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Found 54 papers, 15 papers with code
眼动记录与主旨结构标注的关联性分析研究(Research on the correlation between eye movement feature and thematic structure label)
给定包含主旨概括句的汉语句群, 针对该句群的内部结构标注是基于语言学的分析结果, 而阅读句群时的眼动轨迹则蕴含着人的心理认知, 两者的信息融合和内在关联性分析是该文主要工作。该文使用基于径向基函数支持向量机和递归特征消除的分类模型, 根据标点小句片段对应的眼动指标数据预测该片段是否为包含主旨内容的关键信息, 达到了0. 76的准确率, 并通过分析关键片段上眼动数据的分布特点, 提取出对句群主旨概括信息区分度较好的眼动指标。
Speaker Clustering in Textual Dialogue with Pairwise Utterance Relation and Cross-corpus Dialogue Act Supervision
We propose a speaker clustering model for textual dialogues, which groups the utterances of a multi-party dialogue without speaker annotations, so that the actual speakers are identical inside each cluster.
Continuous-Multiple Image Outpainting in One-Step via Positional Query and A Diffusion-based Approach
At inference, we generate images with arbitrary expansion multiples by inputting an anchor image and its corresponding positional embeddings.
A Dataset and Benchmark for Copyright Protection from Text-to-Image Diffusion Models
Copyright is a legal right that grants creators the exclusive authority to reproduce, distribute, and profit from their creative works.
DMT: Comprehensive Distillation with Multiple Self-supervised Teachers
Numerous self-supervised learning paradigms, such as contrastive learning and masked image modeling, have been proposed to acquire powerful and general representations from unlabeled data.
Language-guided Few-shot Semantic Segmentation
Few-shot learning is a promising way for reducing the label cost in new categories adaptation with the guidance of a small, well labeled support set.
InfMLLM: A Unified Framework for Visual-Language Tasks
Our experiments demonstrate that preserving the positional information of visual embeddings through the pool-adapter is particularly beneficial for tasks like visual grounding.
Ranked #62 on
Visual Question Answering
on MM-Vet
PAD: A Dataset and Benchmark for Pose-agnostic Anomaly Detection
Furthermore, we provide an open-source benchmark library, including dataset and baseline methods that cover 8 anomaly detection paradigms, to facilitate future research and application in this domain.
Ranked #2 on
Anomaly Detection
on PAD Dataset
A ModelOps-based Framework for Intelligent Medical Knowledge Extraction
Extracting medical knowledge from healthcare texts enhances downstream tasks like medical knowledge graph construction and clinical decision-making.
ICPC: Instance-Conditioned Prompting with Contrastive Learning for Semantic Segmentation
Second, we propose an align-guided contrastive loss to refine the alignment of vision and text embeddings.
ES-MVSNet: Efficient Framework for End-to-end Self-supervised Multi-View Stereo
to provide additional consistency constraints, which grows GPU memory consumption and complicates the model's structure and training pipeline.
Dynamic Token-Pass Transformers for Semantic Segmentation
Vision transformers (ViT) usually extract features via forwarding all the tokens in the self-attention layers from top to toe.
RegionBLIP: A Unified Multi-modal Pre-training Framework for Holistic and Regional Comprehension
To this end, we propose to extract features corresponding to regional objects as soft prompts for LLM, which provides a straightforward and scalable approach and eliminates the need for LLM fine-tuning.
Improved Neural Radiance Fields Using Pseudo-depth and Fusion
To make the constructed volumes as close as possible to the surfaces of objects in the scene and the rendered depth more accurate, we propose to perform depth prediction and radiance field reconstruction simultaneously.
Points-to-3D: Bridging the Gap between Sparse Points and Shape-Controllable Text-to-3D Generation
To better utilize the sparse 3D points, we propose an efficient point cloud guidance loss to adaptively drive the NeRF's geometry to align with the shape of the sparse 3D points.
DPF: Learning Dense Prediction Fields with Weak Supervision
We showcase the effectiveness of DPFs using two substantially different tasks: high-level semantic parsing and low-level intrinsic image decomposition.
D2Q-DETR: Decoupling and Dynamic Queries for Oriented Object Detection with Transformers
In this paper, we propose an end-to-end framework for oriented object detection, which simplifies the model pipeline and obtains superior performance.
Foundation Model Drives Weakly Incremental Learning for Semantic Segmentation
In this work, we propose a novel and data-efficient framework for WILSS, named FMWISS.
LMSeg: Language-guided Multi-dataset Segmentation
Instead of relabeling each dataset with the unified taxonomy, a category-guided decoding module is designed to dynamically guide predictions to each datasets taxonomy.
MimCo: Masked Image Modeling Pre-training with Contrastive Teacher
Specifically, MimCo takes a pre-trained contrastive learning model as the teacher model and is pre-trained with two types of learning targets: patch-level and image-level reconstruction losses.
Point-Teaching: Weakly Semi-Supervised Object Detection with Point Annotations
Point annotations are considerably more time-efficient than bounding box annotations.
Point RCNN: An Angle-Free Framework for Rotated Object Detection
To tackle this problem, we propose a purely angle-free framework for rotated object detection, called Point RCNN, which mainly consists of PointRPN and PointReg.
UnrealNAS: Can We Search Neural Architectures with Unreal Data?
Neural architecture search (NAS) has shown great success in the automatic design of deep neural networks (DNNs).
Fix Bugs with Transformer through a Neural-Symbolic Edit Grammar
We introduce NSEdit (neural-symbolic edit), a novel Transformer-based code repair method.
GAMMA Challenge:Glaucoma grAding from Multi-Modality imAges
However, although numerous algorithms are proposed based on fundus images or OCT volumes in computer-aided diagnosis, there are still few methods leveraging both of the modalities for the glaucoma assessment.
LUAI Challenge 2021 on Learning to Understand Aerial Images
This report summarizes the results of Learning to Understand Aerial Images (LUAI) 2021 challenge held on ICCV 2021, which focuses on object detection and semantic segmentation in aerial images.
Delving Deep into the Generalization of Vision Transformers under Distribution Shifts
By comprehensively investigating these GE-ViTs and comparing with their corresponding CNN models, we observe: 1) For the enhanced model, larger ViTs still benefit more for the OOD generalization.
CAPTRA: CAtegory-level Pose Tracking for Rigid and Articulated Objects from Point Clouds
For the first time, we propose a unified framework that can handle 9DoF pose tracking for novel rigid object instances as well as per-part pose tracking for articulated objects from known categories.
Human De-occlusion: Invisible Perception and Recovery for Humans
Besides, an Amodal Human Perception dataset (AHP) is collected to settle the task of human de-occlusion.
Instant-Teaching: An End-to-End Semi-Supervised Object Detection Framework
To alleviate the confirmation bias problem and improve the quality of pseudo annotations, we further propose a co-rectify scheme based on Instant-Teaching, denoted as Instant-Teaching$^*$.
Ranked #12 on
Semi-Supervised Object Detection
on COCO 100% labeled data
(using extra training data)
Object Detection Made Simpler by Eliminating Heuristic NMS
On the COCO dataset, our simple design achieves superior performance compared to both the FCOS baseline detector with NMS post-processing and the recent end-to-end NMS-free detectors.
Modeling Heterogeneous Relations across Multiple Modes for Potential Crowd Flow Prediction
Intuitively, the potential crowd flow of the new coming site can be implied by exploring the nearby sites.
Coherent optical communications using coherence-cloned Kerr soliton microcombs
Dissipative Kerr soliton microcomb has been recognized as a promising on-chip multi-wavelength laser source for fiber optical communications, as its comb lines possess frequency and phase stability far beyond independent lasers.
A Tree-structure Convolutional Neural Network for Temporal Features Exaction on Sensor-based Multi-resident Activity Recognition
With the propagation of sensor devices applied in smart home, activity recognition has ignited huge interest and most existing works assume that there is only one habitant.
Exploiting Interpretable Patterns for Flow Prediction in Dockless Bike Sharing Systems
Unlike the traditional dock-based systems, dockless bike-sharing systems are more convenient for users in terms of flexibility.
TransMoMo: Invariance-Driven Unsupervised Video Motion Retargeting
We present a lightweight video motion retargeting approach TransMoMo that is capable of transferring motion of a person in a source video realistically to another video of a target person.
Deep Learning-based Detection for COVID-19 from Chest CT using Weak Label
Our weakly-supervised deep learning model can accurately predict the COVID-19 infectious probability in chest CT volumes without the need for annotating the lesions for training.
FAB: A Robust Facial Landmark Detection Framework for Motion-Blurred Videos
A structure predictor is proposed to predict the missing face structural information temporally, which serves as a geometry prior.
Proposal, Tracking and Segmentation (PTS): A Cascaded Network for Video Object Segmentation
Video object segmentation (VOS) aims at pixel-level object tracking given only the annotations in the first frame.
Ranked #1 on
Visual Object Tracking
on YouTube-VOS 2018
(Jaccard (Seen) metric)
STN-Homography: estimate homography parameters directly
In this paper, we introduce the STN-Homography model to directly estimate the homography matrix between image pair.
Look at Boundary: A Boundary-Aware Face Alignment Algorithm
By utilising boundary information of 300-W dataset, our method achieves 3. 92% mean error with 0. 39% failure rate on COFW dataset, and 1. 25% mean error on AFLW-Full dataset.
Ranked #4 on
Face Alignment
on AFLW-19
(using extra training data)
Exploiting Spin-Orbit Torque Devices as Reconfigurable Logic for Circuit Obfuscation
Circuit obfuscation is a frequently used approach to conceal logic functionalities in order to prevent reverse engineering attacks on fabricated chips.
Emerging Technologies Cryptography and Security
A Novel Fast Framework for Topic Labeling Based on Similarity-preserved Hashing
Recently, topic modeling has been widely applied in data mining due to its powerful ability.
Distant Supervision for Entity Linking
In this paper, we propose a new paradigm named distantly supervised entity linking (DSEL), in the sense that the disambiguated entities that belong to a huge knowledge repository (Freebase) are automatically aligned to the corresponding descriptive webpages (Wiki pages).
Probabilistic Belief Embedding for Knowledge Base Completion
This paper contributes a novel embedding model which measures the probability of each belief $\langle h, r, t, m\rangle$ in a large-scale knowledge repository via simultaneously learning distributed representations for entities ($h$ and $t$), relations ($r$), and the words in relation mentions ($m$).
Large Margin Nearest Neighbor Embedding for Knowledge Representation
Traditional way of storing facts in triplets ({\it head\_entity, relation, tail\_entity}), abbreviated as ({\it h, r, t}), makes the knowledge intuitively displayed and easily acquired by mankind, but hardly computed or even reasoned by AI machines.
Learning Embedding Representations for Knowledge Inference on Imperfect and Incomplete Repositories
This paper considers the problem of knowledge inference on large-scale imperfect repositories with incomplete coverage by means of embedding entities and relations at the first attempt.
Errata: Distant Supervision for Relation Extraction with Matrix Completion
The essence of distantly supervised relation extraction is that it is an incomplete multi-label classification problem with sparse and noisy features.
