Search Results for author:
Found 35 papers, 17 papers with code
DanceCamera3D: 3D Camera Movement Synthesis with Music and Dance
However, camera movement synthesis with music and dance remains an unsolved challenging problem due to the scarcity of paired data.
AlphaFin: Benchmarking Financial Analysis with Retrieval-Augmented Stock-Chain Framework
The task of financial analysis primarily encompasses two key areas: stock trend prediction and the corresponding financial question answering.
A Vanilla Multi-Task Framework for Dense Visual Prediction Solution to 1st VCL Challenge -- Multi-Task Robustness Track
In this report, we present our solution to the multi-task robustness track of the 1st Visual Continual Learning (VCL) Challenge at ICCV 2023 Workshop.
AvatarMMC: 3D Head Avatar Generation and Editing with Multi-Modal Conditioning
To the best of our knowledge, our approach is the first to introduce multi-modal conditioning to 3D avatar generation and editing.
UniMem: Towards a Unified View of Long-Context Large Language Models
Although there exist various methods devoted to enhancing the long-context processing ability of large language models (LLMs), they are developed in an isolated manner and lack systematic analysis and integration of their strengths, hindering further developments.
PatchFusion: An End-to-End Tile-Based Framework for High-Resolution Monocular Metric Depth Estimation
Single image depth estimation is a foundational task in computer vision and generative modeling.
Enhancing the Spatial Awareness Capability of Multi-Modal Large Language Model
The Multi-Modal Large Language Model (MLLM) refers to an extension of the Large Language Model (LLM) equipped with the capability to receive and infer multi-modal data.
Enhancing Subtask Performance of Multi-modal Large Language Model
Finally, the results from multiple pre-trained models for the same subtask are compared using the LLM, and the best result is chosen as the outcome for that subtask.
FlexKBQA: A Flexible LLM-Powered Framework for Few-Shot Knowledge Base Question Answering
Knowledge base question answering (KBQA) is a critical yet challenging task due to the vast number of entities within knowledge bases and the diversity of natural language questions posed by users.
The RoboDepth Challenge: Methods and Advancements Towards Robust Depth Estimation
In this paper, we summarize the winning solutions from the RoboDepth Challenge -- an academic competition designed to facilitate and advance robust OoD depth estimation.
Secure Split Learning against Property Inference, Data Reconstruction, and Feature Space Hijacking Attacks
Split learning of deep neural networks (SplitNN) has provided a promising solution to learning jointly for the mutual interest of a guest and a host, which may come from different backgrounds, holding features partitioned vertically.
Bridging the Language Gap: Knowledge Injected Multilingual Question Answering
As a popular topic in natural language processing tasks, extractive question answering task (extractive QA) has gained extensive attention in the past few years.
Augment and Criticize: Exploring Informative Samples for Semi-Supervised Monocular 3D Object Detection
In this paper, we improve the challenging monocular 3D object detection problem with a general semi-supervised framework.
Learning from Noisy Data for Semi-Supervised 3D Object Detection
To this end, we propose NoiseDet, a simple yet effective framework for semi-supervised 3D object detection.
Toward a Unified Framework for Unsupervised Complex Tabular Reasoning
To bridge the gap between the programs and natural language sentences, we design a powerful "NL-Generator" module to generate natural language sentences with complex logic from these programs.
BEVDistill: Cross-Modal BEV Distillation for Multi-View 3D Object Detection
Instead of directly training a depth prediction network, we unify the image and LiDAR features in the Bird-Eye-View (BEV) space and adaptively transfer knowledge across non-homogenous representations in a teacher-student paradigm.
Ranked #14 on
3D Object Detection
on nuScenes Camera Only
Not Just Plain Text! Fuel Document-Level Relation Extraction with Explicit Syntax Refinement and Subsentence Modeling
Document-level relation extraction (DocRE) aims to identify semantic labels among entities within a single document.
Efficient Single-Image Depth Estimation on Mobile Devices, Mobile AI & AIM 2022 Challenge: Report
Various depth estimation models are now widely used on many mobile and IoT devices for image segmentation, bokeh effect rendering, object tracking and many other mobile tasks.
LiteDepth: Digging into Fast and Accurate Depth Estimation on Mobile Devices
Notably, our solution named LiteDepth ranks 2nd in the MAI&AIM2022 Monocular Depth Estimation Challenge}, with a si-RMSE of 0. 311, an RMSE of 3. 79, and the inference time is 37$ms$ tested on the Raspberry Pi 4.
AutoAlignV2: Deformable Feature Aggregation for Dynamic Multi-Modal 3D Object Detection
Recently, AutoAlign presents a learnable paradigm in combining these two modalities for 3D object detection.
Effective Few-Shot Named Entity Linking by Meta-Learning
In this paper, we endeavor to solve the problem of few-shot entity linking, which only requires a minimal amount of in-domain labeled data and is more practical in real situations.
Towards Model Generalization for Monocular 3D Object Detection
However, caused by severe domain gaps (e. g., the field of view (FOV), pixel size, and object size among datasets), Mono3D detectors have difficulty in generalization, leading to drastic performance degradation on unseen domains.
Graph-DETR3D: Rethinking Overlapping Regions for Multi-View 3D Object Detection
3D object detection from multiple image views is a fundamental and challenging task for visual scene understanding.
Unsupervised Domain Adaptation for Monocular 3D Object Detection via Self-Training
Based on this, we develop a teacher-student paradigm to generate adaptive pseudo labels on the target domain.
BinsFormer: Revisiting Adaptive Bins for Monocular Depth Estimation
Recently, some methods reformulate it as a classification-regression task to boost the model performance, where continuous depth is estimated via a linear combination of predicted probability distributions and discrete bins.
Ranked #20 on
Monocular Depth Estimation
on KITTI Eigen split
(using extra training data)
DepthFormer: Exploiting Long-Range Correlation and Local Information for Accurate Monocular Depth Estimation
This paper aims to address the problem of supervised monocular depth estimation.
Ranked #20 on
Monocular Depth Estimation
on KITTI Eigen split
(using extra training data)
AutoAlign: Pixel-Instance Feature Aggregation for Multi-Modal 3D Object Detection
This map enables our model to automate the alignment of non-homogenous features in a dynamic and data-driven manner.
ForgeryNet -- Face Forgery Analysis Challenge 2021: Methods and Results
The rapid progress of photorealistic synthesis techniques has reached a critical point where the boundary between real and manipulated images starts to blur.
SimIPU: Simple 2D Image and 3D Point Cloud Unsupervised Pre-Training for Spatial-Aware Visual Representations
To bridge this gap, we aim to learn a spatial-aware visual representation that can describe the three-dimensional space and is more suitable and effective for these tasks.
Fast and Accurate Single-Image Depth Estimation on Mobile Devices, Mobile AI 2021 Challenge: Report
While many solutions have been proposed for this task, they are usually very computationally expensive and thus are not applicable for on-device inference.
A Cloud connected NO2 and Ozone Sensor System for Personalized Pediatric Asthma Research and Management
This paper presents a cloud-connected indoor air quality sensor system that can be deployed to patients' homes to study personal microenvironmental exposure for asthma research and management.
Predictions of 2019-nCoV Transmission Ending via Comprehensive Methods
Since the SARS outbreak in 2003, a lot of predictive epidemiological models have been proposed.
QMR:Q-learning based Multi-objective optimization Routing protocol for Flying Ad Hoc Networks
It is difficult for existing routing protocols for Mobile Ad Hoc Networks (MANETs) and Vehicular Ad Hoc Networks (VANETs) to adapt the high dynamics of FANETs.
Locating the boundaries of Pareto fronts: A Many-Objective Evolutionary Algorithm Based on Corner Solution Search
In this paper, an evolutionary many-objective optimization algorithm based on corner solution search (MaOEA-CS) was proposed.
Estimation of genomic characteristics by analyzing k-mer frequency in de novo genome projects
Therefore, it is necessary to develop efficient assembly-independent methods for accurate estimation of these genomic characteristics.
