Search Results for author:
Found 17 papers, 8 papers with code
GhostNetV3: Exploring the Training Strategies for Compact Models
In this paper, by systematically investigating the impact of different training ingredients, we introduce a strong training strategy for compact models.
DiffusionDialog: A Diffusion Model for Diverse Dialog Generation with Latent Space
Previous studies attempted to introduce discrete or Gaussian-based continuous latent variables to address the one-to-many problem, but the diversity is limited.
Controllable and Diverse Data Augmentation with Large Language Model for Low-Resource Open-Domain Dialogue Generation
To evaluate the efficacy of data augmentation methods for open-domain dialogue, we designed a clustering-based metric to characterize the semantic diversity of the augmented dialogue data.
A Similarity Alignment Model for Video Copy Segment Matching
We propose a Similarity Alignment Model(SAM) for video copy segment matching.
A Dual-level Detection Method for Video Copy Detection
With the development of multimedia technology, Video Copy Detection has been a crucial problem for social media platforms.
Diffusion-Based 3D Human Pose Estimation with Multi-Hypothesis Aggregation
On the other hand, JPMA is proposed to assemble multiple hypotheses generated by D3DP into a single 3D pose for practical use.
P-STMO: Pre-Trained Spatial Temporal Many-to-One Model for 3D Human Pose Estimation
In Stage II, the pre-trained encoder is loaded to STMO model and fine-tuned.
Ranked #10 on
Monocular 3D Human Pose Estimation
on Human3.6M
Tencent-MVSE: A Large-Scale Benchmark Dataset for Multi-Modal Video Similarity Evaluation
In this paper, we propose the Tencent-MVSE dataset, which is the first benchmark dataset for the multi-modal video similarity evaluation task.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Instance-Aware Dynamic Neural Network Quantization
However, natural images are of huge diversity with abundant content and using such a universal quantization configuration for all samples is not an optimal strategy.
CLIP4Caption: CLIP for Video Caption
It is noted that our model is only trained on the MSR-VTT dataset.
LICHEE: Improving Language Model Pre-training with Multi-grained Tokenization
Language model pre-training based on large corpora has achieved tremendous success in terms of constructing enriched contextual representations and has led to significant performance gains on a diverse range of Natural Language Understanding (NLU) tasks.
Post-Training Quantization for Vision Transformer
Recently, transformer has achieved remarkable performance on a variety of computer vision applications.
GhostSR: Learning Ghost Features for Efficient Image Super-Resolution
Based on the observation that many features in SISR models are also similar to each other, we propose to use shift operation to generate the redundant features (i. e., ghost features).
A Survey on Visual Transformer
Transformer, first applied to the field of natural language processing, is a type of deep neural network mainly based on the self-attention mechanism.
Pre-Trained Image Processing Transformer
To maximally excavate the capability of transformer, we present to utilize the well-known ImageNet benchmark for generating a large amount of corrupted image pairs.
Ranked #1 on
Single Image Deraining
on Rain100L
(using extra training data)
Knowledge Transfer via Student-Teacher Collaboration
One way to compress these heavy models is knowledge transfer (KT), in which a light student network is trained through absorbing the knowledge from a powerful teacher network.
Frequency-Domain Dynamic Pruning for Convolutional Neural Networks
Deep convolutional neural networks have demonstrated their powerfulness in a variety of applications.
