Search Results for author:
Found 15 papers, 4 papers with code
Anchor-aware Deep Metric Learning for Audio-visual Retrieval
However, the model training fails to fully explore the space due to the scarcity of training data points, resulting in an incomplete representation of the overall positive and negative distributions.
Amplitude-Phase Fusion for Enhanced Electrocardiogram Morphological Analysis
Considering the variability of amplitude and phase patterns in electrocardiogram (ECG) signals due to cardiac activity and individual differences, existing entropy-based studies have not fully utilized these two patterns and lack integration.
TimeGraphs: Graph-based Temporal Reasoning
The results demonstrate both robustness and efficiency of TimeGraphs on a range of temporal reasoning tasks.
VideoAdviser: Video Knowledge Distillation for Multimodal Transfer Learning
In this work, to achieve high efficiency-performance multimodal transfer learning, we propose VideoAdviser, a video knowledge distillation method to transfer multimodal knowledge of video-enhanced prompts from a multimodal fundamental model (teacher) to a specific modal fundamental model (student).
What are Public Concerns about ChatGPT? A Novel Self-Supervised Neural Topic Model Tells You
The recently released artificial intelligence conversational agent, ChatGPT, has gained significant attention in academia and real life.
Topic-switch adapted Japanese Dialogue System based on PLATO-2
Furthermore, our proposed topic-switch algorithm achieves an average score of 1. 767 and outperforms PLATO-JDS by 0. 267, indicating its effectiveness in improving the user experience of our system.
Do I Have Your Attention: A Large Scale Engagement Prediction Dataset and Baselines
The experiments show the usefulness of the proposed dataset.
Complete Cross-triplet Loss in Label Space for Audio-visual Cross-modal Retrieval
In this paper, to reduce the interference of hard negative samples in representation learning, we propose a new AV-CMR model to optimize semantic features by directly predicting labels and then measuring the intrinsic correlation between audio-visual data using complete cross-triple loss.
VQA-GNN: Reasoning with Multimodal Knowledge via Graph Neural Networks for Visual Question Answering
Visual question answering (VQA) requires systems to perform concept-level reasoning by unifying unstructured (e. g., the context in question and answer; "QA context") and structured (e. g., knowledge graph for the QA context and scene; "concept graph") multimodal knowledge.
Cloning Outfits from Real-World Images to 3D Characters for Generalizable Person Re-Identification
To address this, in this work, an automatic approach is proposed to directly clone the whole outfits from real-world person images to virtual 3D characters, such that any virtual person thus created will appear very similar to its real-world counterpart.
Ranked #1 on
Unsupervised Domain Adaptation
on ClonedPerson
(using extra training data)
Generalizable Person Re-identification
Unsupervised Domain Adaptation
+1
Sequential Search with Off-Policy Reinforcement Learning
Recent years have seen a significant amount of interests in Sequential Recommendation (SR), which aims to understand and model the sequential user behaviors and the interactions between users and items over time.
EVBattery: A Large-Scale Electric Vehicle Dataset for Battery Health and Capacity Estimation
In addition to demonstrating how existing deep learning algorithms can be applied to this task, we further develop an algorithm that exploits the data structure of battery systems.
Offline Meta-level Model-based Reinforcement Learning Approach for Cold-Start Recommendation
To improve adaptation efficiency, we learn to recover the user policy and reward from only a few interactions via an inverse reinforcement learning method to assist a meta-level recommendation agent.
Model-based Reinforcement Learning
Recommendation Systems
+2
Surpassing Real-World Source Training Data: Random 3D Characters for Generalizable Person Re-Identification
To address this, we propose to automatically synthesize a large-scale person re-identification dataset following a set-up similar to real surveillance but with virtual environments, and then use the synthesized person images to train a generalizable person re-identification model.
Domain Generalization
Generalizable Person Re-identification
+1
STMARL: A Spatio-Temporal Multi-Agent Reinforcement Learning Approach for Cooperative Traffic Light Control
Moreover, based on the temporally-dependent traffic information, we design a Graph Neural Network based model to represent relationships among multiple traffic lights, and the decision for each traffic light will be made in a distributed way by the deep Q-learning method.
