Search Results for author:
Found 10 papers, 2 papers with code
Have You Seen That Number? Investigating Extrapolation in Question Answering Models
Our work rigorously tests state-of-the-art models on DROP, a numerical MRC dataset, to see if they can handle passages that contain out-of-range numbers.
Can You Distinguish Truthful from Fake Reviews? User Analysis and Assistance Tool for Fake Review Detection
Customer reviews are useful in providing an indirect, secondhand experience of a product.
Exploiting Numerical-Contextual Knowledge to Improve Numerical Reasoning in Question Answering
Numerical reasoning over text is a challenging subtask in question answering (QA) that requires both the understanding of texts and numbers.
Finer: Investigating and Enhancing Fine-Grained Visual Concept Recognition in Large Vision Language Models
Recent advances in instruction-tuned Large Vision-Language Models (LVLMs) have imbued the models with the ability to generate high-level, image-grounded explanations with ease.
ParaHome: Parameterizing Everyday Home Activities Towards 3D Generative Modeling of Human-Object Interactions
To address this challenge, we introduce the ParaHome system, designed to capture and parameterize dynamic 3D movements of humans and objects within a common home environment.
Why So Gullible? Enhancing the Robustness of Retrieval-Augmented Models against Counterfactual Noise
Most existing retrieval-augmented language models (LMs) assume a naive dichotomy within a retrieved document set: query-relevance and irrelevance.
Sampling is Matter: Point-guided 3D Human Mesh Reconstruction
Even though those approaches have shown the remarkable progress in 3D human mesh reconstruction, it is still difficult to directly infer the relationship between features, which are encoded from the 2D input image, and 3D coordinates of each vertex.
Ranked #19 on
Monocular 3D Human Pose Estimation
on Human3.6M
(using extra training data)
Maximizing Efficiency of Language Model Pre-training for Learning Representation
Moreover, we evaluate an initial approach to the problem that has not succeeded in maintaining the accuracy of the model while showing a promising compute efficiency by thoroughly investigating the necessity of the generator module of ELECTRA.
Learning to Generate 3D Shapes with Generative Cellular Automata
We formulate the shape generation process as sampling from the transition kernel of a Markov chain, where the sampling chain eventually evolves to the full shape of the learned distribution.
Leveraging Order-Free Tag Relations for Context-Aware Recommendation
Tag recommendation relies on either a ranking function for top-$k$ tags or an autoregressive generation method.
