Is this you? Claim this profile to correct it, add a bio and choose the work people see first.
Claim this profileAcademic lineage
Possible advisorsa guess from early papers, not confirmed
- Chenliang XuSuggested from co-authorship
Is this you? Claim this profile to confirm or dismiss it.
Works103 from public data
- Residual Dense Network for Image Super-Resolution4,163
This paper proposes residual dense block (RDB) to extract abundant local features via dense connected convolutional layers and uses global feature fusion in RDB to jointly and adaptively learn global hierarchical features in a holistic way.
- 1,955
- Deep Learning for Single Image Super-Resolution: A Brief Review1,151
This survey reviews representative deep learning-based SISR methods and group them into two categories according to their contributions to two essential aspects of S ISR: The exploration of efficient neural network architectures for SISS and the development of effective optimization objectives for deep SISr learning.
- Residual Dense Network for Image Restoration895
This work proposes residual dense block (RDB) to extract abundant local features via densely connected convolutional layers and proposes local feature fusion in RDB to adaptively learn more effective features from preceding and current local features and stabilize the training of wider network.
- TDAN: Temporally-Deformable Alignment Network for Video Super-Resolution655
Experimental results demonstrate that theTDAN is capable of alleviating occlusions and artifacts for temporal alignment and the TDAN-based VSR model outperforms several recent state-of-the-art VSR networks with a comparable or even much smaller model size.
- 491
- DiffIR: Efficient Diffusion Model for Image Restoration476
This work proposes an efficient DM for IR (DiffIR), which consists of a compact IR prior extraction network (CPEN), dynamic IR transformer (DIRformer), and denoising network, which can adopt a joint optimization of CPENS2, DIRformer, and denoising network, which can further reduce the estimation error influence.
- Learning to Answer Questions in Dynamic Audio-Visual Scenarios295
The results demonstrate that AVQA benefits from multisensory perception and the model outperforms recent A-, V-, andAVQA approaches.
- Unified Multisensory Perception: Weakly-Supervised Audio-Visual Video Parsing234
Experimental results show that the challenging audio-visual video parsing can be achieved even with only video-level weak labels, and the proposed framework can effectively leverage unimodal and cross-modal temporal contexts and alleviate modality bias and noisy labels problems.
- Zooming Slow-Mo: Fast and Accurate One-Stage Space-Time Video Super-Resolution214
A one-stage space-time video super-resolution framework is proposed, which directly synthesizes an HR slow-motion video from an LFR, LR video and proposes a deformable ConvLSTM to align and aggregate temporal information simultaneously for better leveraging global temporal contexts.
- Transformer-empowered Multi-scale Contextual Matching and Aggregation for Multi-contrast MRI Super-resolution124
A novel network of innovative Transformer-empowered multi-scale contextual matching and aggregation techniques is proposed, which outperforms state-of-the-art approaches and has great potential to be applied in clinical practice.
- Efficient Non-local Contrastive Attention for Image Super-resolution106
A novel Efficient Non-Local Contrastive Attention (ENLCA) to perform long-range visual modeling and leverage more relevant non-local features and reaches superior performance over state-of-the-art approaches on both quantitative and qualitative evaluations.
- Cyclic Co-Learning of Sounding Object Visual Grounding and Sound Separation99
This paper proposes a cyclic co-learning (CCoL) paradigm that can jointly learn sounding object visual grounding and audio-visual sound separation in a unified framework and improves training example sampling for sounding object grounding, which builds a co- learning cycle for the two tasks and makes them mutually beneficial.
- Learning in Audio-visual Context: A Review, Analysis, and New Perspective81
This survey reviews and outlooks the current audio-visual learning field from different aspects and proposes a new perspective on audio- Visual scene understanding, then discusses and analyze the feasible future direction of the audio- visual learning area.
- Audio-Visual Grouping Network for Sound Localization from Mixtures75
This paper proposes a novel audio-visual grouping network, namely AVGN, that can directly learn category-wise semantic features for each source from the input audio mixture and image to localize multiple sources simultaneously.
- CFSNet: Toward a Controllable Feature Space for Image Restoration70
The proposed framework, named Controllable Feature Space Network (CFSNet), is entangled by two branches based on different objectives, which can adaptively learn the coupling coefficients of different layers and channels, which provides finer control of the restored image quality.
- AV-SAM: Segment Anything Model Meets Audio-Visual Localization and Segmentation65
This work proposes a simple yet effective audio-visual localization and segmentation framework based on the Segment Anything Model, namely AV-SAM, that can generate sounding object masks corresponding to the audio.
- Cross Modality Bias in Visual Question Answering: A Causal View With Possible Worlds VQA63
This is the first work to reduce biases resulting from confounding effects of vision and language in VQA, leveraging causal explain-away relations and outperforms the state-of-the-art methods in VQA-CP v2 datasets.
- Egocentric Audio-Visual Object Localization55
This paper proposes a geometryaware temporal aggregation module that handles the egomotion explicitly and proposes a cascaded feature enhancement module to over-come the second issue and improves cross-modal localization robustness by disentangling visually-indicated audio representation.
- Audio-Visual Class-Incremental Learning55
AV-CIL is proposed, which incorporates Dual-Audio-Visual Similarity Constraint (D-AVSC) to maintain both instance-aware and class-aware semantic similarity between audio-visual modalities and Visual Attention Distillation (VAD) to retain previously learned audio-guided visual attentive ability.
- 54
- Can audio-visual integration strengthen robustness under multimodal attacks?46
A systematic study on machines’ multisensory perception under attacks using the audio-visual event recognition task against multi-modal adversarial attacks as a proxy to investigate the robustness of audio- visual learning.
- 45
- AV-DiT: Efficient Audio-Visual Diffusion Transformer for Joint Audio and Video Generation44
AV-DiT is introduced, a novel and efficient audio-visual diffusion transformer designed to generate high-quality, realistic videos with both visual and audio tracks, and highlights that a single shared image generative backbone with modality-specific adaptations is sufficient for constructing a joint audio-video generator.
- CMRxRecon: A publicly available k-space dataset and benchmark to advance deep learning for cardiac MRI43
A dataset that includes multi-contrast, multi-view, multi-slice and multi-coil CMR imaging data from 300 subjects is released, introducing standardized evaluation criteria and making the dataset freely accessible to the research community.
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.
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.