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Found 11 papers, 4 papers with code
RingID: Rethinking Tree-Ring Watermarking for Enhanced Multi-Key Identification
We revisit Tree-Ring Watermarking, a recent diffusion model watermarking method that demonstrates great robustness to various attacks.
PMT-IQA: Progressive Multi-task Learning for Blind Image Quality Assessment
To verify the effectiveness of the proposed PMT-IQA model, we conduct experiments on four widely used public datasets, and the experimental results indicate that the performance of PMT-IQA is superior to the comparison approaches, and both MS and PMT modules improve the model's performance.
Modeling Hierarchical Structural Distance for Unsupervised Domain Adaptation
Compared with the ground distance of the conventional domain-level OT, the image-level OT captures structural associations among local regions of images that are beneficial to classification.
TriangleNet: Edge Prior Augmented Network for Semantic Segmentation through Cross-Task Consistency
The proposed method underscores the significance of multi-task learning and explicit cross-task consistency enhancement for advancing semantic segmentation and highlights the potential of multitasking in real-time semantic segmentation.
Graph-based Visual-Semantic Entanglement Network for Zero-shot Image Recognition
Zero-shot learning uses semantic attributes to connect the search space of unseen objects.
Parallelized Training of Restricted Boltzmann Machines using Markov-Chain Monte Carlo Methods
Restricted Boltzmann Machine (RBM) is a generative stochastic neural network that can be applied to collaborative filtering technique used by recommendation systems.
Densifying Assumed-sparse Tensors: Improving Memory Efficiency and MPI Collective Performance during Tensor Accumulation for Parallelized Training of Neural Machine Translation Models
Neural machine translation - using neural networks to translate human language - is an area of active research exploring new neuron types and network topologies with the goal of dramatically improving machine translation performance.
An attention-based BiLSTM-CRF approach to document-level chemical named entity recognition
Motivation: In biomedical research, chemical is an important class of entities, and chemical named entity recognition (NER) is an important task in the field of biomedical information extraction.
Ranked #2 on
Named Entity Recognition (NER)
on BC4CHEMD
An Effective Way to Improve YouTube-8M Classification Accuracy in Google Cloud Platform
Competitors are challenged to develop classification algorithms that assign video-level labels using the new and improved Youtube-8M V2 dataset.
Multi-agent Reinforcement Learning with Sparse Interactions by Negotiation and Knowledge Transfer
In this paper, a novel algorithm named negotiation-based MARL with sparse interactions (NegoSI) is presented.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
