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- Generative Image Inpainting with Contextual Attention2,597
This work proposes a new deep generative model-based approach which can not only synthesize novel image structures but also explicitly utilize surrounding image features as references during network training to make better predictions.
- Free-Form Image Inpainting With Gated Convolution2,102
The proposed gated convolution solves the issue of vanilla convolution that treats all input pixels as valid ones, generalizes partial convolution by providing a learnable dynamic feature selection mechanism for each channel at each spatial location across all layers.
- 1,363
- Top-Down Neural Attention by Excitation Backprop1,087
A new backpropagation scheme, called Excitation Backprop, is proposed to pass along top-down signals downwards in the network hierarchy via a probabilistic Winner-Take-All process, and the concept of contrastive attention is introduced to make the top- down attention maps more discriminative.
- Interactive Facial Feature Localization1,015
An improvement to the Active Shape Model is proposed that allows for greater independence among the facial components and improves on the appearance fitting step by introducing a Viterbi optimization process that operates along the facial contours.
- MAttNet: Modular Attention Network for Referring Expression Comprehension1,003
This work proposes to decompose expressions into three modular components related to subject appearance, location, and relationship to other objects, which allows for flexibly adapt to expressions containing different types of information in an end-to-end framework.
- High-Resolution Image Inpainting Using Multi-scale Neural Patch Synthesis877
This work proposes a multi-scale neural patch synthesis approach based on joint optimization of image content and texture constraints, which not only preserves contextual structures but also produces high-frequency details by matching and adapting patches with the most similar mid-layer feature correlations of a deep classification network.
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- MAT: Mask-Aware Transformer for Large Hole Image Inpainting539
A novel transformer-based model for large hole inpainting is presented, which unifies the merits of transformers and convolutions to efficiently process high-resolution images.
- 460
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- RAPID434
The RAPID (RAting PIctorial aesthetics using Deep learning) system is presented, which adopts a novel deep neural network approach to enable automatic feature learning and style attributes of images to help improve the aesthetic quality categorization accuracy.
- InstantBooth: Personalized Text-to-Image Generation without Test-Time Finetuning428
InstantBooth is an innovative approach leveraging existing text-to-image models for instantaneous text-guided image personalization, eliminating the need for test-time finetuning and boasting a 100-fold increase in generation speed.
- Foreground-Aware Image Inpainting402
This work proposes a foreground-aware image inpainting system that explicitly disentangles structure inference and content completion, and shows that by such disentanglement, the contour completion model predicts reasonable contours of objects, and further substantially improves the performance of image inPainting.
- SmartBrush: Text and Shape Guided Object Inpainting with Diffusion Model395
This work proposes a new diffusion-based model named SmartBrush for completing a missing region with an object using both text and shape-guidance and shows that this model outperforms all baselines in terms of visual quality, mask controllability, and background preservation.
- Video-P2P: Video Editing with Cross-Attention Control391
This work proposes to first tune a Text-to-Set (T2S) model to complete an approximate inversion and then optimize a shared unconditional embedding to achieve accurate video inversion with a small memory cost, and proves that it is crucial for consistent video editing.
- Image Super-Resolution by Neural Texture Transfer379
An end-to-end deep model which enriches HR details by adaptively transferring the texture from Ref images according to their textural similarity is designed, which facilitates multi-scale neural transfer that allows the model to benefit more from those semantically related Ref patches, and gracefully degrade to SISR performance on the least relevant Ref inputs.
- 337
- Scene Graph Generation With External Knowledge and Image Reconstruction322
This paper proposes a novel scene graph generation algorithm with external knowledge and image reconstruction loss to overcome dataset issues, and extracts commonsense knowledge from the external knowledge base to refine object and phrase features for improving generalizability inscene graph generation.
- 321
- Recurrent Multimodal Interaction for Referring Image Segmentation315
It is argued that learning word-to-image interaction is more native in the sense of jointly modeling two modalities for the image segmentation task, and a convolutional multimodal LSTM to encode the sequential interactions between individual words, visual information, and spatial information is proposed.
- Deep Image Harmonization314
This work proposes an end-to-end deep convolutional neural network for image harmonization, which can capture both the context and semantic information of the composite images during harmonization and introduces an efficient way to collect large-scale and high-quality training data that can facilitate the training process.
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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 2026-10-13. 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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