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Found 34 papers, 10 papers with code
Eyes Can Deceive: Benchmarking Counterfactual Reasoning Abilities of Multi-modal Large Language Models
Counterfactual reasoning, as a crucial manifestation of human intelligence, refers to making presuppositions based on established facts and extrapolating potential outcomes.
Detection of Problem Gambling with Less Features Using Machine Learning Methods
Analytic features in gambling study are performed based on the amount of data monitoring on user daily actions.
Lumen: Unleashing Versatile Vision-Centric Capabilities of Large Multimodal Models
This adaptation leads to convenient development of such LMMs with minimal modifications, however, it overlooks the intrinsic characteristics of diverse visual tasks and hinders the learning of perception capabilities.
From Canteen Food to Daily Meals: Generalizing Food Recognition to More Practical Scenarios
These two datasets are used to evaluate the transferability of approaches from the well-curated food image domain to the everyday-life food image domain.
Federated Learning While Providing Model as a Service: Joint Training and Inference Optimization
Toward the challenges, we propose an online problem approximation to reduce the problem complexity and optimize the resources to balance the needs of model training and inference.
Provably Convergent Federated Trilevel Learning
To address the aforementioned challenges, this paper proposes an asynchronous federated trilevel optimization method to solve TLO problems.
Instance-aware Multi-Camera 3D Object Detection with Structural Priors Mining and Self-Boosting Learning
Camera-based bird-eye-view (BEV) perception paradigm has made significant progress in the autonomous driving field.
Bandit Learning to Rank with Position-Based Click Models: Personalized and Equal Treatments
Online learning to rank (ONL2R) is a foundational problem for recommender systems and has received increasing attention in recent years.
Federated Distributionally Robust Optimization with Non-Convex Objectives: Algorithm and Analysis
Distributionally Robust Optimization (DRO), which aims to find an optimal decision that minimizes the worst case cost over the ambiguity set of probability distribution, has been widely applied in diverse applications, e. g., network behavior analysis, risk management, etc.
NuScenes-QA: A Multi-modal Visual Question Answering Benchmark for Autonomous Driving Scenario
We introduce a novel visual question answering (VQA) task in the context of autonomous driving, aiming to answer natural language questions based on street-view clues.
Learning Attribute and Class-Specific Representation Duet for Fine-Grained Fashion Analysis
Fashion representation learning involves the analysis and understanding of various visual elements at different granularities and the interactions among them.
Stock Market Prediction via Deep Learning Techniques: A Survey
Existing surveys on stock market prediction often focus on traditional machine learning methods instead of deep learning methods.
Asynchronous Distributed Bilevel Optimization
Bilevel optimization plays an essential role in many machine learning tasks, ranging from hyperparameter optimization to meta-learning.
CPMLHO:Hyperparameter Tuning via Cutting Plane and Mixed-Level Optimization
The bilevel optimization is used to automatically update the hyperparameter, and the gradient of the hyperparameter is the approximate gradient based on the best response function.
Distributed Distributionally Robust Optimization with Non-Convex Objectives
Distributionally Robust Optimization (DRO), which aims to find an optimal decision that minimizes the worst case cost over the ambiguity set of probability distribution, has been widely applied in diverse applications, e. g., network behavior analysis, risk management, etc.
MSMDFusion: Fusing LiDAR and Camera at Multiple Scales with Multi-Depth Seeds for 3D Object Detection
Recent approaches aim at exploring the semantic densities of camera features through lifting points in 2D camera images (referred to as seeds) into 3D space, and then incorporate 2D semantics via cross-modal interaction or fusion techniques.
Task-aware Similarity Learning for Event-triggered Time Series
The proposed framework aspires to offer a stepping stone that gives rise to a systematic approach to model and learn similarities among a multitude of event-triggered time series.
Suspected Object Matters: Rethinking Model's Prediction for One-stage Visual Grounding
Recently, one-stage visual grounders attract high attention due to their comparable accuracy but significantly higher efficiency than two-stage grounders.
MORE: Multi-Order RElation Mining for Dense Captioning in 3D Scenes
3D dense captioning is a recently-proposed novel task, where point clouds contain more geometric information than the 2D counterpart.
PATO: Producibility-Aware Topology Optimization using Deep Learning for Metal Additive Manufacturing
In this paper, we propose PATO-a producibility-aware topology optimization (TO) framework to help efficiently explore the design space of components fabricated using metal additive manufacturing (AM), while ensuring manufacturability with respect to cracking.
Two-stage Visual Cues Enhancement Network for Referring Image Segmentation
Referring Image Segmentation (RIS) aims at segmenting the target object from an image referred by one given natural language expression.
Data-Driven Learning of 3-Point Correlation Functions as Microstructure Representations
This paper considers the open challenge of identifying complete, concise, and explainable quantitative microstructure representations for disordered heterogeneous material systems.
Optical Flow Estimation via Motion Feature Recovery
In this paper, we discover that the lost information is related to a large quantity of motion features (more than 40%) computed from the popular discriminative cost-volume feature would completely vanish due to invalid sampling, leading to the low efficiency of optical flow learning.
Topological Transformations in Pentagonal 2D Materials Induced by Stone-Wales Defects
We discover two distinct topological pathways through which the pentagonal Cairo tiling (P5), a structural model for single-layer $AB_2$ pyrite materials, respectively transforms into a crystalline rhombus-hexagon (C46) tiling and random rhombus-pentagon-hexagon (R456) tilings, by continuously introducing the Stone-Wales (SW) topological defects.
Soft Condensed Matter Disordered Systems and Neural Networks Materials Science
EffiScene: Efficient Per-Pixel Rigidity Inference for Unsupervised Joint Learning of Optical Flow, Depth, Camera Pose and Motion Segmentation
This paper addresses the challenging unsupervised scene flow estimation problem by jointly learning four low-level vision sub-tasks: optical flow $\textbf{F}$, stereo-depth $\textbf{D}$, camera pose $\textbf{P}$ and motion segmentation $\textbf{S}$.
2D+3D Facial Expression Recognition via Discriminative Dynamic Range Enhancement and Multi-Scale Learning
In 2D+3D facial expression recognition (FER), existing methods generate multi-view geometry maps to enhance the depth feature representation.
3D Facial Expression Recognition
Facial Expression Recognition
SeNsER: Learning Cross-Building Sensor Metadata Tagger
Sensor metadata tagging, akin to the named entity recognition task, provides key contextual information (e. g., measurement type and location) about sensors for running smart building applications.
BERT for Joint Multichannel Speech Dereverberation with Spatial-aware Tasks
The proposed method addresses involved tasks as a sequence to sequence mapping problem, which is general enough for a variety of front-end speech enhancement tasks.
TimeAutoML: Autonomous Representation Learning for Multivariate Irregularly Sampled Time Series
To this end, we propose an autonomous representation learning approach for multivariate time series (TimeAutoML) with irregular sampling rates and variable lengths.
Translate Reverberated Speech to Anechoic Ones: Speech Dereverberation with BERT
Single channel speech dereverberation is considered in this work.
Multi-Object Portion Tracking in 4D Fluorescence Microscopy Imagery with Deep Feature Maps
In this paper, we first define the problem of multi-object portion tracking to model the protein object tracking process.
Modeling cell migration regulated by cell-ECM micromechanical coupling
During migration, individual cells can generate active pulling forces via actin filament contraction, which are transmitted to the ECM fibers through focal adhesion complexes, remodel the ECM, and eventually propagate to and can be sensed by other cells in the system.
Improving Direct Physical Properties Prediction of Heterogeneous Materials from Imaging Data via Convolutional Neural Network and a Morphology-Aware Generative Model
Direct prediction of material properties from microstructures through statistical models has shown to be a potential approach to accelerating computational material design with large design spaces.
Computational Physics Materials Science
Microstructure Representation and Reconstruction of Heterogeneous Materials via Deep Belief Network for Computational Material Design
Integrated Computational Materials Engineering (ICME) aims to accelerate optimal design of complex material systems by integrating material science and design automation.
