Search Results for author:
Found 31 papers, 3 papers with code
User Satisfaction Modeling with Domain Adaptation in Task-oriented Dialogue Systems
To the best of our knowledge, this is the first work to study user satisfaction estimation with unsupervised domain adaptation from chitchat to task-oriented dialogue.
Enhanced Multi-Tuple Extraction for Alloys: Integrating Pointer Networks and Augmented Attention
Extracting high-quality structured information from scientific literature is crucial for advancing material design through data-driven methods.
Cool-Fusion: Fuse Large Language Models without Training
We focus on the problem of fusing two or more heterogeneous large language models (LLMs) to facilitate their complementary strengths.
OTO Planner: An Efficient Only Travelling Once Exploration Planner for Complex and Unknown Environments
OTO Planner includes fast frontier updating, viewpoint evaluation and viewpoint refinement.
The Future of Cognitive Strategy-enhanced Persuasive Dialogue Agents: New Perspectives and Trends
To depict the research trends of CogAgent, in this paper, we first present several fundamental cognitive psychology theories and give the formalized definition of three typical cognitive strategies, including the persuasion strategy, the topic path planning strategy, and the argument structure prediction strategy.
CoRaiS: Lightweight Real-Time Scheduler for Multi-Edge Cooperative Computing
CoRaiS embeds the real-time states of multi-edge system and requests information, and combines the embeddings with a policy network to schedule the requests, so that the response time of all requests can be minimized.
Improving Entropy-Based Test-Time Adaptation from a Clustering View
This new perspective allows us to explore how entropy minimization influences test-time adaptation.
Camera-LiDAR Fusion with Latent Contact for Place Recognition in Challenging Cross-Scenes
Although significant progress has been made, achieving place recognition in environments with perspective changes, seasonal variations, and scene transformations remains challenging.
Toward Understanding Why Adam Converges Faster Than SGD for Transformers
We further observe that only a small fraction of the coordinates causes the bad sharpness and slow convergence of SGD, and propose to use coordinate-wise clipping as a solution to SGD and other optimization algorithms.
MotionBEV: Attention-Aware Online LiDAR Moving Object Segmentation with Bird's Eye View based Appearance and Motion Features
In this paper, we present MotionBEV, a fast and accurate framework for LiDAR moving object segmentation, which segments moving objects with appearance and motion features in the bird's eye view (BEV) domain.
Deep Hashing With Minimal-Distance-Separated Hash Centers
This paper presents an optimization method that finds hash centers with a constraint on the minimal distance between any pair of hash centers, which is non-trivial due to the non-convex nature of the problem.
Few-Shot Nested Named Entity Recognition
Leveraging contextual dependency to distinguish nested entities, we propose a Biaffine-based Contrastive Learning (BCL) framework.
Optimizing Evaluation Metrics for Multi-Task Learning via the Alternating Direction Method of Multipliers
The formulation of MTL that directly optimizes evaluation metrics is the combination of two parts: (1) a regularizer defined on the weight matrix over all tasks, in order to capture the relatedness of these tasks; (2) a sum of multiple structured hinge losses, each corresponding to a surrogate of some evaluation metric on one task.
ViT2Hash: Unsupervised Information-Preserving Hashing
Specifically, from pixels to continuous features, we first propose a feature-preserving module, using the corrupted image as input to reconstruct the original feature from the pre-trained ViT model and the complete image, so that the feature extractor can focus on preserving the meaningful information of original data.
Expressive Talking Head Generation With Granular Audio-Visual Control
Generating expressive talking heads is essential for creating virtual humans.
Can depth-adaptive BERT perform better on binary classification tasks
In light of the success of transferring language models into NLP tasks, we ask whether the full BERT model is always the best and does it exist a simple but effective method to find the winning ticket in state-of-the-art deep neural networks without complex calculations.
3DCaricShop: A Dataset and A Baseline Method for Single-view 3D Caricature Face Reconstruction
Caricature is an artistic representation that deliberately exaggerates the distinctive features of a human face to convey humor or sarcasm.
Distributed Low Precision Training Without Mixed Precision
However, most low precision training solution is based on a mixed precision strategy.
Feature Pyramid Hashing
In recent years, deep-networks-based hashing has become a leading approach for large-scale image retrieval.
Improving Deep Binary Embedding Networks by Order-aware Reweighting of Triplets
In this paper, we focus on triplet-based deep binary embedding networks for image retrieval task.
Regularizing Deep Hashing Networks Using GAN Generated Fake Images
To address this issue, in this paper, we propose a simple two-stage pipeline to learn deep hashing models, by regularizing the deep hashing networks using fake images.
HashGAN:Attention-aware Deep Adversarial Hashing for Cross Modal Retrieval
The proposed new adversarial network, HashGAN, consists of three building blocks: 1) the feature learning module to obtain feature representations, 2) the generative attention module to generate an attention mask, which is used to obtain the attended (foreground) and the unattended (background) feature representations, 3) the discriminative hash coding module to learn hash functions that preserve the similarities between different modalities.
Personalized and Occupational-aware Age Progression by Generative Adversarial Networks
Second, we propose a new occupational-aware adversarial face aging network, which learns human aging process under different occupations.
Transductive Zero-Shot Hashing via Coarse-to-Fine Similarity Mining
It mainly consists of two building blocks in the proposed deep architecture: 1) a shared two-streams network, which the first stream operates on the source data and the second stream operates on the unlabeled data, to learn the effective common image representations, and 2) a coarse-to-fine module, which begins with finding the most representative images from target classes and then further detect similarities among these images, to transfer the similarities of the source data to the target data in a greedy fashion.
Improved Search in Hamming Space using Deep Multi-Index Hashing
Similarity-preserving hashing is a widely-used method for nearest neighbour search in large-scale image retrieval tasks.
Modelling Sentence Pairs with Tree-structured Attentive Encoder
We describe an attentive encoder that combines tree-structured recursive neural networks and sequential recurrent neural networks for modelling sentence pairs.
Deep Recurrent Regression for Facial Landmark Detection
We propose a novel end-to-end deep architecture for face landmark detection, based on a deep convolutional and deconvolutional network followed by carefully designed recurrent network structures.
Simultaneous Feature Learning and Hash Coding with Deep Neural Networks
Similarity-preserving hashing is a widely-used method for nearest neighbour search in large-scale image retrieval tasks.
Integrating Graph Partitioning and Matching for Trajectory Analysis in Video Surveillance
With this graph representation, we pose trajectory analysis as a joint task of spatial graph partitioning and temporal graph matching.
A Divide-and-Conquer Method for Scalable Low-Rank Latent Matrix Pursuit
To address this issue, we provide a scalable solution for large-scale low-rank latent matrix pursuit by a divide-andconquer method.
