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Found 25 papers, 16 papers with code
一种非结构化数据表征增强的术后风险预测模型(An Unstructured Data Representation Enhanced Model for Postoperative Risk Prediction)
“准确的术后风险预测对临床资源规划和应急方案准备以及降低患者的术后风险和死亡率具有积极作用。术后风险预测目前主要基于术前和术中的患者基本信息、实验室检查、生命体征等结构化数据, 而蕴含丰富语义信息的非结构化术前诊断的价值还有待验证。针对该问题, 本文提出一种非结构化数据表征增强的术后风险预测模型, 利用自注意力机制, 精巧的将结构化数据与术前诊断数据进行信息加权融合。基于临床数据, 将本文方法与术后风险预测常用的统计机器学习模型以及最新的深度神经网络进行对比, 本文方法不仅提升了术后风险预测的性能, 同时也为预测模型带来了良好的可解释性。”
SPMamba: State-space model is all you need in speech separation
Notably, within computer vision, Mamba-based methods have been celebrated for their formidable performance and reduced computational requirements.
EgoExoLearn: A Dataset for Bridging Asynchronous Ego- and Exo-centric View of Procedural Activities in Real World
Along with the videos we record high-quality gaze data and provide detailed multimodal annotations, formulating a playground for modeling the human ability to bridge asynchronous procedural actions from different viewpoints.
InternVideo2: Scaling Video Foundation Models for Multimodal Video Understanding
We introduce InternVideo2, a new video foundation model (ViFM) that achieves the state-of-the-art performance in action recognition, video-text tasks, and video-centric dialogue.
Ranked #1 on
Audio Classification
on ESC-50
(using extra training data)
Video Mamba Suite: State Space Model as a Versatile Alternative for Video Understanding
We categorize Mamba into four roles for modeling videos, deriving a Video Mamba Suite composed of 14 models/modules, and evaluating them on 12 video understanding tasks.
Ranked #1 on
Temporal Action Localization
on FineAction
Retrieval-Augmented Egocentric Video Captioning
In this paper, (1) we develop EgoInstructor, a retrieval-augmented multimodal captioning model that automatically retrieves semantically relevant third-person instructional videos to enhance the video captioning of egocentric videos.
InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks
However, the progress in vision and vision-language foundation models, which are also critical elements of multi-modal AGI, has not kept pace with LLMs.
Ranked #1 on
Zero-Shot Video Retrieval
on MSR-VTT-full
(using extra training data)
Decoupling SQL Query Hardness Parsing for Text-to-SQL
This framework decouples the Text-to-SQL task based on query hardness by analyzing questions and schemas, simplifying the multi-hardness task into a single-hardness challenge.
MVBench: A Comprehensive Multi-modal Video Understanding Benchmark
With the rapid development of Multi-modal Large Language Models (MLLMs), a number of diagnostic benchmarks have recently emerged to evaluate the comprehension capabilities of these models.
Ranked #1 on
Zero-Shot Video Question Answer
on STAR Benchmark
Memory-and-Anticipation Transformer for Online Action Understanding
Based on this idea, we present Memory-and-Anticipation Transformer (MAT), a memory-anticipation-based approach, to address the online action detection and anticipation tasks.
Ranked #1 on
Action Detection
on THUMOS' 14
InternVid: A Large-scale Video-Text Dataset for Multimodal Understanding and Generation
Specifically, we utilize a multi-scale approach to generate video-related descriptions.
AVSegFormer: Audio-Visual Segmentation with Transformer
In this paper, we propose AVSegFormer, a novel framework for AVS tasks that leverages the transformer architecture.
VideoLLM: Modeling Video Sequence with Large Language Models
Building upon this insight, we propose a novel framework called VideoLLM that leverages the sequence reasoning capabilities of pre-trained LLMs from natural language processing (NLP) for video sequence understanding.
MRSN: Multi-Relation Support Network for Video Action Detection
Action detection is a challenging video understanding task, requiring modeling spatio-temporal and interaction relations.
Champion Solution for the WSDM2023 Toloka VQA Challenge
In this report, we present our champion solution to the WSDM2023 Toloka Visual Question Answering (VQA) Challenge.
InternVideo: General Video Foundation Models via Generative and Discriminative Learning
Specifically, InternVideo efficiently explores masked video modeling and video-language contrastive learning as the pretraining objectives, and selectively coordinates video representations of these two complementary frameworks in a learnable manner to boost various video applications.
Ranked #1 on
Action Recognition
on Something-Something V1
(using extra training data)
InternVideo-Ego4D: A Pack of Champion Solutions to Ego4D Challenges
In this report, we present our champion solutions to five tracks at Ego4D challenge.
Ranked #1 on
State Change Object Detection
on Ego4D
Exploring adaptation of VideoMAE for Audio-Visual Diarization & Social @ Ego4d Looking at me Challenge
In this report, we present the transferring pretrained video mask autoencoders(VideoMAE) to egocentric tasks for Ego4d Looking at me Challenge.
Exploring State Change Capture of Heterogeneous Backbones @ Ego4D Hands and Objects Challenge 2022
Our method achieves an accuracy of 0. 796 on OSCC while achieving an absolute temporal localization error of 0. 516 on PNR.
BasicTAD: an Astounding RGB-Only Baseline for Temporal Action Detection
Empirical results demonstrate that our PlusTAD is very efficient and significantly outperforms the previous methods on the datasets of THUMOS14 and FineAction.
Ranked #1 on
Temporal Action Localization
on THUMOS14
Optically-generated focused ultrasound for noninvasive brain stimulation with ultrahigh precision
Here, we report optically-generated focused ultrasound (OFUS) for non-invasive brain stimulation with ultrahigh precision.
DCAN: Improving Temporal Action Detection via Dual Context Aggregation
Specifically, we design the Multi-Path Temporal Context Aggregation (MTCA) to achieve smooth context aggregation on boundary level and precise evaluation of boundaries.
Ranked #19 on
Temporal Action Localization
on ActivityNet-1.3
FAST: Faster Arbitrarily-Shaped Text Detector with Minimalist Kernel Representation
We propose an accurate and efficient scene text detection framework, termed FAST (i. e., faster arbitrarily-shaped text detector).
Ranked #2 on
Scene Text Detection
on MSRA-TD500
Word-Free Spoken Language Understanding for Mandarin-Chinese
Spoken dialogue systems such as Siri and Alexa provide great convenience to people's everyday life.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+6
Non-genetic acoustic stimulation of single neurons by a tapered fiber optoacoustic emitter
As an emerging technology, transcranial focused ultrasound has been demonstrated to successfully evoke motor responses in mice, rabbits, and sensory/motor responses in humans.
