Search Results for author:
Found 47 papers, 23 papers with code
The Four Color Theorem for Cell Instance Segmentation
In this paper, we propose a novel cell instance segmentation method inspired by the four-color theorem.
Visual Abstract Thinking Empowers Multimodal Reasoning
Images usually convey richer detail than text, but often include redundant information which potentially downgrades multimodal reasoning performance.
TextFlux: An OCR-Free DiT Model for High-Fidelity Multilingual Scene Text Synthesis
TextFlux is effective in low-resource multilingual settings, and achieves strong performance in newly added languages with fewer than 1, 000 samples.
ReSurgSAM2: Referring Segment Anything in Surgical Video via Credible Long-term Tracking
In this paper, we introduce ReSurgSAM2, a two-stage surgical referring segmentation framework that leverages Segment Anything Model 2 to perform text-referred target detection, followed by tracking with reliable initial frame identification and diversity-driven long-term memory.
Robust Beamforming Design for STAR-RIS Aided RSMA Network with Hardware Impairments
In this article, we investigate the robust beamforming design for a simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) aided downlink rate-splitting multiple access (RSMA) communication system, where both transceivers and STAR-RIS suffer from the impact of hardware impairments (HWI).
Evaluating Frontier Models for Stealth and Situational Awareness
Recent work has demonstrated the plausibility of frontier AI models scheming -- knowingly and covertly pursuing an objective misaligned with its developer's intentions.
MedAgent-Pro: Towards Evidence-based Multi-modal Medical Diagnosis via Reasoning Agentic Workflow
In modern medicine, clinical diagnosis relies on the comprehensive analysis of primarily textual and visual data, drawing on medical expertise to ensure systematic and rigorous reasoning.
CoSpace: Benchmarking Continuous Space Perception Ability for Vision-Language Models
In this paper, we present CoSpace, a multi-image visual understanding benchmark designed to assess the Continuous Space perception ability for VLMs.
BEVDiffLoc: End-to-End LiDAR Global Localization in BEV View based on Diffusion Model
Bird's-Eye-View (BEV) image is one of the most widely adopted data representations in autonomous driving.
SurgRAW: Multi-Agent Workflow with Chain-of-Thought Reasoning for Surgical Intelligence
Integration of Vision-Language Models (VLMs) in surgical intelligence is hindered by hallucinations, domain knowledge gaps, and limited understanding of task interdependencies within surgical scenes, undermining clinical reliability.
How Do Multimodal Large Language Models Handle Complex Multimodal Reasoning? Placing Them in An Extensible Escape Game
However, existing evaluations primarily assess the final task completion, often degrading assessments to isolated abilities such as visual grounding and visual question answering.
DongbaMIE: A Multimodal Information Extraction Dataset for Evaluating Semantic Understanding of Dongba Pictograms
To this end, we propose DongbaMIE, the first multimodal dataset for semantic understanding and extraction of Dongba pictographs.
EgoLife: Towards Egocentric Life Assistant
This effort resulted in the EgoLife Dataset, a comprehensive 300-hour egocentric, interpersonal, multiview, and multimodal daily life dataset with intensive annotation.
Perspective Transition of Large Language Models for Solving Subjective Tasks
Large language models (LLMs) have revolutionized the field of natural language processing, enabling remarkable progress in various tasks.
ActiView: Evaluating Active Perception Ability for Multimodal Large Language Models
Active perception, a crucial human capability, involves setting a goal based on the current understanding of the environment and performing actions to achieve that goal.
Multimodal Generalized Category Discovery
Generalized Category Discovery (GCD) aims to classify inputs into both known and novel categories, a task crucial for open-world scientific discoveries.
Research and Design of a Financial Intelligent Risk Control Platform Based on Big Data Analysis and Deep Machine Learning
The core objective of this article is to explore how to fully utilize big data technology to achieve complete integration of internal and external data of financial institutions, and create an efficient and reliable platform for big data collection, storage, and analysis.
Innovative RIS Prototyping Enhancing Wireless Communication with Real-Time Spot Beam Tracking and OAM Wavefront Manipulation
Owing to the capability of each unit cell on the metasurface to independently switch states, the entire RIS is not limited to controlling general beams with specific directional patterns, but also generates beams with more complex structures, including multi-focus 3D spot beams and vortex beams.
Graph-Structured Speculative Decoding
We apply GSD across a range of LLMs, including a 70-billion parameter LLaMA-2 model, and observe a remarkable speedup of 1. 73$\times$ to 1. 96$\times$, significantly surpassing standard speculative decoding.
SegNet4D: Efficient Instance-Aware 4D Semantic Segmentation for LiDAR Point Cloud
In this paper, we introduce SegNet4D, a novel real-time 4D semantic segmentation network offering both efficiency and strong semantic understanding.
Dynamic Pseudo Label Optimization in Point-Supervised Nuclei Segmentation
Deep learning has achieved impressive results in nuclei segmentation, but the massive requirement for pixel-wise labels remains a significant challenge.
Long Context Transfer from Language to Vision
By simply extrapolating the context length of the language backbone, we enable LMMs to comprehend orders of magnitude more visual tokens without any video training.
Ranked #9 on
Visual Question Answering (VQA)
on VLM2-Bench
An Effective Software Risk Prediction Management Analysis of Data Using Machine Learning and Data Mining Method
The results of our experiments show that, when compared to other current methods, our integrative fuzzy techniques may perform more accurately and effectively in the evaluation of software project risks.
SVASTIN: Sparse Video Adversarial Attack via Spatio-Temporal Invertible Neural Networks
Robust and imperceptible adversarial video attack is challenging due to the spatial and temporal characteristics of videos.
MGI: Multimodal Contrastive pre-training of Genomic and Medical Imaging
We pre-trained on the TCGA dataset using paired gene expression data and imaging data, and fine-tuned it for downstream tumor segmentation tasks.
Explainable Interface for Human-Autonomy Teaming: A Survey
Second, we contribute to a novel framework for EI, addressing the unique challenges in HAT.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
+1
DAWN: Domain-Adaptive Weakly Supervised Nuclei Segmentation via Cross-Task Interactions
However, the current weakly supervised nuclei segmentation approaches typically follow a two-stage pseudo-label generation and network training process.
Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context
In this report, we introduce the Gemini 1. 5 family of models, representing the next generation of highly compute-efficient multimodal models capable of recalling and reasoning over fine-grained information from millions of tokens of context, including multiple long documents and hours of video and audio.
Ranked #1 on
Zero-Shot Video Question Answer
on Zero-shot Video Question Answering on LongVideoBench
(using extra training data)
CODIS: Benchmarking Context-Dependent Visual Comprehension for Multimodal Large Language Models
Multimodal large language models (MLLMs) have demonstrated promising results in a variety of tasks that combine vision and language.
Model Composition for Multimodal Large Language Models
In this paper, we propose a new paradigm through the model composition of existing MLLMs to create a new model that retains the modal understanding capabilities of each original model.
Browse and Concentrate: Comprehending Multimodal Content via prior-LLM Context Fusion
With the bloom of Large Language Models (LLMs), Multimodal Large Language Models (MLLMs) that incorporate LLMs with pre-trained vision models have recently demonstrated impressive performance across diverse vision-language tasks.
Boundary-aware Contrastive Learning for Semi-supervised Nuclei Instance Segmentation
The model has two key designs: a low-resolution denoising (LRD) module and a cross-RoI contrastive learning (CRC) module.
SEINE: Structure Encoding and Interaction Network for Nuclei Instance Segmentation
Concretely, SEINE introduces a contour-based structure encoding (SE) that considers the correlation between nuclei structure and semantics, realizing a reasonable representation of the nuclei structure.
Filling the Image Information Gap for VQA: Prompting Large Language Models to Proactively Ask Questions
Large Language Models (LLMs) demonstrate impressive reasoning ability and the maintenance of world knowledge not only in natural language tasks, but also in some vision-language tasks such as open-domain knowledge-based visual question answering (OK-VQA).
Octopus: Embodied Vision-Language Programmer from Environmental Feedback
To bridge this gap, we introduce Octopus, an embodied vision-language programmer that uses executable code generation as a medium to connect planning and manipulation.
Domain generalization across tumor types, laboratories, and species -- insights from the 2022 edition of the Mitosis Domain Generalization Challenge
The challenge provided annotated histologic tumor images from six different domains and evaluated the algorithmic approaches for mitotic figure detection provided by nine challenge participants on ten independent domains.
Imperceptible Adversarial Attack via Invertible Neural Networks
Adding perturbations via utilizing auxiliary gradient information or discarding existing details of the benign images are two common approaches for generating adversarial examples.
Renmin University of China at TRECVID 2022: Improving Video Search by Feature Fusion and Negation Understanding
The 2022 edition of the TRECVID benchmark has again been a fruitful participation for the RUCMM team.
Learn to Understand Negation in Video Retrieval
We propose a learning based method for training a negation-aware video retrieval model.
InterHT: Knowledge Graph Embeddings by Interaction between Head and Tail Entities
Knowledge graph embedding (KGE) models learn the representation of entities and relations in knowledge graphs.
Ranked #4 on
Link Property Prediction
on ogbl-wikikg2
Tractable and Near-Optimal Adversarial Algorithms for Robust Estimation in Contaminated Gaussian Models
This is the first time such near-optimal error rates are established for adversarial algorithms with linear discriminators under Huber's contamination model.
Lightweight Attentional Feature Fusion: A New Baseline for Text-to-Video Retrieval
In this paper we revisit feature fusion, an old-fashioned topic, in the new context of text-to-video retrieval.
Ranked #1 on
Ad-hoc video search
on TRECVID-AVS20 (V3C1)
(using extra training data)
Accelerating Coordinate Descent via Active Set Selection for Device Activity Detection for Multi-Cell Massive Random Access
Specifically, at each iteration, the proposed active set CD algorithm first selects a small subset of all devices, namely the active set, which contains a few devices that contribute the most to the deviation from the first-order optimality condition of the MLE problem thus potentially can provide the most improvement to the objective function, then applies the CD algorithm to perform the detection for the devices in the active set.
An Efficient Active Set Algorithm for Covariance Based Joint Data and Activity Detection for Massive Random Access with Massive MIMO
Specifically, at each iteration, the proposed algorithm focuses on only a small subset of all potential sequences, namely the active set, which contains a few most likely active sequences (i. e., transmitted sequences by all active devices), and performs the detection for the sequences in the active set.
Entity Profiling in Knowledge Graphs
Traditional profiling technologies encompass a vast array of methods to find distinctive features in various applications, which can help to differentiate entities in the process of human understanding of KGs.
CJRC: A Reliable Human-Annotated Benchmark DataSet for Chinese Judicial Reading Comprehension
We present a Chinese judicial reading comprehension (CJRC) dataset which contains approximately 10K documents and almost 50K questions with answers.
IFlyLegal: A Chinese Legal System for Consultation, Law Searching, and Document Analysis
To our knowledge, IFlyLegal is the first Chinese legal system that employs up-to-date NLP techniques and caters for needs of different user groups, such as lawyers, judges, procurators, and clients.
