Search Results for author:
Found 33 papers, 14 papers with code
EDTC: A Corpus for Discourse-Level Topic Chain Parsing
Discourse analysis has long been known to be fundamental in natural language processing.
Coupling Context Modeling with Zero Pronoun Recovering for Document-Level Natural Language Generation
Natural language generation (NLG) tasks on pro-drop languages are known to suffer from zero pronoun (ZP) problems, and the problems remain challenging due to the scarcity of ZP-annotated NLG corpora.
PromptAD: Learning Prompts with only Normal Samples for Few-Shot Anomaly Detection
The vision-language model has brought great improvement to few-shot industrial anomaly detection, which usually needs to design of hundreds of prompts through prompt engineering.
Exploring Safety Generalization Challenges of Large Language Models via Code
The rapid advancement of Large Language Models (LLMs) has brought about remarkable generative capabilities but also raised concerns about their potential misuse.
Continuous Piecewise-Affine Based Motion Model for Image Animation
To address this issue, we propose to model motion from the source image to the driving frame in highly-expressive diffeomorphism spaces.
Beyond the Label Itself: Latent Labels Enhance Semi-supervised Point Cloud Panoptic Segmentation
Second, in the Image Branch, we propose the Instance Position-scale Learning (IPSL) Module to learn and fuse the information of instance position and scale, which is from a 2D pre-trained detector and a type of latent label obtained from 3D to 2D projection.
Evidence-based Interpretable Open-domain Fact-checking with Large Language Models
Universal fact-checking systems for real-world claims face significant challenges in gathering valid and sufficient real-time evidence and making reasoned decisions.
COTR: Compact Occupancy TRansformer for Vision-based 3D Occupancy Prediction
The autonomous driving community has shown significant interest in 3D occupancy prediction, driven by its exceptional geometric perception and general object recognition capabilities.
Multi-modal In-Context Learning Makes an Ego-evolving Scene Text Recognizer
A straightforward solution is performing model fine-tuning tailored to a specific scenario, but it is computationally intensive and requires multiple model copies for various scenarios.
Generalized Category Discovery in Semantic Segmentation
This paper explores a novel setting called Generalized Category Discovery in Semantic Segmentation (GCDSS), aiming to segment unlabeled images given prior knowledge from a labeled set of base classes.
Boosting Semi-Supervised Learning by Exploiting All Unlabeled Data
ANL introduces the additional negative pseudo-label for all unlabeled data to leverage low-confidence examples.
Multi-Centroid Task Descriptor for Dynamic Class Incremental Inference
As a result, our dynamic inference network is trained independently of baseline and provides a flexible, efficient solution to distinguish between tasks.
Learning To Detect Mirrors From Videos via Dual Correspondences
However, detecting mirrors over dynamic scenes is still under-explored due to the lack of a high-quality dataset and an effective method for video mirror detection (VMD).
Rethinking Gradient Projection Continual Learning: Stability / Plasticity Feature Space Decoupling
In this paper, we propose a space decoupling (SD) algorithm to decouple the feature space into a pair of complementary subspaces, i. e., the stability space I, and the plasticity space R. I is established by conducting space intersection between the historic and current feature space, and thus I contains more task-shared bases.
Instance and Category Supervision are Alternate Learners for Continual Learning
Continual Learning (CL) is the constant development of complex behaviors by building upon previously acquired skills.
Image Understands Point Cloud: Weakly Supervised 3D Semantic Segmentation via Association Learning
In this paper, we propose a novel cross-modality weakly supervised method for 3D segmentation, incorporating complementary information from unlabeled images.
Boosting Night-time Scene Parsing with Learnable Frequency
Based on this, we propose to exploit the image frequency distributions for night-time scene parsing.
Discourse Cohesion Evaluation for Document-Level Neural Machine Translation
It is well known that translations generated by an excellent document-level neural machine translation (NMT) model are consistent and coherent.
Dual Windows Are Significant: Learning from Mediastinal Window and Focusing on Lung Window
Since the pandemic of COVID-19, several deep learning methods were proposed to analyze the chest Computed Tomography (CT) for diagnosis.
Rethinking Efficient Lane Detection via Curve Modeling
This paper presents a novel parametric curve-based method for lane detection in RGB images.
Ranked #2 on
Lane Detection
on LLAMAS
Novelty Detection via Contrastive Learning with Negative Data Augmentation
Moreover, our model is more stable for training in a non-adversarial manner, compared to other adversarial based novelty detection methods.
Omni-supervised Point Cloud Segmentation via Gradual Receptive Field Component Reasoning
Our method can significantly improve the backbones in all three datasets.
Ranked #2 on
Semantic Segmentation
on Semantic3D
Instance and Pair-Aware Dynamic Networks for Re-Identification
The proposed model is composed of three main branches where a self-guided dynamic branch is constructed to strengthen instance-specific features, focusing on every single image.
Boundary-Aware Geometric Encoding for Semantic Segmentation of Point Clouds
Boundary information plays a significant role in 2D image segmentation, while usually being ignored in 3D point cloud segmentation where ambiguous features might be generated in feature extraction, leading to misclassification in the transition area between two objects.
Coreference Resolution: Are the eliminated spans totally worthless?
Various neural-based methods have been proposed so far for joint mention detection and coreference resolution.
Weakly-Supervised Saliency Detection via Salient Object Subitizing
This allows the supervision to be aligned with the property of saliency detection, where the salient objects of an image could be from more than one class.
DMT: Dynamic Mutual Training for Semi-Supervised Learning
Instead, leveraging inter-model disagreement between different models is a key to locate pseudo label errors.
Night-time Scene Parsing with a Large Real Dataset
Although huge progress has been made on scene analysis in recent years, most existing works assume the input images to be in day-time with good lighting conditions.
SceneEncoder: Scene-Aware Semantic Segmentation of Point Clouds with A Learnable Scene Descriptor
Besides local features, global information plays an essential role in semantic segmentation, while recent works usually fail to explicitly extract the meaningful global information and make full use of it.
Hierarchical Modeling of Global Context for Document-Level Neural Machine Translation
In this paper, we propose a hierarchical model to learn the global context for document-level neural machine translation (NMT).
Re-ID Driven Localization Refinement for Person Search
The state-of-the-art methods train the detector individually, and the detected bounding boxes may be sub-optimal for the following re-ID task.
FVNet: 3D Front-View Proposal Generation for Real-Time Object Detection from Point Clouds
We then introduce a proposal generation network to predict 3D region proposals from the generated maps and further extrude objects of interest from the whole point cloud.
Deep Multi-Center Learning for Face Alignment
Most of the existing deep learning methods only use one fully-connected layer called shape prediction layer to estimate the locations of facial landmarks.
Ranked #3 on
Face Alignment
on AFLW2000
