Search Results for author:
Found 37 papers, 15 papers with code
BioD2C: A Dual-level Semantic Consistency Constraint Framework for Biomedical VQA
Biomedical visual question answering (VQA) has been widely studied and has demonstrated significant application value and potential in fields such as assistive medical diagnosis.
Text-promptable Propagation for Referring Medical Image Sequence Segmentation
We develop a strong baseline model, Text-Promptable Propagation (TPP), designed to exploit the intrinsic relationships among sequential images and their associated textual descriptions.
Large Language Models for Bioinformatics
With the rapid advancements in large language model (LLM) technology and the emergence of bioinformatics-specific language models (BioLMs), there is a growing need for a comprehensive analysis of the current landscape, computational characteristics, and diverse applications.
Computer Vision-Driven Gesture Recognition: Toward Natural and Intuitive Human-Computer
This paper proposes a gesture recognition method based on a three-dimensional hand skeleton model.
Hyperbolic Chamfer Distance for Point Cloud Completion and Beyond
In divergence from the existing literature, which largely concentrates on resolving such concerns in the realm of Euclidean space, we put forth a notably uncomplicated yet potent metric specifically designed for point cloud completion tasks: {Hyperbolic Chamfer Distance (HyperCD)}.
SPADE: Spectroscopic Photoacoustic Denoising using an Analytical and Data-free Enhancement Framework
This framework integrates a data-free learning-based method with an efficient BM3D-based analytical approach while preserves spectral linearity, providing noise reduction and ensuring that functional information is maintained.
GSSF: Generalized Structural Sparse Function for Deep Cross-modal Metric Learning
Cross-modal metric learning is a prominent research topic that bridges the semantic heterogeneity between vision and language.
Measuring the Groundedness of Legal Question-Answering Systems
In summary, this study demonstrates the potential of various detection methods to improve the trustworthiness of generative AI in legal settings.
A Little Confidence Goes a Long Way
We introduce a group of related methods for binary classification tasks using probes of the hidden state activations in large language models (LLMs).
Self-Attention-Based Contextual Modulation Improves Neural System Identification
We find that self-attention can replace posterior spatial-integration convolutions when learned incrementally, and is further enhanced in the presence of a fully connected readout layer, suggesting that the two context mechanisms are complementary.
GRAMMAR: Grounded and Modular Methodology for Assessment of Closed-Domain Retrieval-Augmented Language Model
Retrieval-Augmented Generation (RAG) systems are widely used across various industries for querying closed-domain and in-house knowledge bases.
Deep Boosting Learning: A Brand-new Cooperative Approach for Image-Text Matching
Specifically, we propose a brand-new Deep Boosting Learning (DBL) algorithm, where an anchor branch is first trained to provide insights into the data properties, with a target branch gaining more advanced knowledge to develop optimal features and distance metrics.
Can't Remember Details in Long Documents? You Need Some R&R
Long-context large language models (LLMs) hold promise for tasks such as question-answering (QA) over long documents, but they tend to miss important information in the middle of context documents (arXiv:2307. 03172v3).
Part Representation Learning with Teacher-Student Decoder for Occluded Person Re-identification
In addition, existing occluded person ReID benchmarks utilize occluded samples as queries, which will amplify the role of alleviating occlusion interference and underestimate the impact of the feature absence issue.
Learning to Learn for Few-shot Continual Active Learning
Recent advances in continual learning are mostly confined to a supervised learning setting, especially in NLP domain.
Enhanced Knowledge Injection for Radiology Report Generation
Automatic generation of radiology reports holds crucial clinical value, as it can alleviate substantial workload on radiologists and remind less experienced ones of potential anomalies.
LegalBench: A Collaboratively Built Benchmark for Measuring Legal Reasoning in Large Language Models
The advent of large language models (LLMs) and their adoption by the legal community has given rise to the question: what types of legal reasoning can LLMs perform?
Make Text Unlearnable: Exploiting Effective Patterns to Protect Personal Data
This paper addresses the ethical concerns arising from the use of unauthorized public data in deep learning models and proposes a novel solution.
A Survey on Out-of-Distribution Evaluation of Neural NLP Models
Adversarial robustness, domain generalization and dataset biases are three active lines of research contributing to out-of-distribution (OOD) evaluation on neural NLP models.
Can Pretrained Language Models Derive Correct Semantics from Corrupt Subwords under Noise?
For Pretrained Language Models (PLMs), their susceptibility to noise has recently been linked to subword segmentation.
Track Anything: Segment Anything Meets Videos
Therefore, in this report, we propose Track Anything Model (TAM), which achieves high-performance interactive tracking and segmentation in videos.
Anomaly Detection of UAV State Data Based on Single-class Triangular Global Alignment Kernel Extreme Learning Machine
To improve the performance of OCKELM, we choose a Triangular Global Alignment Kernel (TGAK) instead of an RBF Kernel and introduce the Fast Independent Component Analysis (FastICA) algorithm to reconstruct UAV data.
Resource-Efficient RGBD Aerial Tracking
However, current research on aerial perception has mainly focused on limited categories, such as pedestrian or vehicle, and most scenes are captured in urban environments from a birds-eye view.
DGD-cGAN: A Dual Generator for Image Dewatering and Restoration
Our Dual Generator Dewatering cGAN (DGD-cGAN) removes the haze and colour cast induced by the water column and restores the true colours of underwater scenes whereby the effects of various attenuation and scattering phenomena that occur in underwater images are tackled by the two generators.
Learning Dual-Fused Modality-Aware Representations for RGBD Tracking
However, some existing RGBD trackers use the two modalities separately and thus some particularly useful shared information between them is ignored.
Graph Classification via Discriminative Edge Feature Learning
Three new graph datasets are constructed based on ModelNet40, ModelNet10 and ShapeNet Part datasets.
CREAM: Weakly Supervised Object Localization via Class RE-Activation Mapping
In this paper, we empirically prove that this problem is associated with the mixup of the activation values between less discriminative foreground regions and the background.
Fault Diagnosis of Discrete-Event Systems under Non-Deterministic Observations with Output Fairness
Non-deterministic observation is a general observation model that includes the case of intermittent loss of observations.
Towards Uniform Point Distribution in Feature-preserving Point Cloud Filtering
As a popular representation of 3D data, point cloud may contain noise and need to be filtered before use.
Automated Security Assessment for the Internet of Things
This security model automatically assesses the security of the IoT network by capturing potential attack paths.
3D Face Recognition: A Survey
Face recognition is one of the most studied research topics in the community.
Hierarchical excitations from correlated spin tetrahedra on the breathing pyrochlore lattice
The hierarchy of the coupling strengths in a physical system often engenders an effective model at low energies where the decoupled high-energy modes are integrated out.
Strongly Correlated Electrons
Integration of Domain Knowledge using Medical Knowledge Graph Deep Learning for Cancer Phenotyping
Word embeddings that effectively capture the meaning and context of the word that they represent can significantly improve the performance of downstream DL models for various NLP tasks.
A catastrophic charge density wave in BaFe$_2$Al$_9$
Single crystal x-ray diffraction reveals super-lattice peaks in the low-temperature phase signaling the development of a CDW lattice modulation.
Strongly Correlated Electrons Materials Science
Pyramid Spatial-Temporal Aggregation for Video-Based Person Re-Identification
Video-based person re-identification aims to associate the video clips of the same person across multiple non-overlapping cameras.
Ranked #1 on
Person Re-Identification
on DukeMTMC-VideoReID
Pose-guided Visible Part Matching for Occluded Person ReID
Occluded person re-identification is a challenging task as the appearance varies substantially with various obstacles, especially in the crowd scenario.
Hierarchical Convolutional Attention Networks for Text Classification
Recent work in machine translation has demonstrated that self-attention mechanisms can be used in place of recurrent neural networks to increase training speed without sacrificing model accuracy.
