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- Dynamic Convolution: Attention Over Convolution Kernels1,508
By simply using dynamic convolution for the state-of-the-art architecture MobileNetV3-Small, the top-1 accuracy of ImageNet classification is boosted by 2.9% with only 4% additional FLOPs and 2.8 AP gain is achieved on COCO keypoint detection.
- CSWin Transformer: A General Vision Transformer Backbone with Cross-Shaped Windows1,458
The Cross-Shaped Window self-attention mechanism for computing self-Attention in the horizontal and vertical stripes in parallel that form a cross-shaped window is developed, with each stripe obtained by splitting the input feature into stripes of equal width.
- Vector Quantized Diffusion Model for Text-to-Image Synthesis1,117
This method is based on a vector quantized variational autoencoder whose latent space is modeled by a conditional variant of the recently developed Denoising Diffusion Probabilistic Model (DDPM), and it is found that this latent-space method is well-suited for text-to-image generation tasks.
- Dynamic Head: Unifying Object Detection Heads with Attentions1,037
This paper presents a novel dynamic head framework to unify object detection heads with attentions by coherently combining multiple self-attention mechanisms between feature levels for scale- awareness, among spatial locations for spatial-awareness, and within output channels for task-awareness that significantly improves the representation ability of object detection Heads without any computational overhead.
- Multi-attentional Deepfake Detection995
A new multi-attentional deepfake detection network that consists of three key components: multiple spatial attention heads to make the network attend to different local parts, a new regional independence loss and an attention guided data augmentation strategy, and state-of-the-art performance.
- Gated Context Aggregation Network for Image Dehazing and Deraining803
An end-to-end gated context aggregation network to directly restore the final haze-free image by adopting the latest smoothed dilation technique to help remove the gridding artifacts caused by the widely-used dilated convolution with negligible extra parameters.
- Mobile-Former: Bridging MobileNet and Transformer727
The proposed light-weight cross attention to model the bridge enables bidirectional fusion of local and global features and builds an efficient end-to-end detector by replacing backbone, encoder and decoder in DETR with Mobile-Former, which outperforms DETR but saves 52% of computational cost and 36% of parameters.
- StyleBank: An Explicit Representation for Neural Image Style Transfer504
This work proposes StyleBank, which is composed of multiple convolution filter banks and each filter bank explicitly represents one style, for neural image style transfer, the first style transfer network that links back to traditional texton mapping methods, and hence provides new understanding on neural style transfer.
- CLIP-NeRF: Text-and-Image Driven Manipulation of Neural Radiance Fields490
This work introduces a disentangled conditional NeRF architecture that allows individual control over both shape and appearance and proposes an inverse optimization method that accurately projects an input image to the latent codes for manipulation to enable editing on real images.
- Deep exemplar-based colorization342
This work proposes the first deep learning approach for exemplar-based local colorization, which performs robustly and generalizes well even when using reference images that are unrelated to the input grayscale image.
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- High-Fidelity Pluralistic Image Completion with Transformers314
The proposed method vastly outperforms state-of-the-art methods in terms of large performance boost on image fidelity even compared to deterministic completion methods; better diversity and higher fidelity for pluralistic completion; and exceptional generalization ability on large masks and generic dataset, like ImageNet.
- Coherent Online Video Style Transfer313
This work proposes the first end-toend network for online video style transfer, which generates temporally coherent stylized video sequences in near realtime and can achieve visually comparable coherence to optimization-based video styleTransfer, but is three orders of magnitude faster.
- Equivariant Multi-Modality Image Fusion299
This work proposes the Equivariant Multi-Modality imAge fusion (EMMA) paradigm, a novel training paradigm that encompasses a fusion module, a pseudo-sensing module, and an equivariant fusion module that enables the net training to follow the principles of the natural sensing-imaging process while satisfying the equivariant imaging prior.
- MaskCLIP: Masked Self-Distillation Advances Contrastive Language-Image Pretraining294
This paper presents a simple yet effective framework MaskCLIP, which incorporates a newly proposed masked self-distillation into contrastive language-image pretraining and introduces the local semantic supervision into the text branch, which further improves the pretraining performance.
- PeCo: Perceptual Codebook for BERT Pre-training of Vision Transformers289
A self-supervised transformer model for deep feature extraction and it is shown that it works well for calculating perceptual similarity, and such learned visual tokens indeed exhibit better semantic meanings, and help pre-training achieve superior transfer performance in various downstream tasks.
- General Facial Representation Learning in a Visual-Linguistic Manner281
This paper studies the transfer performance of pre-trained models on face analysis tasks and introduces a framework, called FaRL, for general facial representation learning, which achieves better transfer performance compared with previous pre- trained models and achieves its superiority in the low-data regime.
- BEVT: BERT Pretraining of Video Transformers279
BEVT is introduced which decouples video representation learning into spatial representation learning and temporal dynamics learning and achieves state-of-the-art performance on three challenging video benchmarks where BEVT achieves very promising results.
- Bringing Old Photos Back to Life261
A novel triplet domain translation network by leveraging real photos along with massive synthetic image pairs to restore old photos that suffer from severe degradation through a deep learning approach outperforms state-of-the-art methods in terms of visual quality.
- Dynamic ReLU226
This paper proposes dynamic ReLU (DY-ReLU), a dynamic rectifier of which parameters are generated by a hyper function over all in-put elements, and encodes the global context into the hyper function, and adapts the piecewise linear activation function accordingly.
- Unsupervised Pre-training for Person Re-identification214
This paper presents a large scale unlabeled person re-identification (Re-ID) dataset "LUPerson" and makes the first attempt of performing unsupervised pre-training for improving the generalization ability of the learned person Re-ID feature representation.
- Protecting Celebrities from DeepFake with Identity Consistency Transformer194
It is shown that Identity Consistency Transformer exhibits superior generalization ability not only across different datasets but also across various types of image degradation forms found in real-world applications including deepfake videos.
- NeRF-Art: Text-Driven Neural Radiance Fields Stylization183
This paper presents NeRF-Art, a text-guided NeRF stylization approach that manipulates the style of a pre-trained NeRF model with a simple text prompt that can shift a 3D scene to the target style characterized by desired geometry and appearance variations without any mesh guidance.
- Deep Model Intellectual Property Protection via Deep Watermarking176
A new model watermarking framework is proposed for protecting deep networks trained for low-level computer vision or image processing tasks, which can resist attacks with different network structures and objective functions.
- Model Watermarking for Image Processing Networks170
The first model watermarking framework for protecting image processing models, which can resist surrogate models learned with different network structures and objective functions is proposed and is easy to be extended to protect data and traditional image processing algorithms.
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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