Search Results for author:
Found 33 papers, 14 papers with code
Generalizable Face Landmarking Guided by Conditional Face Warping
Given a pair of real and stylized facial images, the conditional face warper predicts a warping field from the real face to the stylized one, in which the face landmarker predicts the ending points of the warping field and provides us with high-quality pseudo landmarks for the corresponding stylized facial images.
Deep Cooperation in ISAC System: Resource, Node and Infrastructure Perspectives
With the mobile communication system evolving into 6th-generation (6G), the Internet of Everything (IoE) is becoming reality, which connects human, big data and intelligent machines to support the intelligent decision making, reconfiguring the traditional industries and human life.
PCDepth: Pattern-based Complementary Learning for Monocular Depth Estimation by Best of Both Worlds
Therefore, existing complementary learning approaches for MDE fuse intensity information from images and scene details from event data for better scene understanding.
Edit One for All: Interactive Batch Image Editing
With increased human control, it is now possible to edit an image in a plethora of ways; from specifying in text what we want to change, to straight up dragging the contents of the image in an interactive point-based manner.
Making Large Multimodal Models Understand Arbitrary Visual Prompts
Furthermore, we present ViP-Bench, a comprehensive benchmark to assess the capability of models in understanding visual prompts across multiple dimensions, enabling future research in this domain.
LLaVA-Plus: Learning to Use Tools for Creating Multimodal Agents
LLaVA-Plus is a general-purpose multimodal assistant that expands the capabilities of large multimodal models.
Ranked #1 on
LMM real-life tasks
on Leaderboard
Improved Baselines with Visual Instruction Tuning
Large multimodal models (LMM) have recently shown encouraging progress with visual instruction tuning.
Ranked #3 on
visual instruction following
on LLaVA-Bench
Factual Inconsistency Detection in Chart Captioning
visual instruction following
+1
Integrated Sensing and Communication Signal Processing Based on Compressed Sensing Over Unlicensed Spectrum Bands
The ISAC-enabled mobile communication system regularly operate in non-continuous spectrum bands due to crowded licensed frequency bands.
Carrier Aggregation Enabled Integrated Sensing and Communication Signal Design and Processing
The future mobile communication systems will support intelligent applications such as Internet of Vehicles (IoV) and Extended Reality (XR).
Aligning Large Multimodal Models with Factually Augmented RLHF
Large Multimodal Models (LMM) are built across modalities and the misalignment between two modalities can result in "hallucination", generating textual outputs that are not grounded by the multimodal information in context.
An Empirical Study of Scaling Instruct-Tuned Large Multimodal Models
We find that scaling LMM consistently enhances model performance and improves language capabilities, and performance of LoRA/QLoRA tuning of LMM are comparable to the performance of full-model fine-tuning.
Ranked #47 on
Visual Question Answering
on MM-Vet
Benchmarking and Analyzing Generative Data for Visual Recognition
Advancements in large pre-trained generative models have expanded their potential as effective data generators in visual recognition.
Generate Anything Anywhere in Any Scene
Text-to-image diffusion models have attracted considerable interest due to their wide applicability across diverse fields.
LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day
In this paper, we propose a cost-efficient approach for training a vision-language conversational assistant that can answer open-ended research questions of biomedical images.
Visual Instruction Tuning
Instruction tuning large language models (LLMs) using machine-generated instruction-following data has improved zero-shot capabilities on new tasks, but the idea is less explored in the multimodal field.
Ranked #4 on
Visual Question Answering
on BenchLMM
Data-Efficient Image Quality Assessment with Attention-Panel Decoder
Blind Image Quality Assessment (BIQA) is a fundamental task in computer vision, which however remains unresolved due to the complex distortion conditions and diversified image contents.
TMA: Temporal Motion Aggregation for Event-based Optical Flow
In this paper, we argue that temporal continuity is a vital element of event-based optical flow and propose a novel Temporal Motion Aggregation (TMA) approach to unlock its potential.
GLIGEN: Open-Set Grounded Text-to-Image Generation
Large-scale text-to-image diffusion models have made amazing advances.
Ranked #4 on
Conditional Text-to-Image Synthesis
on COCO-MIG
Learning Customized Visual Models with Retrieval-Augmented Knowledge
Image-text contrastive learning models such as CLIP have demonstrated strong task transfer ability.
Ranked #1 on
Semi-Supervised Image Classification
on ImageNet - 1% labeled data
(using extra training data)
Reducing Action Space: Reference-Model-Assisted Deep Reinforcement Learning for Inverter-based Volt-Var Control
We investigate that a large action space increases the learning difficulties of DRL and degrades the optimization performance in the process of generating data and training neural networks.
ELEVATER: A Benchmark and Toolkit for Evaluating Language-Augmented Visual Models
In general, these language-augmented visual models demonstrate strong transferability to a variety of datasets and tasks.
Ranked #1 on
Object Detection
on ELEVATER
End-to-End Instance Edge Detection
We demonstrate highly competitive instance edge detection performance compared to state-of-the-art baselines, and also show that the proposed task and loss are complementary to instance segmentation and object detection.
Reducing Learning Difficulties: One-Step Two-Critic Deep Reinforcement Learning for Inverter-based Volt-Var Control
Then we design the one-step actor-critic DRL scheme which is a simplified version of recent DRL algorithms, and it avoids the issue of Q value overestimation successfully.
Masked Discrimination for Self-Supervised Learning on Point Clouds
Masked autoencoding has achieved great success for self-supervised learning in the image and language domains.
Ranked #12 on
Few-Shot 3D Point Cloud Classification
on ModelNet40 5-way (10-shot)
(using extra training data)
M2MRF: Many-to-Many Reassembly of Features for Tiny Lesion Segmentation in Fundus Images
It reassembles features in a dimension-reduced feature space and simultaneously aggregates multiple features inside a large predefined region into multiple target features.
Bi-level Off-policy Reinforcement Learning for Volt/VAR Control Involving Continuous and Discrete Devices
Such VCC is formulated as a two-timescale optimization problem to jointly optimize FTCDs and STDDs in ADNs.
YolactEdge: Real-time Instance Segmentation on the Edge
We propose YolactEdge, the first competitive instance segmentation approach that runs on small edge devices at real-time speeds.
Dual-Branch Network with Dual-Sampling Modulated Dice Loss for Hard Exudate Segmentation from Colour Fundus Images
In detail, for the first branch, we use a uniform sampler to sample pixels from predicted segmentation mask for Dice loss calculation, which leads to this branch naturally be biased in favour of large hard exudates as Dice loss generates larger cost on misidentification of large hard exudates than small hard exudates.
Online Multi-agent Reinforcement Learning for Decentralized Inverter-based Volt-VAR Control
In this paper, we propose an online multi-agent reinforcement learning and decentralized control framework (OLDC) for VVC.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Universal time delay in static spherically symmetric spacetimes for null and timelike signals
A perturbative method to compute the total travel time of both null and lightlike rays in arbitrary static spherically symmetric spacetimes in the weak field limit is proposed.
General Relativity and Quantum Cosmology
Two-stage Deep Reinforcement Learning for Inverter-based Volt-VAR Control in Active Distribution Networks
In the sequential online stage, we transfer the offline agent safely as the online agent to perform continuous learning and controlling online with significantly improved safety and efficiency.
Using Deep Learning and Machine Learning to Detect Epileptic Seizure with Electroencephalography (EEG) Data
The prediction of epileptic seizure has always been extremely challenging in medical domain.
A Constructive Algorithm for Decomposing a Tensor into a Finite Sum of Orthonormal Rank-1 Terms
We propose a constructive algorithm that decomposes an arbitrary real tensor into a finite sum of orthonormal rank-1 outer products.
Numerical Analysis Numerical Analysis
