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Found 24 papers, 11 papers with code
A comprehensive review of remote sensing in wetland classification and mapping
Although some review articles summarized the development of this field, there is a lack of a thorough and in-depth understanding of wetland classification and mapping: (1) the scientific importance of wetlands, (2) major data, methods used in wetland classification and mapping, (3) driving factors of wetland changes, (4) current research paradigm and limitations, (5) challenges and opportunities in wetland classification and mapping under the context of technological innovation and global environmental change.
GPBench: A Comprehensive and Fine-Grained Benchmark for Evaluating Large Language Models as General Practitioners
To evaluate how effectively LLMs can make decisions in the daily work of GPs, we designed GPBench, which consists of both test questions from clinical practice and a novel evaluation framework.
Speculative MoE: Communication Efficient Parallel MoE Inference with Speculative Token and Expert Pre-scheduling
MoE (Mixture of Experts) prevails as a neural architecture that can scale modern transformer-based LLMs (Large Language Models) to unprecedented scales.
Deep Learning-Enhanced Visual Monitoring in Hazardous Underwater Environments with a Swarm of Micro-Robots
In this paper, we propose a novel approach that integrates data simulation, a multi-modal deep learning network for coordinate prediction, and image reassembly to address the challenges posed by environmental disturbances causing drift and rotation in the robots' positions and orientations.
ThinkBench: Dynamic Out-of-Distribution Evaluation for Robust LLM Reasoning
Evaluating large language models (LLMs) poses significant challenges, particularly due to issues of data contamination and the leakage of correct answers.
VidSketch: Hand-drawn Sketch-Driven Video Generation with Diffusion Control
With the advancement of generative artificial intelligence, previous studies have achieved the task of generating aesthetic images from hand-drawn sketches, fulfilling the public's needs for drawing.
HuViDPO:Enhancing Video Generation through Direct Preference Optimization for Human-Centric Alignment
3) We adopt a First-Frame-Conditioned strategy, leveraging the rich in formation from the first frame to guide the generation of subsequent frames, enhancing flexibility in video generation.
Artificial Intelligence for Geometry-Based Feature Extraction, Analysis and Synthesis in Artistic Images: A Survey
Artificial Intelligence significantly enhances the visual art industry by analyzing, identifying and generating digitized artistic images.
SEM-Net: Efficient Pixel Modelling for image inpainting with Spatially Enhanced SSM
Image inpainting aims to repair a partially damaged image based on the information from known regions of the images.
Hunyuan-Large: An Open-Source MoE Model with 52 Billion Activated Parameters by Tencent
In this paper, we introduce Hunyuan-Large, which is currently the largest open-source Transformer-based mixture of experts model, with a total of 389 billion parameters and 52 billion activation parameters, capable of handling up to 256K tokens.
One-Index Vector Quantization Based Adversarial Attack on Image Classification
In this paper, we propose a novel one-index attack method in the VQ domain to generate adversarial images by a differential evolution algorithm, successfully resulting in image misclassification in victim models.
MxT: Mamba x Transformer for Image Inpainting
Image inpainting, or image completion, is a crucial task in computer vision that aims to restore missing or damaged regions of images with semantically coherent content.
Depth-Aware Endoscopic Video Inpainting
Video inpainting fills in corrupted video content with plausible replacements.
Geometric Features Enhanced Human-Object Interaction Detection
Human-object interaction (HOI) detection is one of the most popular pattern detection approaches for captured human-centric visual scenes.
Taking a Deep Breath: Enhancing Language Modeling of Large Language Models with Sentinel Tokens
We then modify the attention mask to integrate the chunk's information into the corresponding <SR> token.
FUSU: A Multi-temporal-source Land Use Change Segmentation Dataset for Fine-grained Urban Semantic Understanding
Although there have been advances in high-quality land cover datasets that reveal the physical features of urban landscapes, the lack of fine-grained land use datasets hinders a deeper understanding of how human activities are distributed across the landscape and the impact of these activities on the environment, thus constraining proper technique development.
Fact :Teaching MLLMs with Faithful, Concise and Transferable Rationales
Their ability to execute intricate compositional reasoning tasks is also constrained, culminating in a stagnation of learning progression for these models.
HINT: High-quality INPainting Transformer with Mask-Aware Encoding and Enhanced Attention
In this paper, we propose an end-to-end High-quality INpainting Transformer, abbreviated as HINT, which consists of a novel mask-aware pixel-shuffle downsampling module (MPD) to preserve the visible information extracted from the corrupted image while maintaining the integrity of the information available for high-level inferences made within the model.
INCLG: Inpainting for Non-Cleft Lip Generation with a Multi-Task Image Processing Network
We present a software that predicts non-cleft facial images for patients with cleft lip, thereby facilitating the understanding, awareness and discussion of cleft lip surgeries.
A Feasibility Study on Image Inpainting for Non-cleft Lip Generation from Patients with Cleft Lip
A Cleft lip is a congenital abnormality requiring surgical repair by a specialist.
Rows from Many Sources: Enriching row completions from Wikidata with a pre-trained Language Model
The task divides into two steps: subject suggestion, the task of populating the main column; and gap filling, the task of populating the remaining columns.
ReTraCk: A Flexible and Efficient Framework for Knowledge Base Question Answering
We present Retriever-Transducer-Checker (ReTraCk), a neural semantic parsing framework for large scale knowledge base question answering (KBQA).
Ranked #1 on
Knowledge Base Question Answering
on GrailQA
Improving Entity Linking by Modeling Latent Entity Type Information
Existing state of the art neural entity linking models employ attention-based bag-of-words context model and pre-trained entity embeddings bootstrapped from word embeddings to assess topic level context compatibility.
Ranked #2 on
Entity Disambiguation
on AIDA-CoNLL
(Micro-F1 metric)
Enhancing Neural Data-To-Text Generation Models with External Background Knowledge
Recent neural models for data-to-text generation rely on massive parallel pairs of data and text to learn the writing knowledge.
