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Found 29 papers, 14 papers with code
Modeling Aspect Correlation for Aspect-based Sentiment Analysis via Recurrent Inverse Learning Guidance
Second, we propose Inverse Learning Guidance to improve the selection of aspect feature by considering aspect correlation, which provides more useful information to determine polarity.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+1
OIPR: Evaluation for Time-series Anomaly Detection Inspired by Operator Interest
Furthermore, we build a special scenario dataset to compare the characteristics of different evaluation methods.
Erasing Without Remembering: Safeguarding Knowledge Forgetting in Large Language Models
In this paper, we explore machine unlearning from a novel dimension, by studying how to safeguard model unlearning in large language models (LLMs).
Diversified Augmentation with Domain Adaptation for Debiased Video Temporal Grounding
The data augmentation generates videos with various lengths and target moment locations to diversify temporal distributions.
ChatTime: A Unified Multimodal Time Series Foundation Model Bridging Numerical and Textual Data
In this paper, we innovatively model time series as a foreign language and construct ChatTime, a unified framework for time series and text processing.
DeepCore: Simple Fingerprint Construction for Differentiating Homologous and Piracy Models
Then DeepCore constructs core points far away from the decision boundary by optimizing the predicted confidence of a few sample points and leverages behavioral discrepancies between piracy and homologous models to identify piracy models.
Interdependency Matters: Graph Alignment for Multivariate Time Series Anomaly Detection
To capitalize on this insight, we introduce MADGA (MTS Anomaly Detection via Graph Alignment), which redefines anomaly detection as a graph alignment (GA) problem that explicitly utilizes interdependencies for anomaly detection.
Rethinking the Power of Timestamps for Robust Time Series Forecasting: A Global-Local Fusion Perspective
Time series forecasting has played a pivotal role across various industries, including finance, transportation, energy, healthcare, and climate.
OutlierTune: Efficient Channel-Wise Quantization for Large Language Models
The pre-execution of dequantization updates the model weights by the activation scaling factors, avoiding the internal scaling and costly additional computational overheads brought by the per-channel activation quantization.
Understanding and Guiding Weakly Supervised Entity Alignment with Potential Isomorphism Propagation
In this paper, we present a propagation perspective to analyze weakly supervised EA and explain the existing aggregation-based EA models.
Towards Semantic Consistency: Dirichlet Energy Driven Robust Multi-Modal Entity Alignment
This study introduces a novel approach, DESAlign, which addresses these issues by applying a theoretical framework based on Dirichlet energy to ensure semantic consistency.
Gradient Flow of Energy: A General and Efficient Approach for Entity Alignment Decoding
However, the decoding process in EA - essential for effective operation and alignment accuracy - has received limited attention and remains tailored to specific datasets and model architectures, necessitating both entity and additional explicit relation embeddings.
Dynamic Support Information Mining for Category-Agnostic Pose Estimation
Category-agnostic pose estimation (CAPE) aims to predict the pose of a query image based on few support images with pose annotations.
Multi-Scale Video Anomaly Detection by Multi-Grained Spatio-Temporal Representation Learning
ecent progress in video anomaly detection suggests that the features of appearance and motion play crucial roles in distinguishing abnormal patterns from normal ones.
An Empirical Study of NetOps Capability of Pre-Trained Large Language Models
Nowadays, the versatile capabilities of Pre-trained Large Language Models (LLMs) have attracted much attention from the industry.
How Does Diffusion Influence Pretrained Language Models on Out-of-Distribution Data?
It shows that finetuning PLMs with diffusion degrades the reconstruction ability on OOD data.
Adaptive DNN Surgery for Selfish Inference Acceleration with On-demand Edge Resource
Deep Neural Networks (DNNs) have significantly improved the accuracy of intelligent applications on mobile devices.
Decoupled Iterative Refinement Framework for Interacting Hands Reconstruction from a Single RGB Image
On the other hand, there are complex spatial relationship between interacting hands, which significantly increases the solution space of hand poses and increases the difficulty of network learning.
Ranked #3 on
3D Interacting Hand Pose Estimation
on InterHand2.6M
Can Shuffling Video Benefit Temporal Bias Problem: A Novel Training Framework for Temporal Grounding
Our framework introduces two auxiliary tasks, cross-modal matching and temporal order discrimination, to promote the grounding model training.
Mining Multi-View Information: A Strong Self-Supervised Framework for Depth-Based 3D Hand Pose and Mesh Estimation
However, these methods ignore the rich semantic information in each view and ignore the complex dependencies between different regions of different views.
Towards automatic visual inspection: A weakly supervised learning method for industrial applicable object detection
With the recent progress of deep learning, advanced industrial object detectors are built for smart industrial applications.
Ranked #28 on
Weakly Supervised Object Detection
on PASCAL VOC 2007
AWR: Adaptive Weighting Regression for 3D Hand Pose Estimation
In this paper, we propose an adaptive weighting regression (AWR) method to leverage the advantages of both detection-based and regression-based methods.
Ranked #1 on
Hand Pose Estimation
on HANDS 2017
Adversarial and Domain-Aware BERT for Cross-Domain Sentiment Analysis
To tackle these problems, we design a post-training procedure, which contains the target domain masked language model task and a novel domain-distinguish pre-training task.
Measuring Generalisation to Unseen Viewpoints, Articulations, Shapes and Objects for 3D Hand Pose Estimation under Hand-Object Interaction
To address these issues, we designed a public challenge (HANDS'19) to evaluate the abilities of current 3D hand pose estimators (HPEs) to interpolate and extrapolate the poses of a training set.
Capsule Network with Interactive Attention for Aspect-Level Sentiment Classification
Aspect-level sentiment classification is a crucial task for sentiment analysis, which aims to identify the sentiment polarities of specific targets in their context.
Investigating Capsule Network and Semantic Feature on Hyperplanes for Text Classification
It has been demonstrated that multiple senses of a word actually reside in linear superposition within the word embedding so that specific senses can be extracted from the original word embedding.
AUTOHOME-ORCA at SemEval-2019 Task 8: Application of BERT for Fact-Checking in Community Forums
In order to address this task, we propose a system based on the BERT model with meta information of questions.
OICSR: Out-In-Channel Sparsity Regularization for Compact Deep Neural Networks
Many channel pruning works utilize structured sparsity regularization to zero out all the weights in some channels and automatically obtain structure-sparse network in training stage.
Fewer is More: Image Segmentation Based Weakly Supervised Object Detection with Partial Aggregation
As most weakly supervised object detection methods are based on pre-generated proposals, they often show two false detections: (i) group multiple object instances with one bounding box, and (ii) focus on only parts rather than the whole objects.
Ranked #31 on
Weakly Supervised Object Detection
on PASCAL VOC 2007
