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- 619
- Residual Feature Distillation Network for Lightweight Image Super-Resolution610
The feature distillation connection (FDC) is proposed that is functionally equivalent to the channel splitting operation while being more lightweight and flexible and can rethink the information multi-distillation network (IMDN) and propose a lightweight and accurate SISR model called residual feature distilling network (RFDN).
- GLM-4.5: Agentic, Reasoning, and Coding (ARC) Foundation Models474
GLM-4.5 is presented, an open-source Mixture-of-Experts (MoE) large language model with 355B total parameters and 32B activated parameters, featuring a hybrid reasoning method that supports both thinking and direct response modes that ranks 3rd overall among all evaluated models and 2nd on agentic benchmarks.
- Robust Object Modeling for Visual Tracking203
A robust object modeling framework for visual tracking (ROMTrack) is proposed, which simultaneously models the inherent template and the hybrid template features, thus resulting in a more robust object modeling framework.
- PIRM Challenge on Perceptual Image Enhancement on Smartphones: Report161
This paper reviews the first challenge on efficient perceptual image enhancement with the focus on deploying deep learning models on smartphones and proposes solutions that significantly improved baseline results defining the state-of-the-art for image enhancement on smartphones.
- Fast and Memory-Efficient Network Towards Efficient Image Super-Resolution135
A novel sequential attention branch is proposed, where every pixel is assigned an important factor according to local and global contexts, to enhance high-frequency details in a fast and memory-efficient network.
- AIM 2020 Challenge on Efficient Super-Resolution: Methods and Results101
The AIM 2020 challenge on efficient single image super-resolution was to super-resolve an input image with a magnification factor x4 based on a set of prior examples of low and corresponding high resolution images with focus on the proposed solutions and results.
- NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results94
The NTIRE 2022 challenge was to super-resolve an input image with a magnification factor of ×4 based on pairs of low and corresponding high resolution images and the aim was to design a network for single image super-resolution that achieved improvement of efficiency measured according to several metrics.
- Fauce78
Fauce is the first estimator that incorporates uncertainty information for cardinality estimation into a deep learning model and is the first estimator that incorporates uncertainty information for cardinality estimation into a deep learning model.
- Real-Time Quantized Image Super-Resolution on Mobile NPUs, Mobile AI 2021 Challenge: Report76
The first Mobile AI challenge is introduced, where the target is to develop an end-to-end deep learning-based image super-resolution solutions that can demonstrate a real-time performance on mobile or edge NPUs.
- Seedance 1.5 pro: A Native Audio-Visual Joint Generation Foundation Model73
This work introduces an acceleration framework that boosts inference speed by over 10X and distinguishes itself through precise multilingual and dialect lip-syncing, dynamic cinematic camera control, and enhanced narrative coherence, positioning it as a robust engine for professional-grade content creation.
- Anchor-based Plain Net for Mobile Image Super-Resolution63
The authors' model can outperform 8-bit quantized FSRCNN by nearly 2dB in terms of PSNR, while satisfying realistic needs at the same time, and adopt quantization-aware training strategy to further boost the performance.
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- Sensing data centres for energy efficiency25
The paper summarizes data centre operations in order to describe the parameters that a data centre sensing network should collect and motivate the challenges that such a network faces.
- Lightweight Super-Resolution Head for Human Pose Estimation24
This work proposes the SR head, which predicts heatmaps with a spatial resolution higher than the input feature maps by super-resolution, to effectively reduce the quantization error and the dependence on further post-processing in heatmap prediction as a Super-Resolution (SR) task.
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- Sparse representation and adaptive mixed samples regression for single image super-resolution17
This paper presents a novel extraction and learning method about the complementary information between external samples and internal samples, and then the learned complementary information is used to improve the single image SR performance.
- RAVSS: Robust Audio-Visual Speech Separation in Multi-Speaker Scenarios with Missing Visual Cues13
This study proposes a simultaneous multi-speaker separation framework that can facilitate the concurrent separation of multiple speakers within a singular process, and introduces speaker-wise interactions to establish distinctions and correlations among speakers.
- HASN: hybrid attention separable network for efficient image super-resolution12
This work uses depth-wise separable convolutions, fully connected layers, and activation functions as the basic feature extraction modules, and proposes the hybrid attention separable block, which combines channel attention and spatial attention, thus making use of their complementary advantages.
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- From general to specific: Online updating for blind super-resolution11
An online SR (ONSR) method that does not rely on predefined degradations and allows the model weights to be updated according to the degradation of the test image, which can generate more visually favorable SR results and achieve state-of-the-art performance in blind SR.
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- HADT: Image super-resolution restoration using Hybrid Attention-Dense Connected Transformer Networks9
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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 2026-10-16. 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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