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Found 25 papers, 7 papers with code
GEXIA: Granularity Expansion and Iterative Approximation for Scalable Multi-grained Video-language Learning
In various video-language learning tasks, the challenge of achieving cross-modality alignment with multi-grained data persists.
DHP Benchmark: Are LLMs Good NLG Evaluators?
Large Language Models (LLMs) are increasingly serving as evaluators in Natural Language Generation (NLG) tasks.
CLR-Fact: Evaluating the Complex Logical Reasoning Capability of Large Language Models over Factual Knowledge
While large language models (LLMs) have demonstrated impressive capabilities across various natural language processing tasks by acquiring rich factual knowledge from their broad training data, their ability to synthesize and logically reason with this knowledge in complex ways remains underexplored.
Exclusive Style Removal for Cross Domain Novel Class Discovery
In this paper, we explore and establish the solvability of NCD in cross domain setting with the necessary condition that style information must be removed.
FairLENS: Assessing Fairness in Law Enforcement Speech Recognition
Automatic speech recognition (ASR) techniques have become powerful tools, enhancing efficiency in law enforcement scenarios.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
iPREFER: An Intelligent Parameter Extractor based on Features for BSIM-CMG Models
This paper introduces an innovative parameter extraction method for BSIM-CMG compact models, seamlessly integrating curve feature extraction and machine learning techniques.
Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context
In this report, we introduce the Gemini 1. 5 family of models, representing the next generation of highly compute-efficient multimodal models capable of recalling and reasoning over fine-grained information from millions of tokens of context, including multiple long documents and hours of video and audio.
Stabilizing Sharpness-aware Minimization Through A Simple Renormalization Strategy
Recently, sharpness-aware minimization (SAM) has attracted much attention because of its surprising effectiveness in improving generalization performance.
Advancing Abductive Reasoning in Knowledge Graphs through Complex Logical Hypothesis Generation
Although many applications require the use of knowledge for explanations, the utilization of abductive reasoning in conjunction with structured knowledge, such as a knowledge graph, remains largely unexplored.
Unsupervised Candidate Answer Extraction through Differentiable Masker-Reconstructor Model
Question generation is a widely used data augmentation approach with extensive applications, and extracting qualified candidate answers from context passages is a critical step for most question generation systems.
Beyond Fairness: Age-Harmless Parkinson's Detection via Voice
Through our investigation, the discrepancy between the elderly and the young arises due to 1) an imbalanced dataset and 2) the milder symptoms often seen in early-onset patients.
Efficient Single-Image Depth Estimation on Mobile Devices, Mobile AI & AIM 2022 Challenge: Report
Various depth estimation models are now widely used on many mobile and IoT devices for image segmentation, bokeh effect rendering, object tracking and many other mobile tasks.
MSSPN: Automatic First Arrival Picking using Multi-Stage Segmentation Picking Network
Besides, since the gather data is a set of signals which are greatly different from the natural images, it is difficult for the current method to solve the FAT picking problem in case of a low Signal to Noise Ratio (SNR).
Auto-PINN: Understanding and Optimizing Physics-Informed Neural Architecture
Physics-informed neural networks (PINNs) are revolutionizing science and engineering practice by bringing together the power of deep learning to bear on scientific computation.
House Price Valuation Model Based on Geographically Neural Network Weighted Regression: The Case Study of Shenzhen, China
Confronted with the spatial heterogeneity of real estate market, some traditional research utilized Geographically Weighted Regression (GWR) to estimate the house price.
AutoVideo: An Automated Video Action Recognition System
Action recognition is an important task for video understanding with broad applications.
Fast and Accurate Single-Image Depth Estimation on Mobile Devices, Mobile AI 2021 Challenge: Report
While many solutions have been proposed for this task, they are usually very computationally expensive and thus are not applicable for on-device inference.
MFIF-GAN: A New Generative Adversarial Network for Multi-Focus Image Fusion
Multi-Focus Image Fusion (MFIF) is a promising image enhancement technique to obtain all-in-focus images meeting visual needs and it is a precondition of other computer vision tasks.
Deep Convolutional Sparse Coding Networks for Image Fusion
Image fusion is a significant problem in many fields including digital photography, computational imaging and remote sensing, to name but a few.
Infrared And Visible Image Fusion
Multi-Exposure Image Fusion
Beyond Application End-Point Results: Quantifying Statistical Robustness of MCMC Accelerators
Applying the framework to guide design space exploration shows that statistical robustness comparable to floating-point software can be achieved by slightly increasing the bit representation, without floating-point hardware requirements.
Analyzing Compositionality-Sensitivity of NLI Models
Therefore, we propose a compositionality-sensitivity testing setup that analyzes models on natural examples from existing datasets that cannot be solved via lexical features alone (i. e., on which a bag-of-words model gives a high probability to one wrong label), hence revealing the models' actual compositionality awareness.
Commonsense for Generative Multi-Hop Question Answering Tasks
We instead focus on a more challenging multi-hop generative task (NarrativeQA), which requires the model to reason, gather, and synthesize disjoint pieces of information within the context to generate an answer.
Ranked #6 on
Question Answering
on NarrativeQA
Qiniu Submission to ActivityNet Challenge 2018
We also propose new non-local-based models for further improvement on the recognition accuracy.
Person Re-Identification With Cascaded Pairwise Convolutions
In this paper, a novel deep architecture named BraidNet is proposed for person re-identification.
Robust Machine Comprehension Models via Adversarial Training
It is shown that many published models for the Stanford Question Answering Dataset (Rajpurkar et al., 2016) lack robustness, suffering an over 50% decrease in F1 score during adversarial evaluation based on the AddSent (Jia and Liang, 2017) algorithm.
