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Found 26 papers, 10 papers with code
A Self-supervised Joint Training Framework for Document Reranking
Pretrained language models such as BERT have been successfully applied to a wide range of natural language processing tasks and also achieved impressive performance in document reranking tasks.
Exploring ChatGPT-based Augmentation Strategies for Contrastive Aspect-based Sentiment Analysis
Specifically, we explore three data augmentation strategies based on ChatGPT: context-focused, aspect-focused, and context-aspect data augmentation techniques.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
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Two-Timescale Synchronization and Migration for Digital Twin Networks: A Multi-Agent Deep Reinforcement Learning Approach
Digital twins (DTs) have emerged as a promising enabler for representing the real-time states of physical worlds and realizing self-sustaining systems.
Self-supervised Topic Taxonomy Discovery in the Box Embedding Space
Topic taxonomy discovery aims at uncovering topics of different abstraction levels and constructing hierarchical relations between them.
Lost in UNet: Improving Infrared Small Target Detection by Underappreciated Local Features
UNet and its variants, as popular detection backbone networks, downsample the local features early and cause the irreversible loss of these local features, leading to both the missed and false detection of small targets in infrared images.
AttnDreamBooth: Towards Text-Aligned Personalized Text-to-Image Generation
We attribute these issues to the incorrect learning of the embedding alignment for the concept.
FriendNet: Detection-Friendly Dehazing Network
In this paper, we raise an intriguing question: can the combination of image restoration and object detection enhance detection performance in adverse weather conditions?
Parameter-Efficient Fine-Tuning Methods for Pretrained Language Models: A Critical Review and Assessment
The demands for fine-tuning PLMs, especially LLMs, have led to a surge in the development of PEFT methods, as depicted in Fig.
Cross-BERT for Point Cloud Pretraining
By performing cross-modal interaction, Cross-BERT can smoothly reconstruct the masked tokens during pretraining, leading to notable performance enhancements for downstream tasks.
Recurrent Attention Networks for Long-text Modeling
Revisiting the self-attention mechanism and the recurrent structure, this paper proposes a novel long-document encoding model, Recurrent Attention Network (RAN), to enable the recurrent operation of self-attention.
Search By Image: Deeply Exploring Beneficial Features for Beauty Product Retrieval
Searching by image is popular yet still challenging due to the extensive interference arose from i) data variations (e. g., background, pose, visual angle, brightness) of real-world captured images and ii) similar images in the query dataset.
PointSee: Image Enhances Point Cloud
There is a trend to fuse multi-modal information for 3D object detection (3OD).
GeoGCN: Geometric Dual-domain Graph Convolution Network for Point Cloud Denoising
We propose GeoGCN, a novel geometric dual-domain graph convolution network for point cloud denoising (PCD).
SPCNet: Stepwise Point Cloud Completion Network
We propose a novel stepwise point cloud completion network (SPCNet) for various 3D models with large missings.
TogetherNet: Bridging Image Restoration and Object Detection Together via Dynamic Enhancement Learning
Adverse weather conditions such as haze, rain, and snow often impair the quality of captured images, causing detection networks trained on normal images to generalize poorly in these scenarios.
Contrastive Semantic-Guided Image Smoothing Network
Image smoothing is a fundamental low-level vision task that aims to preserve salient structures of an image while removing insignificant details.
3DLG-Detector: 3D Object Detection via Simultaneous Local-Global Feature Learning
Capturing both local and global features of irregular point clouds is essential to 3D object detection (3OD).
PV-RCNN++: Semantical Point-Voxel Feature Interaction for 3D Object Detection
Such a module will guide our PV-RCNN++ to integrate more object-related point-wise and voxel-wise features in the pivotal areas.
GeoSegNet: Point Cloud Semantic Segmentation via Geometric Encoder-Decoder Modeling
Semantic segmentation of point clouds, aiming to assign each point a semantic category, is critical to 3D scene understanding. Despite of significant advances in recent years, most of existing methods still suffer from either the object-level misclassification or the boundary-level ambiguity.
UCL-Dehaze: Towards Real-world Image Dehazing via Unsupervised Contrastive Learning
From a different yet new perspective, this paper explores contrastive learning with an adversarial training effort to leverage unpaired real-world hazy and clean images, thus bridging the gap between synthetic and real-world haze is avoided.
Semi-MoreGAN: A New Semi-supervised Generative Adversarial Network for Mixture of Rain Removal
Rain is one of the most common weather which can completely degrade the image quality and interfere with the performance of many computer vision tasks, especially under heavy rain conditions.
When A Conventional Filter Meets Deep Learning: Basis Composition Learning on Image Filters
Image filters are fast, lightweight and effective, which make these conventional wisdoms preferable as basic tools in vision tasks.
Topic Driven Adaptive Network for Cross-Domain Sentiment Classification
In this vein, most approaches utilized domain adaptation that maps data from different domains into a common feature space.
Neural Mixed Counting Models for Dispersed Topic Discovery
Mixed counting models that use the negative binomial distribution as the prior can well model over-dispersed and hierarchically dependent random variables; thus they have attracted much attention in mining dispersed document topics.
Siamese Network-Based Supervised Topic Modeling
Label-specific topics can be widely used for supporting personality psychology, aspect-level sentiment analysis, and cross-domain sentiment classification.
A Network Framework for Noisy Label Aggregation in Social Media
This paper focuses on the task of noisy label aggregation in social media, where users with different social or culture backgrounds may annotate invalid or malicious tags for documents.
Cultural Vocal Bursts Intensity Prediction
Image Classification
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