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Found 13 papers, 3 papers with code
MSSTNet: A Multi-Scale Spatio-Temporal CNN-Transformer Network for Dynamic Facial Expression Recognition
Our approach takes spatial features of different scales extracted by CNN and feeds them into a Multi-scale Embedding Layer (MELayer).
DreamTuner: Single Image is Enough for Subject-Driven Generation
Diffusion-based models have demonstrated impressive capabilities for text-to-image generation and are expected for personalized applications of subject-driven generation, which require the generation of customized concepts with one or a few reference images.
Contrastive Representation Disentanglement for Clustering
We conduct a theoretical analysis of the proposed loss and highlight how it assigns different weights to negative samples during the process of disentangling the feature representation.
Multi-level Distillation of Semantic Knowledge for Pre-training Multilingual Language Model
In this paper, we propose Multi-level Multilingual Knowledge Distillation (MMKD), a novel method for improving multilingual language models.
XMP-Font: Self-Supervised Cross-Modality Pre-training for Few-Shot Font Generation
The cross-modality encoder is pre-trained in a self-supervised manner to allow effective capture of cross- and intra-modality correlations, which facilitates the content-style disentanglement and modeling style representations of all scales (stroke-level, component-level and character-level).
Decoupled IoU Regression for Object Detection
Prior works propose to predict Intersection-over-Union (IoU) between bounding boxes and corresponding ground-truths to improve NMS, while accurately predicting IoU is still a challenging problem.
Multi-shot Temporal Event Localization: a Benchmark
Current developments in temporal event or action localization usually target actions captured by a single camera.
Ranked #2 on
Temporal Action Localization
on MUSES
Multi-level Knowledge Distillation via Knowledge Alignment and Correlation
While it is important to transfer the full knowledge from teacher to student, we introduce the Multi-level Knowledge Distillation (MLKD) by effectively considering both knowledge alignment and correlation.
Model-Free Voltage Regulation of Unbalanced Distribution Network Based on Surrogate Model and Deep Reinforcement Learning
Accurate knowledge of the distribution system topology and parameters is required to achieve good voltage controls, but this is difficult to obtain in practice.
Distributed Voltage Regulation of Active Distribution System Based on Enhanced Multi-agent Deep Reinforcement Learning
This paper proposes a data-driven distributed voltage control approach based on the spectrum clustering and the enhanced multi-agent deep reinforcement learning (MADRL) algorithm.
Unsupervised Hierarchical Graph Representation Learning by Mutual Information Maximization
In addition, supervised graph representation learning requires labeled data, which is expensive and error-prone.
Hierarchical Attention Networks for Medical Image Segmentation
Unlike previous self-attention based methods that capture context information from one level, we reformulate the self-attention mechanism from the view of the high-order graph and propose a novel method, namely Hierarchical Attention Network (HANet), to address the problem of medical image segmentation.
Double cycle-consistent generative adversarial network for unsupervised conditional generation
We enforce the encoder and the generator of GAN to form an encoder-generator pair in addition to the generator-encoder pair, which enables us to avoid the low-diversity generation and the triviality of latent features.
