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Found 36 papers, 20 papers with code
A Good Practice Towards Top Performance of Face Recognition: Transferred Deep Feature Fusion
In this paper, we propose a unified learning framework named Transferred Deep Feature Fusion (TDFF) targeting at the new IARPA Janus Benchmark A (IJB-A) face recognition dataset released by NIST face challenge.
Look Across Elapse: Disentangled Representation Learning and Photorealistic Cross-Age Face Synthesis for Age-Invariant Face Recognition
Benchmarking our model on one of the most popular unconstrained face recognition datasets IJB-C additionally verifies the promising generalizability of AIM in recognizing faces in the wild.
Ranked #1 on
Age-Invariant Face Recognition
on MORPH Album2
Optimal Adaptive and Accelerated Stochastic Gradient Descent
More specifically, we show that diagonal scaling, initially designed to improve vanilla stochastic gradient, can be incorporated into accelerated stochastic gradient descent to achieve the optimal rate of convergence for smooth stochastic optimization.
6D Pose Estimation with Correlation Fusion
To our best knowledge, this is the first work to explore effective intra- and inter-modality fusion in 6D pose estimation.
Zero-shot Chinese Discourse Dependency Parsing via Cross-lingual Mapping
Due to the absence of labeled data, discourse parsing still remains challenging in some languages.
MedDG: An Entity-Centric Medical Consultation Dataset for Entity-Aware Medical Dialogue Generation
To push forward the future research on building expert-sensitive medical dialogue system, we proposes two kinds of medical dialogue tasks based on MedDG dataset.
Unifying Discourse Resources with Dependency Framework
For text-level discourse analysis, there are various discourse schemes but relatively few labeled data, because discourse research is still immature and it is labor-intensive to annotate the inner logic of a text.
Guiding the Growth: Difficulty-Controllable Question Generation through Step-by-Step Rewriting
This paper explores the task of Difficulty-Controllable Question Generation (DCQG), which aims at generating questions with required difficulty levels.
Self-Supervised Global-Local Structure Modeling for Point Cloud Domain Adaptation With Reliable Voted Pseudo Labels
In this paper, we propose an unsupervised domain adaptation method for deep point cloud representation learning.
Research on Parallel SVM Algorithm Based on Cascade SVM
At the same time, it proves that the accuracy of the model obtained by BCSVM algorithm is higher than that of CSVM.
"My nose is running.""Are you also coughing?": Building A Medical Diagnosis Agent with Interpretable Inquiry Logics
In this paper, we explore how to bring interpretability to data-driven DSMD.
Phased Progressive Learning with Coupling-Regulation-Imbalance Loss for Imbalanced Data Classification
This loss is effective in addressing quantity imbalances and outliers, while regulating the focus of attention on samples with varying classification difficulties.
Team VI-I2R Technical Report on EPIC-KITCHENS-100 Unsupervised Domain Adaptation Challenge for Action Recognition 2021
Based on an existing method for video domain adaptation, i. e., TA3N, we propose to learn hand-centric features by leveraging the hand bounding box information for UDA on fine-grained action recognition.
Improving Multi-turn Emotional Support Dialogue Generation with Lookahead Strategy Planning
Providing Emotional Support (ES) to soothe people in emotional distress is an essential capability in social interactions.
Functional Constrained Optimization for Risk Aversion and Sparsity Control
The DNCG is the first single-loop projection-free method, with iteration complexity bounded by $\mathcal{O}\big(1/\epsilon^4\big)$ for computing a so-called $\epsilon$-Wolfe point.
CARE: Causality Reasoning for Empathetic Responses by Conditional Graph Generation
Recent approaches to empathetic response generation incorporate emotion causalities to enhance comprehension of both the user's feelings and experiences.
Team VI-I2R Technical Report on EPIC-KITCHENS-100 Unsupervised Domain Adaptation Challenge for Action Recognition 2022
In this report, we present the technical details of our submission to the EPIC-KITCHENS-100 Unsupervised Domain Adaptation (UDA) Challenge for Action Recognition 2022.
Robust Image Ordinal Regression with Controllable Image Generation
Image ordinal regression has been mainly studied along the line of exploiting the order of categories.
Medical Dialogue Generation via Dual Flow Modeling
It extracts the medical entities and dialogue acts used in the dialogue history and models their transitions with an entity-centric graph flow and a sequential act flow, respectively.
ORGAN: Observation-Guided Radiology Report Generation via Tree Reasoning
This paper explores the task of radiology report generation, which aims at generating free-text descriptions for a set of radiographs.
A Study on Differentiable Logic and LLMs for EPIC-KITCHENS-100 Unsupervised Domain Adaptation Challenge for Action Recognition 2023
Our research focuses on the innovative application of a differentiable logic loss in the training to leverage the co-occurrence relations between verb and noun, as well as the pre-trained Large Language Models (LLMs) to generate the logic rules for the adaptation to unseen action labels.
Ord2Seq: Regarding Ordinal Regression as Label Sequence Prediction
In this way, we decompose an ordinal regression task into a series of recursive binary classification steps, so as to subtly distinguish adjacent categories.
SCA-PVNet: Self-and-Cross Attention Based Aggregation of Point Cloud and Multi-View for 3D Object Retrieval
With deep features extracted from point clouds and multi-view images, we design two types of feature aggregation modules, namely the In-Modality Aggregation Module (IMAM) and the Cross-Modality Aggregation Module (CMAM), for effective feature fusion.
Keyword-Aware Relative Spatio-Temporal Graph Networks for Video Question Answering
The main challenge in video question answering (VideoQA) is to capture and understand the complex spatial and temporal relations between objects based on given questions.
MCPA: Multi-scale Cross Perceptron Attention Network for 2D Medical Image Segmentation
The Cross Perceptron first captures the local correlations using multiple Multi-scale Cross Perceptron modules, facilitating the fusion of features across scales.
Target-oriented Proactive Dialogue Systems with Personalization: Problem Formulation and Dataset Curation
Target-oriented dialogue systems, designed to proactively steer conversations toward predefined targets or accomplish specific system-side goals, are an exciting area in conversational AI.
Self-Detoxifying Language Models via Toxification Reversal
Drawing on this idea, we devise a method to identify the toxification direction from the normal generation process to the one prompted with the negative prefix, and then steer the generation to the reversed direction by manipulating the information movement within the attention layers.
RECAP: Towards Precise Radiology Report Generation via Dynamic Disease Progression Reasoning
It then combines the historical records, spatiotemporal information, and radiographs for report generation, where a disease progression graph and dynamic progression reasoning mechanism are devised to accurately select the attributes of each observation and progression.
Team I2R-VI-FF Technical Report on EPIC-KITCHENS VISOR Hand Object Segmentation Challenge 2023
In this report, we present our approach to the EPIC-KITCHENS VISOR Hand Object Segmentation Challenge, which focuses on the estimation of the relation between the hands and the objects given a single frame as input.
COOPER: Coordinating Specialized Agents towards a Complex Dialogue Goal
In recent years, there has been a growing interest in exploring dialogues with more complex goals, such as negotiation, persuasion, and emotional support, which go beyond traditional service-focused dialogue systems.
How Far Are We from Believable AI Agents? A Framework for Evaluating the Believability of Human Behavior Simulation
Human behavior simulation of AI agents necessitates the agents to possess a quality of believability, which is crucial as it facilitates users in establishing trust toward the agents and streamlines the fulfillment of the agents' goal.
Medical Dialogue Generation via Intuitive-then-Analytical Differential Diagnosis
Clinicians typically employ both intuitive and analytic reasoning to formulate a differential diagnosis.
Arithmetic Feature Interaction Is Necessary for Deep Tabular Learning
Our extensive experiments on real-world data also validate the consistent effectiveness, efficiency, and rationale of AMFormer, suggesting it has established a strong inductive bias for deep learning on tabular data.
ICON: Improving Inter-Report Consistency of Radiology Report Generation via Lesion-aware Mix-up Augmentation
Previous research on radiology report generation has made significant progress in terms of increasing the clinical accuracy of generated reports.
Multi-rater Prompting for Ambiguous Medical Image Segmentation
In this paper, we tackle two challenges arisen in multi-rater annotations for medical image segmentation (called ambiguous medical image segmentation): (1) How to train a deep learning model when a group of raters produces a set of diverse but plausible annotations, and (2) how to fine-tune the model efficiently when computation resources are not available for re-training the entire model on a different dataset domain.
Agile and versatile bipedal robot tracking control through reinforcement learning
Finally, we evaluate the tracking accuracy of our controller on various bipedal tasks, and the effectiveness of our control framework is verified in the simulation environment.
