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Found 15 papers, 1 papers with code
NTIRE 2025 challenge on Text to Image Generation Model Quality Assessment
A total of 211 participants have registered in the structure track.
NTIRE 2025 Challenge on Event-Based Image Deblurring: Methods and Results
This paper presents an overview of NTIRE 2025 the First Challenge on Event-Based Image Deblurring, detailing the proposed methodologies and corresponding results.
Knowledge Memorization and Rumination for Pre-trained Model-based Class-Incremental Learning
Class-Incremental Learning (CIL) enables models to continuously learn new classes while mitigating catastrophic forgetting.
Exploring structure diversity in atomic resolution microscopy with graph neural networks
The emergence of deep learning (DL) has provided great opportunities for the high-throughput analysis of atomic-resolution micrographs.
Hardware Architecture Design of Model-Based Image Reconstruction Towards Palm-size Photoacoustic Tomography
We conducted experiments by FPGA implementation of the algorithm, using both phantoms and in vivo human finger data to verify the feasibility of the proposed method.
Chemical Shift Encoding based Double Bonds Quantification in Triglycerides using Deep Image Prior
Fatty acid can potentially serve as biomarker for evaluating metabolic disorder and inflammation condition, and quantifying the double bonds is the key for revealing fatty acid information.
Optimistic Model Rollouts for Pessimistic Offline Policy Optimization
ORPO generates Optimistic model Rollouts for Pessimistic offline policy Optimization.
Self-Supervised Exploration via Temporal Inconsistency in Reinforcement Learning
Our method involves training a self-supervised prediction model, saving snapshots of the model parameters, and using nuclear norm to evaluate the temporal inconsistency between the predictions of different snapshots as intrinsic rewards.
Dynamic Memory-based Curiosity: A Bootstrap Approach for Exploration
The curiosity arouses if memorized information can not deal with the current state, and the information gap between dual learners can be formulated as the intrinsic reward for agents, and then such state information can be consolidated into the dynamic memory.
Nuclear Norm Maximization Based Curiosity-Driven Learning
To handle the sparsity of the extrinsic rewards in reinforcement learning, researchers have proposed intrinsic reward which enables the agent to learn the skills that might come in handy for pursuing the rewards in the future, such as encouraging the agent to visit novel states.
CLIP2TV: Align, Match and Distill for Video-Text Retrieval
Modern video-text retrieval frameworks basically consist of three parts: video encoder, text encoder and the similarity head.
Ranked #13 on
Video Retrieval
on MSR-VTT-1kA
(using extra training data)
Coarse to Fine: Video Retrieval before Moment Localization
The current state-of-the-art methods for video corpus moment retrieval (VCMR) often use similarity-based feature alignment approach for the sake of convenience and speed.
KnowSR: Knowledge Sharing among Homogeneous Agents in Multi-agent Reinforcement Learning
In this paper, we present an adaptation method of the majority of multi-agent reinforcement learning (MARL) algorithms called KnowSR which takes advantage of the differences in learning between agents.
KnowRU: Knowledge Reusing via Knowledge Distillation in Multi-agent Reinforcement Learning
In this paper, we propose a method, named "KnowRU" for knowledge reusing which can be easily deployed in the majority of the multi-agent reinforcement learning algorithms without complicated hand-coded design.
Random forest model identifies serve strength as a key predictor of tennis match outcome
We compiled, cleaned, and used the largest database of tennis match information to date to predict match outcome using fairly simple machine learning methods.
