Search Results for author:
Found 7 papers, 1 papers with code
CoReS: Orchestrating the Dance of Reasoning and Segmentation
We believe that the act of reasoning segmentation should mirror the cognitive stages of human visual search, where each step is a progressive refinement of thought toward the final object.
DriveDreamer-2: LLM-Enhanced World Models for Diverse Driving Video Generation
DriveDreamer-2 is the first world model to generate customized driving videos, it can generate uncommon driving videos (e. g., vehicles abruptly cut in) in a user-friendly manner.
Understanding the Multi-modal Prompts of the Pre-trained Vision-Language Model
In this paper, we conduct a direct analysis of the multi-modal prompts by asking the following questions: $(i)$ How do the learned multi-modal prompts improve the recognition performance?
Relevant Intrinsic Feature Enhancement Network for Few-Shot Semantic Segmentation
To improve the semantic consistency of foreground instances, we propose an unlabeled branch as an efficient data utilization method, which teaches the model how to extract intrinsic features robust to intra-class differences.
Opinion Tree Parsing for Aspect-based Sentiment Analysis
To address these challenges, we propose an opinion tree parsing model, aiming to parse all the sentiment elements from an opinion tree, which is much faster, and can explicitly reveal a more comprehensive and complete aspect-level sentiment structure.
Aspect-based Sentiment Analysis with Opinion Tree Generation
Furthermore, we propose a pre-trained model to integrate both syntax and semantic features for opinion tree generation.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
Building Interpretable Interaction Trees for Deep NLP Models
This paper proposes a method to disentangle and quantify interactions among words that are encoded inside a DNN for natural language processing.
