the superiority of the proposed STC framework compared with other state-of-the-arts frameworks.
- Graph-Based Global Reasoning Networks512
A new approach for reasoning globally in which a set of features are globally aggregated over the coordinate space and then projected to an interaction space where relational reasoning can be efficiently computed.
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- Rethinking Bottleneck Structure for Efficient Mobile Network Design275
This paper proposes to flip the structure and present a novel bottleneck design, called the sandglass block, that performs identity mapping and spatial transformation at higher dimensions and thus alleviates information loss and gradient confusion effectively and adds it into the search space of neural architecture search method DARTS.
- Multi-fiber Networks for Video Recognition235
This paper presents the novel Multi-Fiber architecture that slices a complex neural network into an ensemble of lightweight networks or fibers that run through the network, and incorporates multiplexer modules to facilitate information flow between fibers.
- Highly Efficient Salient Object Detection with 100K Parameters220
This paper proposes a flexible convolutional module, namely generalized OctConv (gOctConv), to efficiently utilize both in-stage and cross-stages multi-scale features, while reducing the representation redundancy by a novel dynamic weight decay scheme.
- PnP-DETR: Towards Efficient Visual Analysis with Transformers129
This work encapsulates the idea of reducing spatial redundancy into a novel poll and pool (PnP) sampling module, with which it is built an end-to-end PnP-DETR architecture that adaptively allocates its computation spatially to be more efficient.
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- Video Scene Parsing with Predictive Feature Learning117
It is experimentally proved that the learned predictive features in the model are able to significantly enhance the video parsing performance by combining with the standard image parsing network.
- AdversarialNAS: Adversarial Neural Architecture Search for GANs108
The AdversarialNAS is the first method that can search the architectures of generator and discriminator simultaneously in a differentiable manner and only takes 1 GPU day to search for a superior generative model in the proposed large search space.
- Learning to segment with image-level annotations99
This paper explores a more challenging problem by learning to segment under image-level annotations by incorporating the hypotheses-aware classification and cross-image contextual refinement in a supervised manner, and proposes two network training strategies for achieving good segmentation performance.
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- Dynamic Feature Fusion for Semantic Edge Detection96
This work proposes a novel dynamic feature fusion strategy that assigns different fusion weights for different input images and locations adaptively, and outperforms all existing well established methods and achieves new state-of-the-art methods.
- Preparation and Specific Capacitance Properties of Sulfur, Nitrogen Co-Doped Graphene Quantum Dots82
The results indicate that S, N-GQDs exhibit a transverse dimension about 20 nm and a topographic height of 1–2 layers graphene, which can effectively reduce the layers of GQDs and strip the graphene sheets.
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- Continual Learning via Bit-Level Information Preserving55
This work proposes a novel continual learning approach called Bit-Level Information Preserving (BLIP) that preserves the information gain on model parameters through updating the parameters at the bit level, which can be conveniently implemented with parameter quantization.
- Training Group Orthogonal Neural Networks with Privileged Information52
A novel group orthogonal convolutional neural network (GoCNN) that learns untangled representations within each layer by exploiting provided privileged information and enhances representation diversity effectively is proposed.
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- Sharing Residual Units Through Collective Tensor Factorization To Improve Deep Neural Networks47
This work revisits the standard residual function as well as its several successful variants and proposes a unified framework based on tensor Block Term Decomposition (BTD) to explain these apparently different residual functions from the tensor decomposition view.
- Distribution-Aware Single-Stage Models for Multi-Person 3D Pose Estimation45
The proposed Distribution-Aware Single-stage DAS model simultaneously localizes person positions and their corresponding body joints in the 3D camera space in a one-pass manner, leading to a simplified pipeline with enhanced efficiency and its stat-of-the-art accuracy for multi-person 3D pose estimation.
- Adversarial Domain Adaptation With Prototype-Based Normalized Output Conditioner44
This work improves concatenation conditioning by normalizing the output predictions to have the same norm of features, and term the derived method as Normalized OutpUt coNditioner (NOUN), and proposes to condition the cross-domain feature alignment in the prototype space rather than in the output space.
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 2026-10-13. 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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