Search Results for author:
Found 34 papers, 16 papers with code
BatchNorm-based Weakly Supervised Video Anomaly Detection
In the proposed BN-WVAD, we leverage the Divergence of Feature from Mean vector (DFM) of BatchNorm as a reliable abnormality criterion to discern potential abnormal snippets in abnormal videos.
Anomaly Detection In Surveillance Videos
Video Anomaly Detection
X-HRNet: Towards Lightweight Human Pose Estimation with Spatially Unidimensional Self-Attention
Inspired by this observation, we introduce a lightweight and powerful alternative, Spatially Unidimensional Self-Attention (SUSA), to the pointwise (1x1) convolution that is the main computational bottleneck in the depthwise separable 3c3 convolution.
MSFlow: Multi-Scale Flow-based Framework for Unsupervised Anomaly Detection
Unsupervised anomaly detection (UAD) attracts a lot of research interest and drives widespread applications, where only anomaly-free samples are available for training.
Ranked #5 on
Anomaly Detection
on MVTec AD
MoCoSA: Momentum Contrast for Knowledge Graph Completion with Structure-Augmented Pre-trained Language Models
However, they struggle with semantically rich real-world entities due to limited structural information and fail to generalize to unseen entities.
Ranked #1 on
Link Prediction
on WN18RR
ImbSAM: A Closer Look at Sharpness-Aware Minimization in Class-Imbalanced Recognition
To overcome this bottleneck, we leverage class priors to restrict the generalization scope of the class-agnostic SAM and propose a class-aware smoothness optimization algorithm named Imbalanced-SAM (ImbSAM).
Semi-supervised Anomaly Detection
Supervised Anomaly Detection
Unifying Two-Stream Encoders with Transformers for Cross-Modal Retrieval
Specifically, on two key tasks, \textit{i. e.}, image-to-text and text-to-image retrieval, HAT achieves 7. 6\% and 16. 7\% relative score improvement of Recall@1 on MSCOCO, and 4. 4\% and 11. 6\% on Flickr30k respectively.
Do-GOOD: Towards Distribution Shift Evaluation for Pre-Trained Visual Document Understanding Models
Results from the experiments demonstrate that there is a significant performance gap between the in-distribution (ID) and OOD settings for document images, and that fine-grained analysis of distribution shifts can reveal the brittle nature of existing pre-trained VDU models and OOD generalization algorithms.
AnoOnly: Semi-Supervised Anomaly Detection with the Only Loss on Anomalies
Unlike existing SSAD methods that resort to strict loss supervision, AnoOnly suspends it and introduces a form of weak supervision for normal data.
Semi-supervised Anomaly Detection
Supervised Anomaly Detection
+1
Faster Video Moment Retrieval with Point-Level Supervision
Existing VMR methods suffer from two defects: (1) massive expensive temporal annotations are required to obtain satisfying performance; (2) complicated cross-modal interaction modules are deployed, which lead to high computational cost and low efficiency for the retrieval process.
T-SciQ: Teaching Multimodal Chain-of-Thought Reasoning via Mixed Large Language Model Signals for Science Question Answering
To address these issues, we propose a novel method termed T-SciQ that aims at teaching science question answering with LLM signals.
LLM-Adapters: An Adapter Family for Parameter-Efficient Fine-Tuning of Large Language Models
The success of large language models (LLMs), like GPT-4 and ChatGPT, has led to the development of numerous cost-effective and accessible alternatives that are created by finetuning open-access LLMs with task-specific data (e. g., ChatDoctor) or instruction data (e. g., Alpaca).
ICL-D3IE: In-Context Learning with Diverse Demonstrations Updating for Document Information Extraction
To this end, we propose a simple but effective in-context learning framework called ICL-D3IE, which enables LLMs to perform DIE with different types of demonstration examples.
Semantic Enhanced Knowledge Graph for Large-Scale Zero-Shot Learning
In this paper, we provide a new semantic enhanced knowledge graph that contains both expert knowledge and categories semantic correlation.
Alignment-Enriched Tuning for Patch-Level Pre-trained Document Image Models
In this paper, we propose a new model architecture with alignment-enriched tuning (dubbed AETNet) upon pre-trained document image models, to adapt downstream tasks with the joint task-specific supervised and alignment-aware contrastive objective.
Thunder: Thumbnail based Fast Lightweight Image Denoising Network
To achieve promising results on removing noise from real-world images, most of existing denoising networks are formulated with complex network structure, making them impractical for deployment.
Communication-Efficient Consensus Mechanism for Federated Reinforcement Learning
The paper considers independent reinforcement learning (IRL) for multi-agent decision-making process in the paradigm of federated learning (FL).
Semi-Supervised Video Paragraph Grounding With Contrastive Encoder
Video events grounding aims at retrieving the most relevant moments from an untrimmed video in terms of a given natural language query.
I-WKNN: Fast-Speed and High-Accuracy WIFI Positioning for Intelligent Stadiums
The experimental results show that the I-WKNN algorithm has obvious advantages in positioning accuracy and positioning time in a complex noise environment and has obvious application potential in a smart stadium.
From General to Specific: Informative Scene Graph Generation via Balance Adjustment
The scene graph generation (SGG) task aims to detect visual relationship triplets, i. e., subject, predicate, object, in an image, providing a structural vision layout for scene understanding.
Multi-Stage Aggregated Transformer Network for Temporal Language Localization in Videos
Then for a moment candidate, we concatenate the starting/middle/ending representations of its starting/middle/ending elements respectively to form the final moment representation.
Partial Feature Selection and Alignment for Multi-Source Domain Adaptation
By assuming that the source and target domains share consistent key feature representations and identical label space, existing studies on MSDA typically utilize the entire union set of features from both the source and target domains to obtain the feature map and align the map for each category and domain.
Feature Space Targeted Attacks by Statistic Alignment
However, the current choice of pixel-wise Euclidean Distance to measure the discrepancy is questionable because it unreasonably imposes a spatial-consistency constraint on the source and target features.
The Gradient Convergence Bound of Federated Multi-Agent Reinforcement Learning with Efficient Communication
The paper considers independent reinforcement learning (IRL) for multi-agent collaborative decision-making in the paradigm of federated learning (FL).
Universal Weighting Metric Learning for Cross-Modal Matching
Furthermore, we introduce a new polynomial loss under the universal weighting framework, which defines a weight function for the positive and negative informative pairs respectively.
What Machines See Is Not What They Get: Fooling Scene Text Recognition Models With Adversarial Text Images
Specifically, we propose a novel and efficient optimization-based method that can be naturally integrated to different sequential prediction schemes, i. e., connectionist temporal classification (CTC) and attention mechanism.
Stigmergic Independent Reinforcement Learning for Multi-Agent Collaboration
With the rapid evolution of wireless mobile devices, there emerges an increased need to design effective collaboration mechanisms between intelligent agents, so as to gradually approach the final collective objective through continuously learning from the environment based on their individual observations.
Temporal Reasoning Graph for Activity Recognition
In this paper, we propose an efficient temporal reasoning graph (TRG) to simultaneously capture the appearance features and temporal relation between video sequences at multiple time scales.
Ranked #53 on
Action Recognition
on Something-Something V1
Cooperative Cross-Stream Network for Discriminative Action Representation
It extracts this complementary information of different modality from a connection block, which aims at exploring correlations of different stream features.
Ranked #15 on
Action Recognition
on HMDB-51
(using extra training data)
Matching Images and Text with Multi-modal Tensor Fusion and Re-ranking
We propose a novel framework that achieves remarkable matching performance with acceptable model complexity.
Template-based math word problem solvers with recursive neural networks
Then, we design a recursive neural network to encode the quantity with Bi-LSTM and self attention, and infer the unknown operator nodes in a bottom-up manner.
Internet of Intelligence: The Collective Advantage for Advancing Communications and Intelligence
Afterwards, we highlight the potential huge impact of CI on both communications and intelligence.
Matrix Tri-Factorization With Manifold Regularizations for Zero-Shot Learning
By additionally introducing manifold regularizations on visual data and semantic embeddings, the learned projection can effectively captures the geometrical manifold structure residing in both visual and semantic spaces.
Deep Region Hashing for Efficient Large-scale Instance Search from Images
Specifically, DRH is an end-to-end deep neural network which consists of object proposal, feature extraction, and hash code generation.
Bidirectional Long-Short Term Memory for Video Description
Video captioning has been attracting broad research attention in multimedia community.
