Search Results for author:
Found 20 papers, 8 papers with code
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.
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
Large vision-language models (VLMs) have achieved substantial progress in multimodal perception and reasoning.
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.
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.
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.
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.
