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Found 25 papers, 13 papers with code
Right this way: Can VLMs Guide Us to See More to Answer Questions?
To investigate this gap, we identify a critical and challenging task in the Visual Question Answering (VQA) scenario: can VLMs indicate how to adjust an image when the visual information is insufficient to answer a question?
Dual-Model Distillation for Efficient Action Classification with Hybrid Edge-Cloud Solution
As Artificial Intelligence models, such as Large Video-Language models (VLMs), grow in size, their deployment in real-world applications becomes increasingly challenging due to hardware limitations and computational costs.
SoftDedup: an Efficient Data Reweighting Method for Speeding Up Language Model Pre-training
The effectiveness of large language models (LLMs) is often hindered by duplicated data in their extensive pre-training datasets.
Read Anywhere Pointed: Layout-aware GUI Screen Reading with Tree-of-Lens Grounding
Based on the tree, our ToL agent not only comprehends the content of the indicated area but also articulates the layout and spatial relationships between elements.
Enhancing Mobile "How-to" Queries with Automated Search Results Verification and Reranking
This paper introduces a novel approach to improving the accuracy and relevance of online technical support search results through automated search results verification and reranking.
A Survey on Human-AI Teaming with Large Pre-Trained Models
In the rapidly evolving landscape of artificial intelligence (AI), the collaboration between human intelligence and AI systems, known as Human-AI (HAI) Teaming, has emerged as a cornerstone for advancing problem-solving and decision-making processes.
Gaussian Differential Privacy on Riemannian Manifolds
We develop an advanced approach for extending Gaussian Differential Privacy (GDP) to general Riemannian manifolds.
Temporal compressive edge imaging enabled by a lensless diffuser camera
Lensless imagers based on diffusers or encoding masks enable high-dimensional imaging from a single shot measurement and have been applied in various applications.
Adapting Segment Anything Model for Change Detection in HR Remote Sensing Images
In this work, we aim to utilize the strong visual recognition capabilities of VFMs to improve the change detection of high-resolution Remote Sensing Images (RSIs).
Joint Spatio-Temporal Modeling for the Semantic Change Detection in Remote Sensing Images
Semantic Change Detection (SCD) refers to the task of simultaneously extracting the changed areas and the semantic categories (before and after the changes) in Remote Sensing Images (RSIs).
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Deep Unsupervised Key Frame Extraction for Efficient Video Classification
The proposed TSDPC is a generic and powerful framework and it has two advantages compared with previous works, one is that it can calculate the number of key frames automatically.
Conformalized Fairness via Quantile Regression
To fulfill great needs and advocate the significance of quantile fairness, we propose a novel framework to learn a real-valued quantile function under the fairness requirement of Demographic Parity with respect to sensitive attributes, such as race or gender, and thereby derive a reliable fair prediction interval.
AMCAD: Adaptive Mixed-Curvature Representation based Advertisement Retrieval System
Moreover, to deploy AMCAD in Taobao, one of the largest ecommerce platforms with hundreds of million users, we design an efficient two-layer online retrieval framework for the task of graph based advertisement retrieval.
Word Embeddings via Causal Inference: Gender Bias Reducing and Semantic Information Preserving
The proposed method allows us to construct and analyze the complex causal mechanisms facilitating gender information flow while retaining oracle semantic information within word embeddings.
Bi-Temporal Semantic Reasoning for the Semantic Change Detection in HR Remote Sensing Images
Recent studies indicate that the SCD can be modeled through a triple-branch Convolutional Neural Network (CNN), which contains two temporal branches and a change branch.
Looking Outside the Window: Wide-Context Transformer for the Semantic Segmentation of High-Resolution Remote Sensing Images
To overcome this limitation, we propose a Wide-Context Network (WiCoNet) for the semantic segmentation of HR RSIs.
Field-tunable toroidal moment in a chiral-lattice magnet
A toroidal dipole moment appears independent of the electric and magnetic dipole moment in the multipole expansion of electrodynamics.
Strongly Correlated Electrons Materials Science
Adversarial Shape Learning for Building Extraction in VHR Remote Sensing Images
To address this issue, we propose an adversarial shape learning network (ASLNet) to model the building shape patterns that improve the accuracy of building segmentation.
MP-ResNet: Multi-path Residual Network for the Semantic segmentation of High-Resolution PolSAR Images
This CNN architecture can be used as a baseline method for future studies on the semantic segmentation of PolSAR images.
Active Deep Densely Connected Convolutional Network for Hyperspectral Image Classification
First, a deep densely connected convolutional network is considered for hyperspectral image classification.
Connecting Web Event Forecasting with Anomaly Detection: A Case Study on Enterprise Web Applications Using Self-Supervised Neural Networks
In this paper, we present a web event forecasting approach, DeepEvent, in enterprise web applications for better anomaly detection.
Defending against GAN-based Deepfake Attacks via Transformation-aware Adversarial Faces
On the one hand, the quality of the synthesized faces is reduced with more visual artifacts such that the synthesized faces are more obviously fake or less convincing to human observers.
DiResNet: Direction-aware Residual Network for Road Extraction in VHR Remote Sensing Images
The binary segmentation of roads in very high resolution (VHR) remote sensing images (RSIs) has always been a challenging task due to factors such as occlusions (caused by shadows, trees, buildings, etc.)
Reducing Selection Bias in Counterfactual Reasoning for Individual Treatment Effects Estimation
We learn two groups of latent random variables, where one group corresponds to variables that only cause selection bias, and the other group is relevant for outcome prediction.
Improving Semantic Segmentation of Aerial Images Using Patch-based Attention
High-level features extracted from the late layers of a neural network are rich in semantic information, yet have blurred spatial details; low-level features extracted from the early layers of a network contain more pixel-level information, but are isolated and noisy.
