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Found 20 papers, 11 papers with code
APSeg: Auto-Prompt Model with Acquired and Injected Knowledge for Nuclear Instance Segmentation and Classification
Therefore, we focus on generating prompts with more accurate localization and classification and propose \textbf{APSeg}, \textbf{A}uto-\textbf{P}rompt model with acquired and injected knowledge for nuclear instance \textbf{Seg}mentation and classification.
Instance Migration Diffusion for Nuclear Instance Segmentation in Pathology
To tackle this challenge, we propose a novel data augmentation framework Instance Migration Diffusion Model (IM-Diffusion), IM-Diffusion designed to generate more varied pathological images by constructing diverse nuclear layouts and internuclear spatial relationships.
MaintAGT:Sim2Real-Guided Multimodal Large Model for Intelligent Maintenance with Chain-of-Thought Reasoning
In recent years, large language models have made significant advancements in the field of natural language processing, yet there are still inadequacies in specific domain knowledge and applications.
OpenWebVoyager: Building Multimodal Web Agents via Iterative Real-World Exploration, Feedback and Optimization
In this paper, we introduce an open-source framework designed to facilitate the development of multimodal web agent that can autonomously conduct real-world exploration and improve itself.
VSLLaVA: a pipeline of large multimodal foundation model for industrial vibration signal analysis
This paper presents a pipeline named VSLLaVA that leverages a large language model to integrate expert knowledge for identification of signal parameters and diagnosis of faults.
Facilitating Pornographic Text Detection for Open-Domain Dialogue Systems via Knowledge Distillation of Large Language Models
Pornographic content occurring in human-machine interaction dialogues can cause severe side effects for users in open-domain dialogue systems.
WebVoyager: Building an End-to-End Web Agent with Large Multimodal Models
The rapid advancement of large language models (LLMs) has led to a new era marked by the development of autonomous applications in real-world scenarios, which drives innovation in creating advanced web agents.
ConceptPsy:A Benchmark Suite with Conceptual Comprehensiveness in Psychology
The critical field of psychology necessitates a comprehensive benchmark to enhance the evaluation and development of domain-specific Large Language Models (LLMs).
Facilitating NSFW Text Detection in Open-Domain Dialogue Systems via Knowledge Distillation
NSFW (Not Safe for Work) content, in the context of a dialogue, can have severe side effects on users in open-domain dialogue systems.
A Benchmark for Understanding Dialogue Safety in Mental Health Support
Our study reveals that ChatGPT struggles to detect safety categories with detailed safety definitions in a zero- and few-shot paradigm, whereas the fine-tuned model proves to be more suitable.
Latent Jailbreak: A Benchmark for Evaluating Text Safety and Output Robustness of Large Language Models
We present a systematic analysis of the safety and robustness of LLMs regarding the position of explicit normal instructions, word replacements (verbs in explicit normal instructions, target groups in malicious instructions, cue words for explicit normal instructions), and instruction replacements (different explicit normal instructions).
Understanding Client Reactions in Online Mental Health Counseling
Communication success relies heavily on reading participants' reactions.
Instance Smoothed Contrastive Learning for Unsupervised Sentence Embedding
Contrastive learning-based methods, such as unsup-SimCSE, have achieved state-of-the-art (SOTA) performances in learning unsupervised sentence embeddings.
SMILE: Single-turn to Multi-turn Inclusive Language Expansion via ChatGPT for Mental Health Support
Further, we employ our method to generate a large-scale, lifelike, and diverse dialogue dataset named SMILECHAT, consisting of 55k dialogues.
Robust Secrecy via Aerial Reflection and Jamming: Joint Optimization of Deployment and Transmission
The security gain with aerial reflection and jamming is further improved with the optimized deployment of the aerial platform.
TopoSeg: Topology-Aware Nuclear Instance Segmentation
Experiments on three nuclear instance segmentation datasets justify the superiority of TopoSeg, which achieves state-of-the-art performance.
Towards Automated Real-time Evaluation in Text-based Counseling
However, these methods often demand large scale and high quality counseling data, which are difficult to collect.
Uni-Encoder: A Fast and Accurate Response Selection Paradigm for Generation-Based Dialogue Systems
The current state-of-the-art ranking methods mainly use an encoding paradigm called Cross-Encoder, which separately encodes each context-candidate pair and ranks the candidates according to their fitness scores.
Ranked #1 on
Conversational Response Selection
on Persona-Chat
CDNet: Centripetal Direction Network for Nuclear Instance Segmentation
Specifically, we define the centripetal direction feature as a class of adjacent directions pointing to the nuclear center to represent the spatial relationship between pixels within the nucleus.
Unsupervised Multi-Domain Image Translation with Domain-Specific Encoders/Decoders
Unsupervised Image-to-Image Translation achieves spectacularly advanced developments nowadays.
