Search Results for author:
Found 21 papers, 10 papers with code
Diffusion Model-Based Image Editing: A Survey
In this survey, we provide an exhaustive overview of existing methods using diffusion models for image editing, covering both theoretical and practical aspects in the field.
GPT4Motion: Scripting Physical Motions in Text-to-Video Generation via Blender-Oriented GPT Planning
To tackle these issues, we propose GPT4Motion, a training-free framework that leverages the planning capability of large language models such as GPT, the physical simulation strength of Blender, and the excellent image generation ability of text-to-image diffusion models to enhance the quality of video synthesis.
Seal2Real: Prompt Prior Learning on Diffusion Model for Unsupervised Document Seal Data Generation and Realisation
To address the lack of labelled datasets for these seal-related tasks, we propose Seal2Real, a generative method that generates a large amount of labelled document seal data, and construct a Seal-DB dataset containing 20K images with labels.
IFAST: Weakly Supervised Interpretable Face Anti-spoofing from Single-shot Binocular NIR Images
Single-shot face anti-spoofing (FAS) is a key technique for securing face recognition systems, and it requires only static images as input.
KV Inversion: KV Embeddings Learning for Text-Conditioned Real Image Action Editing
Text-conditioned image editing is a recently emerged and highly practical task, and its potential is immeasurable.
Bootstrap Diffusion Model Curve Estimation for High Resolution Low-Light Image Enhancement
Learning-based methods have attracted a lot of research attention and led to significant improvements in low-light image enhancement.
Graph Edit Distance Learning via Different Attention
To this end, DiffAtt uses the difference between two graph-level embeddings as an attentional mechanism to capture the graph structural difference of the two graphs.
WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instruct
Through extensive experiments on two mathematical reasoning benchmarks, namely GSM8k and MATH, we reveal the extraordinary capabilities of our model.
Ranked #49 on
Arithmetic Reasoning
on GSM8K
(using extra training data)
WaveDM: Wavelet-Based Diffusion Models for Image Restoration
Latest diffusion-based methods for many image restoration tasks outperform traditional models, but they encounter the long-time inference problem.
V4D: Voxel for 4D Novel View Synthesis
The proposed LUTs-based refinement module achieves the performance gain with little computational cost and could serve as the plug-and-play module in the novel view synthesis task.
ES6D: A Computation Efficient and Symmetry-Aware 6D Pose Regression Framework
In this paper, a computation efficient regression framework is presented for estimating the 6D pose of rigid objects from a single RGB-D image, which is applicable to handling symmetric objects.
Semi-Supervised Segmentation of Radiation-Induced Pulmonary Fibrosis from Lung CT Scans with Multi-Scale Guided Dense Attention
Computed Tomography (CT) plays an important role in monitoring radiation-induced Pulmonary Fibrosis (PF), where accurate segmentation of the PF lesions is highly desired for diagnosis and treatment follow-up.
New Perspective on Progressive GANs Distillation for One-class Novelty Detection
One-class novelty detection is conducted to identify anomalous instances, with different distributions from the expected normal instances.
Good Practices and A Strong Baseline for Traffic Anomaly Detection
In this paper, we propose a straightforward and efficient framework that includes pre-processing, a dynamic track module, and post-processing.
P-KDGAN: Progressive Knowledge Distillation with GANs for One-class Novelty Detection
The progressive learning of knowledge distillation is a two-step approach that continuously improves the performance of the student GAN and achieves better performance than single step methods.
Ranked #12 on
Anomaly Detection
on Fashion-MNIST
Dynamic Inference: A New Approach Toward Efficient Video Action Recognition
In a nutshell, we treat input frames and network depth of the computational graph as a 2-dimensional grid, and several checkpoints are placed on this grid in advance with a prediction module.
FD-GAN: Generative Adversarial Networks with Fusion-discriminator for Single Image Dehazing
With the proposed Fusion-discriminator which takes frequency information as additional priors, our model can generator more natural and realistic dehazed images with less color distortion and fewer artifacts.
Multi-Agent Reinforcement Learning Based Frame Sampling for Effective Untrimmed Video Recognition
Video Recognition has drawn great research interest and great progress has been made.
Ranked #7 on
Action Recognition
on ActivityNet
Semantic Hierarchy Preserving Deep Hashing for Large-scale Image Retrieval
Deep hashing models have been proposed as an efficient method for large-scale similarity search.
Deep Class-Wise Hashing: Semantics-Preserving Hashing via Class-wise Loss
In this regard, we propose a novel deep supervised hashing model to learn more compact class-level similarity preserving binary codes.
Directional Statistics-based Deep Metric Learning for Image Classification and Retrieval
Deep distance metric learning (DDML), which is proposed to learn image similarity metrics in an end-to-end manner based on the convolution neural network, has achieved encouraging results in many computer vision tasks.$L2$-normalization in the embedding space has been used to improve the performance of several DDML methods.
