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Found 60 papers, 16 papers with code
Localization and Expansion: A Decoupled Framework for Point Cloud Few-shot Semantic Segmentation
Point cloud few-shot semantic segmentation (PC-FSS) aims to segment targets of novel categories in a given query point cloud with only a few annotated support samples.
UNCO: Towards Unifying Neural Combinatorial Optimization through Large Language Model
Recently, applying neural networks to address combinatorial optimization problems (COPs) has attracted considerable research attention.
AdapMoE: Adaptive Sensitivity-based Expert Gating and Management for Efficient MoE Inference
Mixture-of-Experts (MoE) models are designed to enhance the efficiency of large language models (LLMs) without proportionally increasing the computational demands.
Table Transformers for Imputing Textual Attributes
Missing data in tabular dataset is a common issue as the performance of downstream tasks usually depends on the completeness of the training dataset.
HG-PIPE: Vision Transformer Acceleration with Hybrid-Grained Pipeline
With a VCK190 FPGA, HG-PIPE realizes end-to-end ViT acceleration on a single device and achieves 7118 images/s, which is 2. 81 times faster than a V100 GPU.
Topological Persistence Guided Knowledge Distillation for Wearable Sensor Data
Also, we apply an annealing strategy in KD for fast saturation and better accommodation from different features, while the knowledge gap between the teachers and student is reduced.
Evaluating Fairness in Large Vision-Language Models Across Diverse Demographic Attributes and Prompts
Large vision-language models (LVLMs) have recently achieved significant progress, demonstrating strong capabilities in open-world visual understanding.
Do Not Wait: Learning Re-Ranking Model Without User Feedback At Serving Time in E-Commerce
It means the modification is tailored to and only to the current request to capture the specific context of the request.
Reparameterizable Dual-Resolution Network for Real-time Semantic Segmentation
Although existing real-time semantic segmentation models achieve a commendable balance between accuracy and speed, their multi-path blocks still affect overall speed.
Holistic-Motion2D: Scalable Whole-body Human Motion Generation in 2D Space
In this paper, we introduce a novel path to $\textit{general}$ human motion generation by focusing on 2D space.
An Aggregation-Free Federated Learning for Tackling Data Heterogeneity
FedAF inherently avoids the issue of client drift, enhances the quality of condensed data amid notable data heterogeneity, and improves the global model performance.
Using Neural Networks to Model Hysteretic Kinematics in Tendon-Actuated Continuum Robots
The ability to accurately model mechanical hysteretic behavior in tendon-actuated continuum robots using deep learning approaches is a growing area of interest.
Do Large Language Models Rank Fairly? An Empirical Study on the Fairness of LLMs as Rankers
The integration of Large Language Models (LLMs) in information retrieval has raised a critical reevaluation of fairness in the text-ranking models.
Image-to-Image Matching via Foundation Models: A New Perspective for Open-Vocabulary Semantic Segmentation
Open-vocabulary semantic segmentation (OVS) aims to segment images of arbitrary categories specified by class labels or captions.
Mathematical Foundation and Corrections for Full Range Head Pose Estimation
This paper presents (1) code and algorithms for inferring coordinate system from provided source code, code for Euler angle application order and extracting precise rotation matrices and the Euler angles, (2) code and algorithms for converting poses from one rotation system to another, (3) novel formulae for 2D augmentations of the rotation matrices, and (4) derivations and code for the correct drawing routines for rotation matrices and poses.
Pivoting Retail Supply Chain with Deep Generative Techniques: Taxonomy, Survey and Insights
Generative AI applications, such as ChatGPT or DALL-E, have shown the world their impressive capabilities in generating human-like text or image.
ASCEND: Accurate yet Efficient End-to-End Stochastic Computing Acceleration of Vision Transformer
To overcome the compatibility challenges, ASCEND proposes a novel deterministic SC block for GELU and leverages an SC-friendly iterative approximate algorithm to design an accurate and efficient softmax circuit.
Scaling Up LLM Reviews for Google Ads Content Moderation
This study proposes a method for scaling up LLM reviews for content moderation in Google Ads.
G^3-LQ: Marrying Hyperbolic Alignment with Explicit Semantic-Geometric Modeling for 3D Visual Grounding
Specifically the Position Adaptive Geometric Exploring (PAGE) unearths underlying information of 3D objects in the geometric details and spatial relationships perspectives.
Exploring Pose-Aware Human-Object Interaction via Hybrid Learning
Towards these issues we propose a hybrid learning method based on pose-aware HOI feature refinement.
Near-Optimal Resilient Aggregation Rules for Distributed Learning Using 1-Center and 1-Mean Clustering with Outliers
Our analysis show that constant approximations to the 1-center and 1-mean clustering problems with outliers provide near-optimal resilient aggregators for metric-based criteria, which have been proven to be crucial in the homogeneous and heterogeneous cases respectively.
Focus on Query: Adversarial Mining Transformer for Few-Shot Segmentation
In this paper, we rethink the importance of support information and propose a new query-centric FSS model Adversarial Mining Transformer (AMFormer), which achieves accurate query image segmentation with only rough support guidance or even weak support labels.
Exciton-Polariton Condensates: A Fourier Neural Operator Approach
Advancements in semiconductor fabrication over the past decade have catalyzed extensive research into all-optical devices driven by exciton-polariton condensates.
WikiMT++ Dataset Card
WikiMT++ is an expanded and refined version of WikiMusicText (WikiMT), featuring 1010 curated lead sheets in ABC notation.
Quantifying predictive uncertainty of aphasia severity in stroke patients with sparse heteroscedastic Bayesian high-dimensional regression
Bayesian heteroscedastic linear regression models relax the homoscedastic error assumption but can enforce restrictive prior assumptions on parameters, and many are computationally infeasible in the high-dimensional setting.
Razor SNN: Efficient Spiking Neural Network with Temporal Embeddings
The event streams generated by dynamic vision sensors (DVS) are sparse and non-uniform in the spatial domain, while still dense and redundant in the temporal domain.
Controllable Multi-Objective Re-ranking with Policy Hypernetworks
In this paper, we propose a framework called controllable multi-objective re-ranking (CMR) which incorporates a hypernetwork to generate parameters for a re-ranking model according to different preference weights.
Multi-Competitive Virus Spread over a Time-Varying Networked SIS Model with an Infrastructure Network
We study the spread of multi-competitive viruses over a (possibly) time-varying network of individuals accounting for the presence of shared infrastructure networks that further enables transmission of the virus.
Network-based Statistics Distinguish Anomic and Broca Aphasia
We identified a subnetwork of, rather than isolated, connections that statistically differentiate the resting-state brain networks of the two groups, in comparison with standard lesion symptom mapping results that yield isolated connections.
Dynamic Graph Learning With Content-Guided Spatial-Frequency Relation Reasoning for Deepfake Detection
To address this issue, we propose a Spatial-Frequency Dynamic Graph method to exploit the relation-aware features in spatial and frequency domains via dynamic graph learning.
Rethinking the Correlation in Few-Shot Segmentation: A Buoys View
In this work, we rethink how to mitigate the false matches from the perspective of representative reference features (referred to as buoys), and propose a novel adaptive buoys correlation (ABC) network to rectify direct pairwise pixel-level correlation, including a buoys mining module and an adaptive correlation module.
Alignment Before Aggregation: Trajectory Memory Retrieval Network for Video Object Segmentation
In this work, we reconcile the inherent tension of spatial and temporal information to retrieve memory frame information along the object trajectory, and propose a novel and coherent Trajectory Memory Retrieval Network (TMRN) to equip with the trajectory information, including a spatial alignment module and a temporal aggregation module.
5G PRS-Based Sensing: A Sensing Reference Signal Approach for Joint Sensing and Communication System
In this paper, we investigate how to apply the positioning reference signal (PRS) of the 5th generation (5G) mobile communications in radar sensing.
A State Feedback Controller for Mitigation of Continuous-Time Networked SIS Epidemics
The upshot of devising such a strategy is that it allows health administration officials to ensure that there is sufficient capacity in the healthcare system to treat the most severe cases.
A Sample-Based Algorithm for Approximately Testing $r$-Robustness of a Digraph
In this paper, we propose a sample-based algorithm to approximately test $r$-robustness of a digraph with $n$ vertices and $m$ edges.
Memory-efficient Segmentation of High-resolution Volumetric MicroCT Images
In this work, we propose a novel memory-efficient network architecture for 3D high-resolution image segmentation.
Exploiting Dynamic and Fine-grained Semantic Scope for Extreme Multi-label Text Classification
TReaderXML dynamically obtains teacher knowledge for each text by similar texts and hierarchical label information in training sets to release the ability of distinctly fine-grained label-oriented semantic scope.
Multi Label Text Classification
Multi-Label Text Classification
+1
Restricted Black-box Adversarial Attack Against DeepFake Face Swapping
DeepFake face swapping presents a significant threat to online security and social media, which can replace the source face in an arbitrary photo/video with the target face of an entirely different person.
Exploring Dual Encoder Architectures for Question Answering
We further explore and explain why parameter sharing in projection layer significantly improves the efficacy of the dual encoders, by directly probing the embedding spaces of the two encoder towers with t-SNE algorithm.
Reinforcement Re-ranking with 2D Grid-based Recommendation Panels
Then, it defines \emph{the MDP discrete time steps as the ranks in the initial ranking list, and the actions as the prediction of the user-item preference and the selection of the slots}.
Fully Self-Supervised Learning for Semantic Segmentation
To solve this problem, we proposed a bootstrapped training scheme for semantic segmentation, which fully leveraged the global semantic knowledge for self-supervision with our proposed PGG strategy and CAE module.
Ranked #16 on
Unsupervised Semantic Segmentation
on COCO-Stuff-27
Recursive Least Squares Advantage Actor-Critic Algorithms
RLSNA2C uses the Kronecker-factored approximation, the RLS method and the natural policy gradient to learn the compatible parameter and the policy parameter.
Recursive Least Squares for Training and Pruning Convolutional Neural Networks
Convolutional neural networks (CNNs) have succeeded in many practical applications.
Improving Adversarially Robust Few-Shot Image Classification With Generalizable Representations
Extensive experiments show that our method can significantly outperform state-of-the-art adversarially robust FSIC methods on two standard benchmarks.
Testing for Causal Influence using a Partial Coherence Statistic
In this paper we explore partial coherence as a tool for evaluating causal influence of one signal sequence on another.
Graph Attention Layer Evolves Semantic Segmentation for Road Pothole Detection: A Benchmark and Algorithms
Existing road pothole detection approaches can be classified as computer vision-based or machine learning-based.
AdaFit: Rethinking Learning-based Normal Estimation on Point Clouds
Existing works use a network to learn point-wise weights for weighted least squares surface fitting to estimate the normals, which has difficulty in finding accurate normals in complex regions or containing noisy points.
Ranked #5 on
Surface Normals Estimation
on PCPNet
TriTransNet: RGB-D Salient Object Detection with a Triplet Transformer Embedding Network
In view of the more contribution of high-level features for the performance, we propose a triplet transformer embedding module to enhance them by learning long-range dependencies across layers.
Suppressing the endemic equilibrium in SIS epidemics: A state dependent approach
This paper considers the susceptible-infected-susceptible (SIS) epidemic model with an underlying network structure among subpopulations and focuses on the effect of social distancing to regulate the epidemic level.
Integer-Only Neural Network Quantization Scheme Based on Shift-Batch-Normalization
Without big integer multiplication(which is used in previous integer-only-quantization methods), this scheme can achieve good power and latency efficiency, and is especially suitable to be deployed on co-designed hardware platforms.
US Fatal Police Shooting Analysis and Prediction
The top four most relevant attributes were state joined year, state land area, gun ownership rate, and violent crime rate.
Improving Reinforcement Learning with Human Assistance: An Argument for Human Subject Studies with HIPPO Gym
OpenAI Gym is a common framework for RL research, including a large number of standard environments and agents, making RL research significantly more accessible.
Resilient Consensus for Multi-Agent Systems under Adversarial Spreading Processes
Such a problem setting serves as a model of opinion dynamics in social networks where consensus is to be formed at the time of pandemic and infected individuals may deviate from their true opinions.
Resilient Self/Event-Triggered Consensus Based on Ternary Control
The paper considers the problem of multi-agent consensus in the presence of adversarial agents which may try to prevent and introduce undesired influence on the coordination among the regular agents.
Rethinking Road Surface 3D Reconstruction and Pothole Detection: From Perspective Transformation to Disparity Map Segmentation
This paper presents an efficient pothole detection algorithm based on road disparity map estimation and segmentation.
Theoretical proposals to measure resonator-induced modifications of the electronic ground-state in doped quantum wells
Recent interest in the physics of non-perturbative light-matter coupling led to the development of solid-state cavity quantum electrodynamics setups in which the interaction energies are comparable with the bare ones.
Mesoscale and Nanoscale Physics Quantum Physics
Leveraging an Efficient and Semantic Location Embedding to Seek New Ports of Bike Share Services
Finally, we apply ESLE to seek new service ports for NTT DOCOMO's bike share services operated in Japan.
Scalable Hierarchical Agglomerative Clustering
The applicability of agglomerative clustering, for inferring both hierarchical and flat clustering, is limited by its scalability.
Three-Filters-to-Normal: An Accurate and Ultrafast Surface Normal Estimator
To evaluate the performance of our proposed SNE, we created three large-scale synthetic datasets (easy, medium and hard) using 24 3D mesh models, each of which is used to generate 1800--2500 pairs of depth images (resolution: 480X640 pixels) and the corresponding ground-truth surface normal maps from different views.
Anchor & Transform: Learning Sparse Representations of Discrete Objects
Learning continuous representations of discrete objects such as text, users, and items lies at the heart of many applications including text and user modeling.
