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Found 49 papers, 26 papers with code
DocEE: A Large-Scale and Fine-grained Benchmark for Document-level Event Extraction
Event extraction aims to identify an event and then extract the arguments participating in the event.
SRA-MCTS: Self-driven Reasoning Augmentation with Monte Carlo Tree Search for Code Generation
Large language models demonstrate exceptional performance in simple code generation tasks but still face challenges in tackling complex problems.
Constraint Back-translation Improves Complex Instruction Following of Large Language Models
Following the conventional instruction-tuning practice, previous works conduct post-training on complex instruction-response pairs generated by feeding complex instructions to advanced LLMs.
VisScience: An Extensive Benchmark for Evaluating K12 Educational Multi-modal Scientific Reasoning
To address this gap, we meticulously construct a comprehensive benchmark, named VisScience, which is utilized to assess the multi-modal scientific reasoning across the three disciplines of mathematics, physics, and chemistry.
MathGLM-Vision: Solving Mathematical Problems with Multi-Modal Large Language Model
Large language models (LLMs) have demonstrated significant capabilities in mathematical reasoning, particularly with text-based mathematical problems.
CogVLM2: Visual Language Models for Image and Video Understanding
Beginning with VisualGLM and CogVLM, we are continuously exploring VLMs in pursuit of enhanced vision-language fusion, efficient higher-resolution architecture, and broader modalities and applications.
Ranked #10 on
Visual Question Answering
on MM-Vet
CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer
We present CogVideoX, a large-scale text-to-video generation model based on diffusion transformer, which can generate 10-second continuous videos aligned with text prompt, with a frame rate of 16 fps and resolution of 768 * 1360 pixels.
MAVEN-Fact: A Large-scale Event Factuality Detection Dataset
Thanks to the comprehensive annotations of event arguments and relations in MAVEN, MAVEN-Fact also supports some further analyses and we find that adopting event arguments and relations helps in event factuality detection for fine-tuned models but does not benefit LLMs.
LVBench: An Extreme Long Video Understanding Benchmark
To address this gap, we introduce LVBench, a benchmark specifically designed for long video understanding.
ADELIE: Aligning Large Language Models on Information Extraction
Large language models (LLMs) usually fall short on information extraction (IE) tasks and struggle to follow the complex instructions of IE tasks.
Does Negative Sampling Matter? A Review with Insights into its Theory and Applications
Is there a general framework that can incorporate all existing negative sampling methods?
CogCoM: Train Large Vision-Language Models Diving into Details through Chain of Manipulations
Drawing inspiration from human cognition in solving visual problems (e. g., marking, zoom in), this paper introduces Chain of Manipulations, a mechanism that enables VLMs to solve problems step-by-step with evidence.
CogAgent: A Visual Language Model for GUI Agents
People are spending an enormous amount of time on digital devices through graphical user interfaces (GUIs), e. g., computer or smartphone screens.
Ranked #4 on
on
When does In-context Learning Fall Short and Why? A Study on Specification-Heavy Tasks
In this paper, we find that ICL falls short of handling specification-heavy tasks, which are tasks with complicated and extensive task specifications, requiring several hours for ordinary humans to master, such as traditional information extraction tasks.
CogVLM: Visual Expert for Pretrained Language Models
We introduce CogVLM, a powerful open-source visual language foundation model.
Ranked #4 on
Visual Question Answering (VQA)
on InfiMM-Eval
Lookup Table meets Local Laplacian Filter: Pyramid Reconstruction Network for Tone Mapping
Furthermore, we utilize local Laplacian filters to refine the edge details in the high-frequency components in an adaptive manner.
Mastering the Task of Open Information Extraction with Large Language Models and Consistent Reasoning Environment
Open Information Extraction (OIE) aims to extract objective structured knowledge from natural texts, which has attracted growing attention to build dedicated models with human experience.
VidCoM: Fast Video Comprehension through Large Language Models with Multimodal Tools
Building models that comprehends videos and responds specific user instructions is a practical and challenging topic, as it requires mastery of both vision understanding and knowledge reasoning.
ViLTA: Enhancing Vision-Language Pre-training through Textual Augmentation
Vision-language pre-training (VLP) methods are blossoming recently, and its crucial goal is to jointly learn visual and textual features via a transformer-based architecture, demonstrating promising improvements on a variety of vision-language tasks.
Towards General Low-Light Raw Noise Synthesis and Modeling
To address this issue, we introduce a new perspective to synthesize the signal-independent noise by a generative model.
Ranked #2 on
Image Denoising
on SID SonyA7S2 x300
KoLA: Carefully Benchmarking World Knowledge of Large Language Models
The unprecedented performance of large language models (LLMs) necessitates improvements in evaluations.
Preserving Knowledge Invariance: Rethinking Robustness Evaluation of Open Information Extraction
In this paper, we present the first benchmark that simulates the evaluation of open information extraction models in the real world, where the syntactic and expressive distributions under the same knowledge meaning may drift variously.
GOAL: A Challenging Knowledge-grounded Video Captioning Benchmark for Real-time Soccer Commentary Generation
Despite the recent emergence of video captioning models, how to generate vivid, fine-grained video descriptions based on the background knowledge (i. e., long and informative commentary about the domain-specific scenes with appropriate reasoning) is still far from being solved, which however has great applications such as automatic sports narrative.
Syntactically Robust Training on Partially-Observed Data for Open Information Extraction
In this paper, we propose a syntactically robust training framework that enables models to be trained on a syntactic-abundant distribution based on diverse paraphrase generation.
EDUKG: a Heterogeneous Sustainable K-12 Educational Knowledge Graph
To tackle these issues, we propose EDUKG, a heterogeneous sustainable K-12 Educational Knowledge Graph.
ConstGCN: Constrained Transmission-based Graph Convolutional Networks for Document-level Relation Extraction
Document-level relation extraction with graph neural networks faces a fundamental graph construction gap between training and inference - the golden graph structure only available during training, which causes that most methods adopt heuristic or syntactic rules to construct a prior graph as a pseudo proxy.
DSCA: A Dual-Stream Network with Cross-Attention on Whole-Slide Image Pyramids for Cancer Prognosis
Our experiments and ablation studies verify that (i) the proposed DSCA could outperform existing state-of-the-art methods in cancer prognosis, by an average C-Index improvement of around 4. 6%; (ii) our DSCA network is more efficient in computation -- it has more learnable parameters (6. 31M vs. 860. 18K) but less computational costs (2. 51G vs. 4. 94G), compared to a typical existing multi-resolution network.
A Critical Analysis of Image-based Camera Pose Estimation Techniques
Camera, and associated with its objects within the field of view, localization could benefit many computer vision fields, such as autonomous driving, robot navigation, and augmented reality (AR).
OpenQA: Hybrid QA System Relying on Structured Knowledge Base as well as Non-structured Data
Search engines based on keyword retrieval can no longer adapt to the way of information acquisition in the era of intelligent Internet of Things due to the return of keyword related Internet pages.
Improving Low-resource Reading Comprehension via Cross-lingual Transposition Rethinking
To address this issue, we propose a Cross-Lingual Transposition ReThinking (XLTT) model by modelling existing high-quality extractive reading comprehension datasets in a multilingual environment.
PatentMiner: Patent Vacancy Mining via Context-enhanced and Knowledge-guided Graph Attention
In this paper, we propose a new patent vacancy prediction approach named PatentMiner to mine rich semantic knowledge and predict new potential patents based on knowledge graph (KG) and graph attention mechanism.
Learning from Miscellaneous Other-Class Words for Few-shot Named Entity Recognition
Few-shot Named Entity Recognition (NER) exploits only a handful of annotations to identify and classify named entity mentions.
Bilateral Grid Learning for Stereo Matching Networks
In this paper, we present a novel edge-preserving cost volume upsampling module based on the slicing operation in the learned bilateral grid.
A Multilingual Modeling Method for Span-Extraction Reading Comprehension
In this paper, in order to solve the scarce availability of extractive reading comprehension training data in the target language, we propose a multilingual extractive reading comprehension approach called XLRC by simultaneously modeling the existing extractive reading comprehension training data in a multilingual environment using self-adaptive attention and multilingual attention.
DiaKG: an Annotated Diabetes Dataset for Medical Knowledge Graph Construction
In order to accelerate the research for domain-specific knowledge graphs in the medical domain, we introduce DiaKG, a high-quality Chinese dataset for Diabetes knowledge graph, which contains 22, 050 entities and 6, 890 relations in total.
A Cloud-Based Energy Management Strategy for Hybrid Electric City Bus Considering Real-Time Passenger Load Prediction
The Gradient Boost Decision Tree model is selected due to its best accuracy and high stability.
Energy Consumption and Battery Aging Minimization Using a Q-learning Strategy for a Battery/Ultracapacitor Electric Vehicle
Then, the result from a vehicle without ultracapacitor is used as the baseline, which is compared with the results from the vehicle with ultracapacitor using Q-learning, and two heuristic methods as the energy management strategies.
Learning Time Reduction Using Warm Start Methods for a Reinforcement Learning Based Supervisory Control in Hybrid Electric Vehicle Applications
This study aims to reduce the learning iterations of Q-learning in HEV application and improve fuel consumption in initial learning phases utilizing warm start methods.
Improving Event Detection via Open-domain Trigger Knowledge
Event Detection (ED) is a fundamental task in automatically structuring texts.
Model reconstruction from temporal data for coupled oscillator networks
In a complex system, the interactions between individual agents often lead to emergent collective behavior like spontaneous synchronization, swarming, and pattern formation.
Convergence analysis of beetle antennae search algorithm and its applications
The beetle antennae search algorithm was recently proposed and investigated for solving global optimization problems.
Stitching Videos from a Fisheye Lens Camera and a Wide-Angle Lens Camera for Telepresence Robots
In this paper, we propose to stitch videos from the FF-camera with a wide-angle lens and the DF-camera with a fisheye lens for telepresence robots.
Magic numbers in polymer phase separation -- the importance of being rigid
One class of such condensates is composed of two polymer species, where each consists of repeated binding sites that interact in a one-to-one fashion with the binding sites of the other polymer.
Biological Physics Soft Condensed Matter Subcellular Processes
Multi-Level Fusion Based 3D Object Detection From Monocular Images
In this paper, we present an end-to-end deep learning based framework for 3D object detection from a single monocular image.
Ranked #12 on
Vehicle Pose Estimation
on KITTI Cars Hard
3D Object Detection
3D Object Detection From Monocular Images
+4
Robustness of Maximum Correntropy Estimation Against Large Outliers
The maximum correntropy criterion (MCC) has recently been successfully applied in robust regression, classification and adaptive filtering, where the correntropy is maximized instead of minimizing the well-known mean square error (MSE) to improve the robustness with respect to outliers (or impulsive noises).
Maximum Correntropy Unscented Filter
To improve the robustness of the UKF against impulsive noises, a new filter for nonlinear systems is proposed in this work, namely the maximum correntropy unscented filter (MCUF).
Kernel Risk-Sensitive Loss: Definition, Properties and Application to Robust Adaptive Filtering
Nonlinear similarity measures defined in kernel space, such as correntropy, can extract higher-order statistics of data and offer potentially significant performance improvement over their linear counterparts especially in non-Gaussian signal processing and machine learning.
Social cycling and conditional responses in the Rock-Paper-Scissors game
How humans make decisions in non-cooperative strategic interactions is a challenging question.
Physics and Society Computer Science and Game Theory
