Is this you? Claim this profile to correct it, add a bio and choose the work people see first.
Claim this profileWorks211 from public data
- Multi-scale Residual Network for Image Super-Resolution925
A novel multi-scale residual network (MSRN) to fully exploit the image features, which outperform most of the state-of-the-art methods.
- Transformer for Single Image Super-Resolution677
This paper proposes a novel Efficient Super-Resolution Transformer (ESRT) for SISR, a hybrid model, which consists of a Lightweight CNN Backbone and a Light Weight Transformer Backbone that achieves competitive results with low computational cost.
- Masked Autoencoders that Listen524
The Audio-MAE, a simple extension of image-based Masked Autoencoders to self-supervised representation learning from audio spectrograms, sets new state-of-the-art performance on six audio and speech classification tasks, outperforming other recent models that use external supervised pre-training.
- Very deep convolutional neural networks for raw waveforms399
This work proposes very deep convolutional neural networks that directly use time-domain waveforms as inputs that are efficient to optimize over very long sequences, necessary for processing acoustic waveforms.
- Learning Joint Embedding with Multimodal Cues for Cross-Modal Video-Text Retrieval288
This paper proposes a novel framework that simultaneously utilizes multi-modal features (different visual characteristics, audio inputs, and text) by a fusion strategy for efficient retrieval and explores several loss functions in training the embedding.
- AnyEdit: Mastering Unified High-Quality Image Editing for Any Idea261
AnyEdit is presented, a comprehensive multi-modal instruction editing dataset, comprising 2.5 million high-quality editing pairs spanning over 20 editing types and five domains, and shows that AnyEdit consistently boosts the performance of diffusion-based editing models.
- 247
- A Comparison of Five Multiple Instance Learning Pooling Functions for Sound Event Detection with Weak Labeling203
This paper builds a neural network called TALNet, which is the first system to reach state-of-the-art audio tagging performance on Audio Set, while exhibiting strong localization performance on the DCASE 2017 challenge at the same time.
- Lightweight Real-Time Semantic Segmentation Network With Efficient Transformer and CNN184
This work proposes a lightweight real-time semantic segmentation network called LETNet that combines a U-shaped CNN with Transformer effectively in a capsule embedding style to compensate for respective deficiencies.
- Universal Phone Recognition with a Multilingual Allophone System183
This work proposes a joint model of both language-independent phone and language-dependent phoneme distributions that can build a (nearly-)universal phone recognizer that, when combined with the PHOIBLE [1] large, manually curated database of phone inventories, can be customized into 2,000 language dependent recognizers.
- 181
- Structure-Preserving Deraining with Residue Channel Prior Guidance180
A Structure-Preserving Deraining Network (SPDNet) with RCP guidance is proposed, which directly generates high-quality rain-free images with clear and accurate structures under the guidance of RCP but does not rely on any rain-generating assumptions.
- CTCNet: A CNN-Transformer Cooperation Network for Face Image Super-Resolution164
This work proposes an efficient CNN-Transformer Cooperation Network (CTCNet) for face super-resolution tasks, which uses the multi-scale connected encoder-decoder architecture as the backbone and devise a novel Local-Global Feature Cooperation Module (LGCM) to promote the consistency of local facial detail and global facial structure restoration simultaneously.
- 164
- Momentor: Advancing Video Large Language Model with Fine-Grained Temporal Reasoning156
The proposed Momentor, a Video-LLM capable of accomplishing fine-grained temporal understanding tasks, is designed and trained on Moment-10M, a large-scale video instruction dataset with segment-level instruction data, enabling it to perform segment-level reasoning and localization.
- FBSNet: A Fast Bilateral Symmetrical Network for Real-Time Semantic Segmentation154
This article proposes a Fast Bilateral Symmetrical Network (FBSNet), a symmetrical encoder-decoder structure with two branches, semantic information branch and spatial detail branch that can strike a good balance between accuracy and efficiency.
- A comparison of Deep Learning methods for environmental sound detection149
This work presents a comparison of several state-of-the-art Deep Learning models on the IEEE challenge on Detection and Classification of Acoustic Scenes and Events (DCASE) 2016 challenge task and data, classifying sounds into one of fifteen common indoor and outdoor acoustic scenes.
- 146
- 146
- 139
- Progressive Feature Fusion Network for Realistic Image Dehazing133
An U-Net like encoder-decoder deep network via progressive feature fusions has been proposed to directly learn highly nonlinear transformation function from observed hazy image to haze-free ground-truth and can achieve superior performance when compared with popular state-of-the-art methods.
- MDCN: Multi-Scale Dense Cross Network for Image Super-Resolution126
A Multi-scale Dense Cross Network (MDCN), which achieves great performance with fewer parameters and less execution time and achieves competitive results in SISR, especially in the reconstruction task with multiple upsampling factors.
- Feature Distillation Interaction Weighting Network for Lightweight Image Super-resolution122
This work proposes a lightweight yet efficient Feature Distillation Interaction Weighted Network (FDIWN), which utilizes a series of specially designed Feature Shuffle Weighted Groups as the backbone, and several novel mutual Wide-residual Distillation interaction Blocks form an FSWG.
- Fine-Grained Semantically Aligned Vision-Language Pre-Training111
This paper introduces LOUPE, a fine-grained semantically aLigned visiOn-langUage PrE-training framework, which learns fine- grained semantic alignment from the novel perspective of game-theoretic interactions, and proposes an uncertainty-aware neural Shapley interaction learning module.
- Compositional Temporal Grounding with Structured Variational Cross-Graph Correspondence Learning105
A variational cross-graph reasoning framework that explicitly decomposes video and language into multiple structured hierarchies and learns fine-grained semantic correspondence among them is proposed and empirically finds that they fail to generalize to queries with novel combinations of seen words.
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.
Report an error
Wrong papers, two people merged into one, or a profile that should not be here? Tell us and we will fix or hide it.