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Found 34 papers, 15 papers with code
HiTRANS: A Hierarchical Transformer Network for Nested Named Entity Recognition
Specifically, we first utilize a two-phase module to generate span representations by aggregating context information based on a bottom-up and top-down transformer network.
Seeing Far and Clearly: Mitigating Hallucinations in MLLMs with Attention Causal Decoding
With this goal, we present FarSight, a versatile plug-and-play decoding strategy to reduce attention interference from outlier tokens merely by optimizing the causal mask.
Efficient training for large-scale optical neural network using an evolutionary strategy and attention pruning
Considering the influence of dynamic noise in phase shifters, our proposed CAP algorithm (performance degradation of 22. 327% for MNIST dataset and 24. 019% for Fashion-MNIST dataset utilizing a poor fabricated chip and electrical control with a standard deviation of 0. 5) exhibits strongest robustness compared with both our previously reported block adjoint training algorithm (43. 963% and 41. 074%) and the covariance matrix adaptation evolution strategy (25. 757% and 32. 871%), respectively.
SlowFastVAD: Video Anomaly Detection via Integrating Simple Detector and RAG-Enhanced Vision-Language Model
Extensive experiments on four benchmarks demonstrate that SlowFastVAD effectively combines the strengths of both fast and slow detectors, and achieves remarkable detection accuracy and interpretability with significantly reduced computational overhead, making it well-suited for real-world VAD applications with high reliability requirements.
AssistPDA: An Online Video Surveillance Assistant for Video Anomaly Prediction, Detection, and Analysis
To bridge this gap and facilitate the practical deployment of LLM-based VAD, we introduce AssistPDA, the first online video anomaly surveillance assistant that unifies video anomaly prediction, detection, and analysis (VAPDA) within a single framework.
Exploring CLIP's Dense Knowledge for Weakly Supervised Semantic Segmentation
To mine fine-grained knowledge from visual features, our VC module first proposes Static Visual Calibration (SVC) to propagate fine-grained knowledge in a non-parametric manner.
MoRe: Class Patch Attention Needs Regularization for Weakly Supervised Semantic Segmentation
To this end, we first view the attention as a novel directed graph and propose the Graph Category Representation module to implicitly regularize the interaction among class-patch entities.
Weakly supervised Semantic Segmentation
Weakly-Supervised Semantic Segmentation
Boosting ViT-based MRI Reconstruction from the Perspectives of Frequency Modulation, Spatial Purification, and Scale Diversification
In this paper, we propose FPS-Former, a powerful ViT-based framework, to address these issues from the perspectives of frequency modulation, spatial purification, and scale diversification.
Towards Effective, Efficient and Unsupervised Social Event Detection in the Hyperbolic Space
Specifically, the proposed framework first models social messages into semantic-based message anchors, and then leverages the structure of the anchor graph and the expressiveness of the hyperbolic space to acquire structure- and geometry-aware anchor representations.
Continuous K-space Recovery Network with Image Guidance for Fast MRI Reconstruction
In this work, we propose a continuous k-space recovery network from a new perspective of implicit neural representation with image domain guidance, which boosts the performance of MRI reconstruction.
Weakly Supervised Video Anomaly Detection and Localization with Spatio-Temporal Prompts
Existing works typically involve extracting global features from full-resolution video frames and training frame-level classifiers to detect anomalies in the temporal dimension.
Adaptive Differentially Private Structural Entropy Minimization for Unsupervised Social Event Detection
In this process, our method can adaptively apply differential privacy based on the events occurring each day in an open environment to maximize the use of the privacy budget.
Explainable Fake News Detection With Large Language Model via Defense Among Competing Wisdom
To detect fake news from a sea of diverse, crowded and even competing narratives, in this paper, we propose a novel defense-based explainable fake news detection framework.
Tackling Ambiguity from Perspective of Uncertainty Inference and Affinity Diversification for Weakly Supervised Semantic Segmentation
When activating class objects, we argue that the false activation stems from the bias to the ambiguous regions during the feature extraction.
Text Prompt with Normality Guidance for Weakly Supervised Video Anomaly Detection
Further, we propose a learnable text prompt mechanism with the assist of a normality visual prompt to further improve the matching accuracy of video event description text and video frames.
Separate and Conquer: Decoupling Co-occurrence via Decomposition and Representation for Weakly Supervised Semantic Segmentation
In this work, we devise a 'Separate and Conquer' scheme SeCo to tackle this issue from dimensions of image space and feature space.
An efficient dual-branch framework via implicit self-texture enhancement for arbitrary-scale histopathology image super-resolution
Thus, we propose an Implicit Self-Texture Enhancement-based dual-branch framework (ISTE) for arbitrary-scale SR of histopathology images to address this challenge.
WSDMS: Debunk Fake News via Weakly Supervised Detection of Misinforming Sentences with Contextualized Social Wisdom
This model only requires bag-level labels for training but is capable of inferring both sentence-level misinformation and article-level veracity, aided by relevant social media conversations that are attentively contextualized with news sentences.
Knowledge Extraction and Distillation from Large-Scale Image-Text Colonoscopy Records Leveraging Large Language and Vision Models
EndoKED automates the transformation of raw colonoscopy records into image datasets with pixel-level annotation.
OpenAL: An Efficient Deep Active Learning Framework for Open-Set Pathology Image Classification
In this paper, we formulate this scenario as an open-set AL problem and propose an efficient framework, OpenAL, to address the challenge of querying samples from an unlabeled pool with both target class and non-target class samples.
Exploring the Nexus between Exhaustible Human Resources and Economic Development in China: An Application of the Hotelling Model
This paper applies the Hotelling model to the context of exhaustible human resources in China.
SLSG: Industrial Image Anomaly Detection by Learning Better Feature Embeddings and One-Class Classification
Focusing on more effective and comprehensive anomaly detection, we propose a network based on self-supervised learning and self-attentive graph convolution (SLSG) for anomaly detection.
Ranked #8 on
Anomaly Detection
on MVTec LOCO AD
Video Event Restoration Based on Keyframes for Video Anomaly Detection
Video anomaly detection (VAD) is a significant computer vision problem.
Towards Arbitrary-scale Histopathology Image Super-resolution: An Efficient Dual-branch Framework based on Implicit Self-texture Enhancement
Existing super-resolution models for pathology images can only work in fixed integer magnifications and have limited performance.
A Unified Contrastive Transfer Framework with Propagation Structure for Boosting Low-Resource Rumor Detection
The truth is significantly hampered by massive rumors that spread along with breaking news or popular topics.
Boosting Whole Slide Image Classification from the Perspectives of Distribution, Correlation and Magnification
However, there are still three important issues that have not been fully addressed: (1) positive bags with a low positive instance ratio are prone to the influence of a large number of negative instances; (2) the correlation between local and global features of pathology images has not been fully modeled; and (3) there is a lack of effective information interaction between different magnifications.
A Coarse-to-fine Cascaded Evidence-Distillation Neural Network for Explainable Fake News Detection
Existing fake news detection methods aim to classify a piece of news as true or false and provide veracity explanations, achieving remarkable performances.
Ranked #3 on
Fake News Detection
on RAWFC
Dynamic Local Aggregation Network with Adaptive Clusterer for Anomaly Detection
Existing methods for anomaly detection based on memory-augmented autoencoder (AE) have the following drawbacks: (1) Establishing a memory bank requires additional memory space.
The Sufficiency of Off-Policyness and Soft Clipping: PPO is still Insufficient according to an Off-Policy Measure
The popular Proximal Policy Optimization (PPO) algorithm approximates the solution in a clipped policy space.
DecBERT: Enhancing the Language Understanding of BERT with Causal Attention Masks
In contrast, Transformer Decoder with the causal attention masks is naturally sensitive to the word order.
Detect Rumors in Microblog Posts for Low-Resource Domains via Adversarial Contrastive Learning
Massive false rumors emerging along with breaking news or trending topics severely hinder the truth.
Rumor Detection on Twitter with Claim-Guided Hierarchical Graph Attention Networks
Rumors are rampant in the era of social media.
Not only Look, but also Listen: Learning Multimodal Violence Detection under Weak Supervision
Violence detection has been studied in computer vision for years.
Ranked #14 on
Anomaly Detection In Surveillance Videos
on XD-Violence
