Search Results for author:
Found 28 papers, 7 papers with code
Attentional Triple-Encoder Network in Spatiospectral Domains for Medical Image Segmentation
Retinal Optical Coherence Tomography (OCT) segmentation is essential for diagnosing pathology.
Enhancing Reasoning through Process Supervision with Monte Carlo Tree Search
Large language models (LLMs) have demonstrated their remarkable capacity across a variety of tasks.
Multiple Consistency-guided Test-Time Adaptation for Contrastive Audio-Language Models with Unlabeled Audio
First, guidance of consistency on both context tokens and domain tokens of ALMs is set.
Towards Intrinsic Self-Correction Enhancement in Monte Carlo Tree Search Boosted Reasoning via Iterative Preference Learning
Our work leverages step-wise preference learning to enhance self-verification via reinforcement learning.
Low-Rank Adaptation with Task-Relevant Feature Enhancement for Fine-tuning Language Models
Fine-tuning pre-trained large language models in a parameter-efficient manner is widely studied for its effectiveness and efficiency.
YingSound: Video-Guided Sound Effects Generation with Multi-modal Chain-of-Thought Controls
Generating sound effects for product-level videos, where only a small amount of labeled data is available for diverse scenes, requires the production of high-quality sounds in few-shot settings.
Multi-Stage Graph Learning for fMRI Analysis to Diagnose Neuro-Developmental Disorders
The insufficient supervision limit the performance of the deep supervised models for brain disease diagnosis.
OCC-MLLM:Empowering Multimodal Large Language Model For the Understanding of Occluded Objects
There is a gap in the understanding of occluded objects in existing large-scale visual language multi-modal models.
OCC-MLLM-Alpha:Empowering Multi-modal Large Language Model for the Understanding of Occluded Objects with Self-Supervised Test-Time Learning
There is a gap in the understanding of occluded objects in existing large-scale visual language multi-modal models.
Towards Full-parameter and Parameter-efficient Self-learning For Endoscopic Camera Depth Estimation
Adaptation methods are developed to adapt depth foundation models to endoscopic depth estimation recently.
Self-Supervised Learning of Deviation in Latent Representation for Co-speech Gesture Video Generation
Gestures are pivotal in enhancing co-speech communication.
Bailing-TTS: Chinese Dialectal Speech Synthesis Towards Human-like Spontaneous Representation
Large-scale text-to-speech (TTS) models have made significant progress recently. However, they still fall short in the generation of Chinese dialectal speech.
HieraFashDiff: Hierarchical Fashion Design with Multi-stage Diffusion Models
Fashion design is a challenging and complex process. Recent works on fashion generation and editing are all agnostic of the actual fashion design process, which limits their usage in practice. In this paper, we propose a novel hierarchical diffusion-based framework tailored for fashion design, coined as HieraFashDiff.
Hierarchical Reinforcement Learning for Furniture Layout in Virtual Indoor Scenes
In this paper, we explore the furniture layout task as a Markov decision process (MDP) in virtual reality, which is solved by hierarchical reinforcement learning (HRL).
Hierarchical Reinforcement Learning
reinforcement-learning
+2
LWA-HAND: Lightweight Attention Hand for Interacting Hand Reconstruction
The first module is a lightweight feature attention module that extracts both local occlusion representation and global image patch representation in a coarse-to-fine manner.
Multi-Agent Reinforcement Learning of 3D Furniture Layout Simulation in Indoor Graphics Scenes
In the industrial interior design process, professional designers plan the furniture layout to achieve a satisfactory 3D design for selling.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
Deep Reinforcement Learning for Producing Furniture Layout in Indoor Scenes
In the industrial interior design process, professional designers plan the size and position of furniture in a room to achieve a satisfactory design for selling.
End-to-end Generative Floor-plan and Layout with Attributes and Relation Graph
We conduct our experiments on the proposed real-world interior layout dataset that contains $191208$ designs from the professional designers.
Deep Layout of Custom-size Furniture through Multiple-domain Learning
In this paper, we propose a multiple-domain model for producing a custom-size furniture layout in the interior scene.
Structural Plan of Indoor Scenes with Personalized Preferences
In this paper, we propose an assistive model that supports professional interior designers to produce industrial interior decoration solutions and to meet the personalized preferences of the property owners.
Adversarial Model for Rotated Indoor Scenes Planning
In this paper, we propose an adversarial model for producing furniture layout for interior scene synthesis when the interior room is rotated.
The Direction-Aware, Learnable, Additive Kernels and the Adversarial Network for Deep Floor Plan Recognition
Besides, an adversarial network with two discriminators is proposed to further improve the accuracy of the elements and to reduce the noise of the semantic segmentation.
Mutual Information Maximization in Graph Neural Networks
In order to reduce the loss, we extend the GNNs frameworks by exploring the aggregation and iteration scheme in the methodology of mutual information.
Ranked #1 on
Graph Classification
on Citeseer
PointCNN: Convolution On X-Transformed Points
We present a simple and general framework for feature learning from point cloud.
Ranked #2 on
Semantic Segmentation
on S3DIS Area5
(Number of params metric)
Ambient Hidden Space of Generative Adversarial Networks
In this paper, we extend the ambient module to the hidden space of the generator, and provide the uniqueness condition and the corresponding strategy for the ambient hidden generator in the adversarial training process.
Towards Adversarial Training with Moderate Performance Improvement for Neural Network Classification
It has been demonstrated that deep neural networks are prone to noisy examples particular adversarial samples during inference process.
PointCNN: Convolution On $\mathcal{X}$-Transformed Points
The proposed method is a generalization of typical CNNs to feature learning from point clouds, thus we call it PointCNN.
Ranked #1 on
3D Instance Segmentation
on S3DIS
(mIoU metric)
3D Reconstruction of Simple Objects from A Single View Silhouette Image
While recent deep neural networks have achieved promising results for 3D reconstruction from a single-view image, these rely on the availability of RGB textures in images and extra information as supervision.
