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Found 13 papers, 5 papers with code
TARGA: Targeted Synthetic Data Generation for Practical Reasoning over Structured Data
Then we generate corresponding natural language questions for these constructed queries to jointly serve as the synthetic demonstrations for in-context learning.
Visual Perception in Text Strings
This type of artifact possesses the unique characteristic that identical information can be readily formulated in both texts and images, making them a significant proxy for analyzing modern LLMs' and MLLMs' capabilities in modality-agnostic vision understanding.
QueryAgent: A Reliable and Efficient Reasoning Framework with Environmental Feedback-based Self-Correction
Employing Large Language Models (LLMs) for semantic parsing has achieved remarkable success.
MarkQA: A large scale KBQA dataset with numerical reasoning
We design a logic form in Python format called PyQL to represent the reasoning process of numerical reasoning questions.
Pareto Invariant Representation Learning for Multimedia Recommendation
Multimedia recommendation involves personalized ranking tasks, where multimedia content is usually represented using a generic encoder.
Statistically Profiling Biases in Natural Language Reasoning Datasets and Models
Recent work has indicated that many natural language understanding and reasoning datasets contain statistical cues that may be taken advantaged of by NLP models whose capability may thus be grossly overestimated.
AMRec: An Intelligent System for Academic Method Recommendation
Finding new academic Methods for research problems is the key task in a researcher's research career.
Learning spatiotemporal representations for human fall detection in surveillance video
Finally, we apply voting on the results of event classification to perform multicamera fall detection.
ExtRA: Extracting Prominent Review Aspects from Customer Feedback
Many existing systems for analyzing and summarizing customer reviews about products or service are based on a number of prominent review aspects.
Deep Hashing with Triplet Quantization Loss
While minimizing the quantization loss guarantees that quantization has minimal effect on retrieval accuracy, it unfortunately significantly reduces the expressiveness of features even before the quantization.
Unsupervised Triplet Hashing for Fast Image Retrieval
Considering the difficulty in obtaining labeled datasets for image retrieval task in large scale, we propose a novel CNN-based unsupervised hashing method, namely Unsupervised Triplet Hashing (UTH).
Calculating Probabilities Simplifies Word Learning
Children can use the statistical regularities of their environment to learn word meanings, a mechanism known as cross-situational learning.
The Pascal Triangle of a Discrete Image: Definition, Properties and Application to Shape Analysis
We define the Pascal triangle of a discrete (gray scale) image as a pyramidal arrangement of complex-valued moments and we explore its geometric significance.
