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Found 33 papers, 14 papers with code
Graph-based Aspect Representation Learning for Entity Resolution
Entity Resolution (ER) identifies records that refer to the same real-world entity.
Debiased Novel Category Discovering and Localization
In recent years, object detection in deep learning has experienced rapid development.
Enhancing Robustness of LLM-Synthetic Text Detectors for Academic Writing: A Comprehensive Analysis
In this paper, we present a comprehensive analysis of the impact of prompts on the text generated by LLMs and highlight the potential lack of robustness in one of the current state-of-the-art GPT detectors.
Human-Machine Cooperative Multimodal Learning Method for Cross-subject Olfactory Preference Recognition
Secondly, a complementary multimodal data mining strategy is proposed to effectively mine the common features of multimodal data representing odor information and the individual features in olfactory EEG representing individual emotional information.
Modeling Orders of User Behaviors via Differentiable Sorting: A Multi-task Framework to Predicting User Post-click Conversion
Inspired by recent advances on differentiable sorting, in this paper, we propose a novel multi-task framework that leverages orders of user behaviors to predict user post-click conversion in an end-to-end approach.
Consolidator: Mergeable Adapter with Grouped Connections for Visual Adaptation
To further enhance the model's capacity to transfer knowledge under a constrained storage budget and keep inference efficient, we consolidate the parameters in two stages: 1. between adaptation and storage, and 2. between loading and inference.
Hi Sheldon! Creating Deep Personalized Characters from TV Shows
To support this novel task, we further collect a character centric multimodal dialogue dataset, named Deep Personalized Character Dataset (DPCD), from TV shows.
Box-Level Active Detection
Active learning selects informative samples for annotation within budget, which has proven efficient recently on object detection.
X-ReID: Cross-Instance Transformer for Identity-Level Person Re-Identification
In this paper, we propose to promote Instance-Level features to Identity-Level features by employing cross-attention to incorporate information from one image to another of the same identity, thus more unified and discriminative pedestrian information can be obtained.
DarkVision: A Benchmark for Low-light Image/Video Perception
In this paper, we contribute the first multi-illuminance, multi-camera, and low-light dataset, named DarkVision, serving for both image enhancement and object detection.
DartBlur: Privacy Preservation With Detection Artifact Suppression
Facial information is particularly sensitive in this regard.
Ground Plane Matters: Picking Up Ground Plane Prior in Monocular 3D Object Detection
To pick up the ground plane prior for M3OD, we propose a Ground Plane Enhanced Network (GPENet) which resolves both issues at one go.
Automatic Landmark Detection and Registration of Brain Cortical Surfaces via Quasi-Conformal Geometry and Convolutional Neural Networks
In this work, we propose a novel framework for the automatic landmark detection and registration of brain cortical surfaces using quasi-conformal geometry and convolutional neural networks.
JDRec: Practical Actor-Critic Framework for Online Combinatorial Recommender System
A combinatorial recommender (CR) system feeds a list of items to a user at a time in the result page, in which the user behavior is affected by both contextual information and items.
A High-Accuracy Unsupervised Person Re-identification Method Using Auxiliary Information Mined from Datasets
Combined with auxiliary information exploiting modules, our methods achieve mAP of 89. 9% on DukeMTMC, where TOC, STS and SCP all contributed considerable performance improvements.
Ranked #1 on
Unsupervised Person Re-Identification
on MARS
MP2: A Momentum Contrast Approach for Recommendation with Pointwise and Pairwise Learning
To alleviate the influence of the annotation bias, we perform a momentum update to ensure a consistent item representation.
A Free Lunch to Person Re-identification: Learning from Automatically Generated Noisy Tracklets
Extensive experiments on MARS with various manually generated noises show the effectiveness of the proposed framework.
TAGPerson: A Target-Aware Generation Pipeline for Person Re-identification
In TAGPerson, we extract information from target scenes and use them to control our parameterized rendering process to generate target-aware synthetic images, which would hold a smaller gap to the real images in the target domain.
Camera Bias Regularization for Person Re-identification
Due to the discrepancies between cameras caused by illumination, background, or viewpoint, the underlying difficulty for Re-ID is the camera bias problem, which leads to the large gap of within-identity features from different cameras.
LODE: Deep Local Deblurring and A New Benchmark
While recent deep deblurring algorithms have achieved remarkable progress, most existing methods focus on the global deblurring problem, where the image blur mostly arises from severe camera shake.
Manipulating Identical Filter Redundancy for Efficient Pruning on Deep and Complicated CNN
The existence of redundancy in Convolutional Neural Networks (CNNs) enables us to remove some filters/channels with acceptable performance drops.
Zero-shot Node Classification with Decomposed Graph Prototype Network
Node classification is a central task in graph data analysis.
IEEE 802.11be-Wi-Fi 7: New Challenges and Opportunities
Due to the related stringent requirements, supporting these applications over wireless local area network (WLAN) is far beyond the capabilities of the new WLAN standard -- IEEE 802. 11ax.
ResRep: Lossless CNN Pruning via Decoupling Remembering and Forgetting
Via training with regular SGD on the former but a novel update rule with penalty gradients on the latter, we realize structured sparsity.
Topic-aware chatbot using Recurrent Neural Networks and Nonnegative Matrix Factorization
We propose a novel model for a topic-aware chatbot by combining the traditional Recurrent Neural Network (RNN) encoder-decoder model with a topic attention layer based on Nonnegative Matrix Factorization (NMF).
Global Sparse Momentum SGD for Pruning Very Deep Neural Networks
Deep Neural Network (DNN) is powerful but computationally expensive and memory intensive, thus impeding its practical usage on resource-constrained front-end devices.
The Prevalence of Errors in Machine Learning Experiments
Objective: We investigate the incidence of errors in a sample of machine learning experiments in the domain of software defect prediction.
ACNet: Strengthening the Kernel Skeletons for Powerful CNN via Asymmetric Convolution Blocks
We propose Asymmetric Convolution Block (ACB), an architecture-neutral structure as a CNN building block, which uses 1D asymmetric convolutions to strengthen the square convolution kernels.
Approximated Oracle Filter Pruning for Destructive CNN Width Optimization
It is not easy to design and run Convolutional Neural Networks (CNNs) due to: 1) finding the optimal number of filters (i. e., the width) at each layer is tricky, given an architecture; and 2) the computational intensity of CNNs impedes the deployment on computationally limited devices.
Centripetal SGD for Pruning Very Deep Convolutional Networks with Complicated Structure
The redundancy is widely recognized in Convolutional Neural Networks (CNNs), which enables to remove unimportant filters from convolutional layers so as to slim the network with acceptable performance drop.
Collective Matrix Factorization Hashing for Multimodal Data
In this paper, we study the problems of learning hash functions in the context of multimodal data for cross-view similarity search.
Transfer Sparse Coding for Robust Image Representation
In this paper, we propose a Transfer Sparse Coding (TSC) approach to construct robust sparse representations for classifying cross-distribution images accurately.
