Search Results for author:
Found 34 papers, 16 papers with code
Geo-ConvGRU: Geographically Masked Convolutional Gated Recurrent Unit for Bird-Eye View Segmentation
Convolutional Neural Networks (CNNs) have significantly impacted various computer vision tasks, however, they inherently struggle to model long-range dependencies explicitly due to the localized nature of convolution operations.
Direct Preference Optimization Using Sparse Feature-Level Constraints
The alignment of large language models (LLMs) with human preferences remains a key challenge.
CycleResearcher: Improving Automated Research via Automated Review
In research, the papers generated by the CycleResearcher model achieved a score of 5. 36 in simulated peer reviews, surpassing the preprint level of 5. 24 from human experts and approaching the accepted paper level of 5. 69.
Lotus: Diffusion-based Visual Foundation Model for High-quality Dense Prediction
In this paper, we provide a systemic analysis of the diffusion formulation for the dense prediction, focusing on both quality and efficiency.
LLM-based MOFs Synthesis Condition Extraction using Few-Shot Demonstrations
The extraction of Metal-Organic Frameworks (MOFs) synthesis conditions from literature text has been challenging but crucial for the logical design of new MOFs with desirable functionality.
Segment, Lift and Fit: Automatic 3D Shape Labeling from 2D Prompts
Unlike previous arts, our auto-labeler predicts 3D shapes instead of bounding boxes and does not require training on a specific dataset.
An Analysis on Quantizing Diffusion Transformers
Prior works address PTQ of DMs on UNet structures have addressed the challenges in calibrating parameters for both activations and weights via moderate optimization.
Large Language Models Meet Text-Centric Multimodal Sentiment Analysis: A Survey
Compared to traditional sentiment analysis, which only considers text, multimodal sentiment analysis needs to consider emotional signals from multimodal sources simultaneously and is therefore more consistent with the way how humans process sentiment in real-world scenarios.
AutoSurvey: Large Language Models Can Automatically Write Surveys
This paper introduces AutoSurvey, a speedy and well-organized methodology for automating the creation of comprehensive literature surveys in rapidly evolving fields like artificial intelligence.
An Organic Weed Control Prototype using Directed Energy and Deep Learning
In this work, a directed energy weed control robot prototype specifically designed for organic farms is proposed.
How Likely Do LLMs with CoT Mimic Human Reasoning?
Chain-of-thought emerges as a promising technique for eliciting reasoning capabilities from Large Language Models (LLMs).
Forging Vision Foundation Models for Autonomous Driving: Challenges, Methodologies, and Opportunities
The rise of large foundation models, trained on extensive datasets, is revolutionizing the field of AI.
Self-supervised Event-based Monocular Depth Estimation using Cross-modal Consistency
In this paper, to lower the annotation cost, we propose a self-supervised event-based monocular depth estimation framework named EMoDepth.
Efficient Quantization Strategies for Latent Diffusion Models
By treating the quantization discrepancy as relative noise and identifying sensitive part(s) of a model, we propose an efficient quantization approach encompassing both global and local strategies.
HuatuoGPT-II, One-stage Training for Medical Adaption of LLMs
We validate the new protocol in the domains where proprietary LLMs like ChatGPT perform relatively poorly, such as Traditional Chinese Medicine.
Co-training and Co-distillation for Quality Improvement and Compression of Language Models
2) The enhanced performance of the larger model further boosts the performance of the smaller model.
An Early Evaluation of GPT-4V(ision)
In this paper, we evaluate different abilities of GPT-4V including visual understanding, language understanding, visual puzzle solving, and understanding of other modalities such as depth, thermal, video, and audio.
Multi-step prediction of chlorophyll concentration based on Adaptive Graph-Temporal Convolutional Network with Series Decomposition
The adaptive graph convolution learns the relationship between different water quality parameters, updates the state information of each parameter, and improves the learning ability of the update relationship between nodes.
Enhancing Dialogue Generation via Dynamic Graph Knowledge Aggregation
Further analysis also shows that our representation learning framework can fill the semantic gap by coagulating representations of both text and graph knowledge.
HuatuoGPT, towards Taming Language Model to Be a Doctor
Experimental results demonstrate that HuatuoGPT achieves state-of-the-art results in performing medical consultation among open-source LLMs in GPT-4 evaluation, human evaluation, and medical benchmark datasets.
Injecting Knowledge into Biomedical Pre-trained Models via Polymorphism and Synonymous Substitution
This gives us a hint that relational knowledge might not be redundant to the stored knowledge of PLMs, but rather be complementary.
CADGE: Context-Aware Dialogue Generation Enhanced with Graph-Structured Knowledge Aggregation
Commonsense knowledge is crucial to many natural language processing tasks.
Phoenix: Democratizing ChatGPT across Languages
This paper presents our efforts to democratize ChatGPT across language.
Natural Language Reasoning, A Survey
This survey paper proposes a clearer view of natural language reasoning in the field of Natural Language Processing (NLP), both conceptually and practically.
FG-Depth: Flow-Guided Unsupervised Monocular Depth Estimation
The great potential of unsupervised monocular depth estimation has been demonstrated by many works due to low annotation cost and impressive accuracy comparable to supervised methods.
Terminology-aware Medical Dialogue Generation
In this paper, we propose a novel framework to improve medical dialogue generation by considering features centered on domain-specific terminology.
GenLoco: Generalized Locomotion Controllers for Quadrupedal Robots
In this work, we introduce a framework for training generalized locomotion (GenLoco) controllers for quadrupedal robots.
Up-to-Down Network: Fusing Multi-Scale Context for 3D Semantic Scene Completion
An efficient 3D scene perception algorithm is a vital component for autonomous driving and robotics systems.
Ranked #7 on
3D Semantic Scene Completion
on SemanticKITTI
Semantic Segmentation-assisted Scene Completion for LiDAR Point Clouds
Specifically, the network takes a raw point cloud as input, and merges the features from the segmentation branch into the completion branch hierarchically to provide semantic information.
Ranked #5 on
3D Semantic Scene Completion
on SemanticKITTI
SA-LOAM: Semantic-aided LiDAR SLAM with Loop Closure
LiDAR-based SLAM system is admittedly more accurate and stable than others, while its loop closure detection is still an open issue.
Uncertainty-aware INVASE: Enhanced Breast Cancer Diagnosis Feature Selection
In this paper, we present an uncertainty-aware INVASE to quantify predictive confidence of healthcare problem.
Open-set Intersection Intention Prediction for Autonomous Driving
We capture map-centric features that correspond to intersection structures under a spatial-temporal graph representation, and use two MAAMs (mutually auxiliary attention module) that cover respectively lane-level and exitlevel intentions to predict a target that best matches intersection elements in map-centric feature space.
SMARTS: Scalable Multi-Agent Reinforcement Learning Training School for Autonomous Driving
We open-source the SMARTS platform and the associated benchmark tasks and evaluation metrics to encourage and empower research on multi-agent learning for autonomous driving.
Triple-GAIL: A Multi-Modal Imitation Learning Framework with Generative Adversarial Nets
Generative adversarial imitation learning (GAIL) has shown promising results by taking advantage of generative adversarial nets, especially in the field of robot learning.
