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Found 22 papers, 7 papers with code
NTIRE 2025 Challenge on Image Super-Resolution ($\times$4): Methods and Results
This paper presents the NTIRE 2025 image super-resolution ($\times$4) challenge, one of the associated competitions of the 10th NTIRE Workshop at CVPR 2025.
NTIRE 2025 Challenge on Short-form UGC Video Quality Assessment and Enhancement: Methods and Results
This paper presents a review for the NTIRE 2025 Challenge on Short-form UGC Video Quality Assessment and Enhancement.
The Tenth NTIRE 2025 Efficient Super-Resolution Challenge Report
This paper presents a comprehensive review of the NTIRE 2025 Challenge on Single-Image Efficient Super-Resolution (ESR).
Agglomerating Large Vision Encoders via Distillation for VFSS Segmentation
In order to achieve an improved tradeoff between complexity and performance, we propose a new framework to improve the performance of low complexity models via knowledge distillation from multiple large medical foundation models (e. g., MedSAM, RAD-DINO, MedCLIP), each specializing in different vision tasks, with the goal to effectively bridge the performance gap for medical image segmentation tasks.
C2D-ISR: Optimizing Attention-based Image Super-resolution from Continuous to Discrete Scales
Our approach is based on a two-stage training methodology and a hierarchical encoding mechanism.
Memorization Over Reasoning? Exposing and Mitigating Verbatim Memorization in Large Language Models' Character Understanding Evaluation
Recently, Large Language Models (LLMs) have shown impressive performance in character understanding tasks, such as analyzing the roles, personalities, and relationships of fictional characters.
HIIF: Hierarchical Encoding based Implicit Image Function for Continuous Super-resolution
Recent advances in implicit neural representations (INRs) have shown significant promise in modeling visual signals for various low-vision tasks including image super-resolution (ISR).
From Generation to Judgment: Opportunities and Challenges of LLM-as-a-judge
Assessment and evaluation have long been critical challenges in artificial intelligence (AI) and natural language processing (NLP).
RTSR: A Real-Time Super-Resolution Model for AV1 Compressed Content
To address this issue, this paper proposes a low-complexity SR method, RTSR, designed to enhance the visual quality of compressed video content, focusing on resolution up-scaling from a) 360p to 1080p and from b) 540p to 4K.
Real-time Vehicle-to-Vehicle Communication Based Network Cooperative Control System through Distributed Database and Multimodal Perception: Demonstrated in Crossroads
The autonomous driving industry is rapidly advancing, with Vehicle-to-Vehicle (V2V) communication systems highlighting as a key component of enhanced road safety and traffic efficiency.
Benchmarking Conventional and Learned Video Codecs with a Low-Delay Configuration
Recent advances in video compression have seen significant coding performance improvements with the development of new standards and learning-based video codecs.
BVI-AOM: A New Training Dataset for Deep Video Compression Optimization
Deep learning is now playing an important role in enhancing the performance of conventional hybrid video codecs.
Rocket Landing Control with Random Annealing Jump Start Reinforcement Learning
Our approach, called Random Annealing Jump Start (RAJS), is tailored for real-world goal-oriented problems by leveraging prior feedback controllers as guide policy to facilitate environmental exploration and policy learning in RL.
Diffusion Actor-Critic with Entropy Regulator
This algorithm conceptualizes the reverse process of the diffusion model as a novel policy function and leverages the capability of the diffusion model to fit multimodal distributions, thereby enhancing the representational capacity of the policy.
MTKD: Multi-Teacher Knowledge Distillation for Image Super-Resolution
Knowledge distillation (KD) has emerged as a promising technique in deep learning, typically employed to enhance a compact student network through learning from their high-performance but more complex teacher variant.
Compressing Deep Image Super-resolution Models
Deep learning techniques have been applied in the context of image super-resolution (SR), achieving remarkable advances in terms of reconstruction performance.
Xpert: Empowering Incident Management with Query Recommendations via Large Language Models
This paper presents a thorough empirical study on the utilization of queries of KQL, a DSL employed for incident management in a large-scale cloud management system at Microsoft.
LanguageMPC: Large Language Models as Decision Makers for Autonomous Driving
Existing learning-based autonomous driving (AD) systems face challenges in comprehending high-level information, generalizing to rare events, and providing interpretability.
SEPT: Towards Efficient Scene Representation Learning for Motion Prediction
Motion prediction is crucial for autonomous vehicles to operate safely in complex traffic environments.
A new parsimonious method for classifying Cancer Tissue-of-Origin Based on DNA Methylation 450K data
The biological functions in cancer development of 50 selected genes is also explored through enrichment analysis and the results show that 12 out of 16 of our top features have already been identified to be specific with cancer and we also propose some more genes to be tested for future studies.
Probing blood pressure and arterial stiffness noninvasively by guided axial waves
The results and methods reported in this study show the group velocity and dispersion relation of the guided waves can be adopted to probe BP and arterial stiffness noninvasively and thus promising for early diagnosis of the CVDs.
Medical Physics Soft Condensed Matter 74J05, 74L15, 92C50
Procedural Generation of Angry Birds Levels using Building Constructive Grammar with Chinese-Style and/or Japanese-Style Models
This paper presents a procedural generation method that creates visually attractive levels for the Angry Birds game.
