Search Results for author:
Found 92 papers, 27 papers with code
Large Batch Optimization for Object Detection: Training COCO in 12 Minutes
Most of existing object detectors usually adopt a small training batch size ( ~16), which severely hinders the whole community from exploring large-scale datasets due to the extremely long training procedure.
Adaptive Variance Based Label Distribution Learning For Facial Age Estimation
Some existing methods adopt distribution learning to tackle this issue by exploiting the semantic correlation between age labels.
Ranked #6 on
Age Estimation
on MORPH album2 (Caucasian)
Blended Grammar Network for Human Parsing
Although human parsing has made great progress, it still faces a challenge, i. e., how to extract the whole foreground from similar or cluttered scenes effectively.
FOCUS: Fine-grained Optimization with Semantic Guided Understanding for Pedestrian Attributes Recognition
Pedestrian attribute recognition (PAR) is a fundamental perception task in intelligent transportation and security.
FedDAA: Dynamic Client Clustering for Concept Drift Adaptation in Federated Learning
A key challenge lies in distinguishing different sources of drift, as they require distinct adaptation strategies: real drift calls for discarding outdated data, while virtual or label drift benefits from retaining historical data.
An Information-Theoretic Analysis for Federated Learning under Concept Drift
Inspired by this, we propose an algorithm that regularizes the empirical risk minimization approach with KL divergence and mutual information, thereby enhancing long-term performance.
Referring Expression Instance Retrieval and A Strong End-to-End Baseline
Text-Image Retrieval (TIR) retrieves a target image from a gallery based on an image-level description, while Referring Expression Comprehension (REC) localizes a target object within a given image using an instance-level description.
SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought
We address this gap by introducing Flow Chain of Thought (Flow CoT), a reasoning paradigm that models recursive inference as a progressive trajectory of latent cognitive states.
Understand, Think, and Answer: Advancing Visual Reasoning with Large Multimodal Models
Large Multimodal Models (LMMs) have recently demonstrated remarkable visual understanding performance on both vision-language and vision-centric tasks.
One-for-All Pruning: A Universal Model for Customized Compression of Large Language Models
Since the gradient of the Gaussian process is computable, we can use it to approximate the gradient of the non-differentiable pruning process, thereby enabling StratNet updates.
FedEMA: Federated Exponential Moving Averaging with Negative Entropy Regularizer in Autonomous Driving
Street Scene Semantic Understanding (denoted as S3U) is a crucial but complex task for autonomous driving (AD) vehicles.
Integrating Single-Cell Foundation Models with Graph Neural Networks for Drug Response Prediction
Our approach builds on the DeepCDR framework, which encodes drug representations from graph structures and cell representations from multi-omics profiles.
MathPhys-Guided Coarse-to-Fine Anomaly Synthesis with SQE-Driven Bi-Level Optimization for Anomaly Detection
Anomaly detection is a crucial task in computer vision, yet collecting real-world defect images is inherently difficult due to the rarity and unpredictability of anomalies.
Vision-R1: Evolving Human-Free Alignment in Large Vision-Language Models via Vision-Guided Reinforcement Learning
Large Vision-Language Models (LVLMs) typically follow a two-stage training paradigm-pretraining and supervised fine-tuning.
PhysVLM: Enabling Visual Language Models to Understand Robotic Physical Reachability
Specifically, the S-P Map abstracts a robot's physical reachability into a generalized spatial representation, independent of specific robot configurations, allowing the model to focus on reachability features rather than robot-specific parameters.
MIGA: Mutual Information-Guided Attack on Denoising Models for Semantic Manipulation
In this paper, we propose Mutual Information-Guided Attack (MIGA), the first method designed to directly attack deep denoising models by strategically disrupting their ability to preserve semantic content via adversarial perturbations.
FLARE: A Framework for Stellar Flare Forecasting using Stellar Physical Properties and Historical Records
Stellar flare events are critical observational samples for astronomical research; however, recorded flare events remain limited.
Zero Token-Driven Deep Thinking in LLMs: Unlocking the Full Potential of Existing Parameters via Cyclic Refinement
Resource limitations often constrain the parameter counts of Large Language Models (LLMs), hindering their performance.
Systematic Outliers in Large Language Models
Outliers have been widely observed in Large Language Models (LLMs), significantly impacting model performance and posing challenges for model compression.
Label Anything: An Interpretable, High-Fidelity and Prompt-Free Annotator
To mitigate this cost of manual labeling, we propose a Label Anything Model (denoted as LAM), serving as an interpretable, high-fidelity, and prompt-free data annotator.
FiLo++: Zero-/Few-Shot Anomaly Detection by Fused Fine-Grained Descriptions and Deformable Localization
However, their handcrafted generic descriptions fail to capture the diverse range of anomalies that may emerge in different objects, and simple patch-level image-text matching often struggles to localize anomalous regions of varying shapes and sizes.
Enhancing Large Vision Model in Street Scene Semantic Understanding through Leveraging Posterior Optimization Trajectory
Specifically, as the fitted data volume grows to exceed the the AD model's fitting capacities, the AD model is prone to under-fitting.
Cracking the Code of Hallucination in LVLMs with Vision-aware Head Divergence
Large vision-language models (LVLMs) have made substantial progress in integrating large language models (LLMs) with visual inputs, enabling advanced multimodal reasoning.
UniVAD: A Training-free Unified Model for Few-shot Visual Anomaly Detection
Visual Anomaly Detection (VAD) aims to identify abnormal samples in images that deviate from normal patterns, covering multiple domains, including industrial, logical, and medical fields.
Friend or Foe? Harnessing Controllable Overfitting for Anomaly Detection
Overfitting has long been stigmatized as detrimental to model performance, especially in the context of anomaly detection.
SEEKR: Selective Attention-Guided Knowledge Retention for Continual Learning of Large Language Models
In this work, we first explore and emphasize the importance of attention weights in knowledge retention, and then propose a SElective attEntion-guided Knowledge Retention method (SEEKR) for data-efficient replay-based continual learning of large language models (LLMs).
Griffon-G: Bridging Vision-Language and Vision-Centric Tasks via Large Multimodal Models
None of the LMMs have yet comprehensively unified both types of tasks within a single model, as seen in Large Language Models in the natural language processing field.
Fast-Convergent and Communication-Alleviated Heterogeneous Hierarchical Federated Learning in Autonomous Driving
Unfortunately, it suffers from slow convergence because data from different cities are with disparate statistical properties.
Asynchronous Fractional Multi-Agent Deep Reinforcement Learning for Age-Minimal Mobile Edge Computing
In the realm of emerging real-time networked applications like cyber-physical systems (CPS), the Age of Information (AoI) has merged as a pivotal metric for evaluating the timeliness.
The BRAVO Semantic Segmentation Challenge Results in UNCV2024
We propose the unified BRAVO challenge to benchmark the reliability of semantic segmentation models under realistic perturbations and unknown out-of-distribution (OOD) scenarios.
MROVSeg: Breaking the Resolution Curse of Vision-Language Models in Open-Vocabulary Image Segmentation
Pretrained vision-language models (VLMs), \eg CLIP, are increasingly used to bridge the gap between open- and close-vocabulary recognition in open-vocabulary image segmentation.
Optical Semantic Communication through Multimode Fiber: From Symbol Transmission to Sentiment Analysis
By encoding semantically similar symbols to adjacent frequencies, the system's noise tolerance is effectively improved, facilitating accurate sentiment analysis.
Real-time Event Recognition of Long-distance Distributed Vibration Sensing with Knowledge Distillation and Hardware Acceleration
The proposed method greatly enhances the efficiency of vibration pattern recognition, promoting the use of DVS as a smart IoT system.
Fermat Number Transform Based Chromatic Dispersion Compensation and Adaptive Equalization Algorithm
By introducing the Fermat number transform into chromatic dispersion compensation and adaptive equalization, the computational complexity has been reduced by 68% compared with the con? ventional implementation.
FiLo: Zero-Shot Anomaly Detection by Fine-Grained Description and High-Quality Localization
Zero-shot anomaly detection (ZSAD) methods entail detecting anomalies directly without access to any known normal or abnormal samples within the target item categories.
Ranked #4 on
zero-shot anomaly detection
on MVTec AD
Optimization of Prompt Learning via Multi-Knowledge Representation for Vision-Language Models
This limitation restricts the capabilities of pretrained VLMs and can result in incorrect predictions in downstream tasks.
PraFFL: A Preference-Aware Scheme in Fair Federated Learning
We theoretically prove that PraFFL can offer the optimal model tailored to an arbitrary preference of each client, and show its linear convergence.
Efficient and Generalizable Certified Unlearning: A Hessian-free Recollection Approach
Machine unlearning strives to uphold the data owners' right to be forgotten by enabling models to selectively forget specific data.
Griffon v2: Advancing Multimodal Perception with High-Resolution Scaling and Visual-Language Co-Referring
Large Vision Language Models have achieved fine-grained object perception, but the limitation of image resolution remains a significant obstacle to surpass the performance of task-specific experts in complex and dense scenarios.
BFRFormer: Transformer-based generator for Real-World Blind Face Restoration
Blind face restoration is a challenging task due to the unknown and complex degradation.
Convergence Analysis of Split Federated Learning on Heterogeneous Data
We further extend the analysis to non-convex objectives and the scenario where some clients may be unavailable during training.
Self-Supervised Representation Learning from Arbitrary Scenarios
To address this conflict this work abandons the non-generalizable global-level constraints and proposes explicit patch-level contrastive learning as a solution.
Fluctuation-based Adaptive Structured Pruning for Large Language Models
Retraining-free is important for LLMs' pruning methods.
Fractional Deep Reinforcement Learning for Age-Minimal Mobile Edge Computing
The uncertain edge load dynamics, the nature of the fractional objective, and hybrid continuous-discrete action space (due to the joint optimization) make this problem challenging and existing approaches not directly applicable.
Mitigating Hallucination in Visual Language Models with Visual Supervision
Large vision-language models (LVLMs) suffer from hallucination a lot, generating responses that apparently contradict to the image content occasionally.
Continual Instruction Tuning for Large Multimodal Models
2) Are the existing three classes of continual learning methods still applicable to the continual instruction tuning of LMMs?
Griffon: Spelling out All Object Locations at Any Granularity with Large Language Models
Current LVLMs are predominantly constrained to locate a single, pre-existing object.
Price of Stability in Quality-Aware Federated Learning
The presence of label noise can severely degrade the FL performance, and some existing studies have focused on algorithm design for label denoising.
AnomalyGPT: Detecting Industrial Anomalies Using Large Vision-Language Models
Large Vision-Language Models (LVLMs) such as MiniGPT-4 and LLaVA have demonstrated the capability of understanding images and achieved remarkable performance in various visual tasks.
FrFT based estimation of linear and nonlinear impairments using Vision Transformer
To comprehensively assess optical fiber communication system conditions, it is essential to implement joint estimation of the following four critical impairments: nonlinear signal-to-noise ratio (SNRNL), optical signal-to-noise ratio (OSNR), chromatic dispersion (CD) and differential group delay (DGD).
When MiniBatch SGD Meets SplitFed Learning:Convergence Analysis and Performance Evaluation
SplitFed learning (SFL) is a recent distributed approach that alleviates computation workload at the client device by splitting the model at a cut layer into two parts, where clients only need to train part of the model.
Real-time FPGA Implementation of CNN-based Distributed Fiber Optic Vibration Event Recognition Method
Utilizing optical fibers to detect and pinpoint vibrations, Distributed Optical Fiber Vibration Sensing (DVS) technology provides real-time monitoring and surveillance of wide-reaching areas.
Multi-objective Deep Reinforcement Learning for Mobile Edge Computing
In this study, we address this issue by formulating a multi-objective offloading problem for MEC with multiple edges to minimize expected long-term energy consumption and transmission delay while considering unknown preferences as parameters.
Fast Segment Anything
In this paper, we propose a speed-up alternative method for this fundamental task with comparable performance.
Ranked #4 on
Zero-Shot Instance Segmentation
on LVIS v1.0 val
FreConv: Frequency Branch-and-Integration Convolutional Networks
In the high-frequency branch, a derivative-filter-like architecture is designed to extract the high-frequency information while a light extractor is employed in the low-frequency branch because the low-frequency information is usually redundant.
ZBS: Zero-shot Background Subtraction via Instance-level Background Modeling and Foreground Selection
However, previous unsupervised deep learning BGS algorithms perform poorly in sophisticated scenarios such as shadows or night lights, and they cannot detect objects outside the pre-defined categories.
Efficient Masked Autoencoders with Self-Consistency
Besides, we design the self-consistency learning to further maintain the consistency of predictions of overlapping masked patches among parts.
Deep Learning for Human Parsing: A Survey
Human parsing is a key topic in image processing with many applications, such as surveillance analysis, human-robot interaction, person search, and clothing category classification, among many others.
Design of the PID temperature controller for an alkaline electrolysis system with time delays
This paper focuses on the design of the PID temperature controller for an alkaline electrolysis system to achieve fast and stable temperature control.
Obj2Seq: Formatting Objects as Sequences with Class Prompt for Visual Tasks
Obj2Seq is able to flexibly determine input categories to satisfy customized requirements, and be easily extended to different visual tasks.
Transfering Low-Frequency Features for Domain Adaptation
Previous unsupervised domain adaptation methods did not handle the cross-domain problem from the perspective of frequency for computer vision.
Plug-and-Play Pseudo Label Correction Network for Unsupervised Person Re-identification
Clustering-based methods, which alternate between the generation of pseudo labels and the optimization of the feature extraction network, play a dominant role in both unsupervised learning (USL) and unsupervised domain adaptive (UDA) person re-identification (Re-ID).
Beyond the Limitation of Pulse Width in Optical Time-domain Reflectometry
Here, we propose and experimentally demonstrate an OTDR deconvolution neural network based on deep convolutional neural networks.
UniVIP: A Unified Framework for Self-Supervised Visual Pre-training
Furthermore, our method can also exploit single-centric-object dataset such as ImageNet and outperforms BYOL by 2. 5% with the same pre-training epochs in linear probing, and surpass current self-supervised object detection methods on COCO dataset, demonstrating its universality and potential.
PASS: Part-Aware Self-Supervised Pre-Training for Person Re-Identification
PASS learns to match the output of the local views and global views on the same [PART].
Thermal Modelling and Controller Design of an Alkaline Electrolysis System under Dynamic Operating Conditions
A control-oriented thermal model is established in the form of a third-order time-delay process, which is used for simulation and controller design.
Pruning-aware Sparse Regularization for Network Pruning
MaskSparsity imposes the fine-grained sparse regularization on the specific filters selected by a pruning mask, rather than all the filters of the model.
C2AM Loss: Chasing a Better Decision Boundary for Long-Tail Object Detection
The decision boundary between any two categories is the angular bisector of their weight vectors.
Multi-initialization Optimization Network for Accurate 3D Human Pose and Shape Estimation
In the second stage, we design a mesh refinement transformer (MRT) to respectively refine each coarse reconstruction result via a self-attention mechanism.
Ranked #75 on
3D Human Pose Estimation
on 3DPW
(MPJPE metric)
Enabling variable high spatial resolution retrieval from a long pulse BOTDA sensor
We propose a convolutional neural network (CNN) to process the data of conventional Brillouin optical time domain analysis (BOTDA) sensors, which achieves unprecedented performance improvement that allows to directly retrieve higher spatial resolution (SR) from the sensing system that use long pump pulses.
DPT: Deformable Patch-based Transformer for Visual Recognition
To address this problem, we propose a new Deformable Patch (DePatch) module which learns to adaptively split the images into patches with different positions and scales in a data-driven way rather than using predefined fixed patches.
Ranked #17 on
Semantic Segmentation
on DensePASS
Improving Multiple Object Tracking With Single Object Tracking
Despite considerable similarities between multiple object tracking (MOT) and single object tracking (SOT) tasks, modern MOT methods have not benefited from the development of SOT ones to achieve satisfactory performance.
MST: Masked Self-Supervised Transformer for Visual Representation
More importantly, the masked tokens together with the remaining tokens are further recovered by a global image decoder, which preserves the spatial information of the image and is more friendly to the downstream dense prediction tasks.
AAformer: Auto-Aligned Transformer for Person Re-Identification
In this article, we introduce an alignment scheme in transformer architecture for the first time and propose the auto-aligned transformer (AAformer) to automatically locate both the human parts and nonhuman ones at patch level.
Adaptive Class Suppression Loss for Long-Tail Object Detection
To address the problem of long-tail distribution for the large vocabulary object detection task, existing methods usually divide the whole categories into several groups and treat each group with different strategies.
Identify Influential Spreaders in Asymmetrically Interacting Multiplex Networks
Our work provides insights on the importance of nodes in the multiplex network and gives a feasible framework to investigate influential spreaders in the asymmetrically coevolving dynamics.
Physics and Society
High-Performance Discriminative Tracking With Transformers
End-to-end discriminative trackers improve the state of the art significantly, yet the improvement in robustness and efficiency is restricted by the conventional discriminative model, i. e., least-squares based regression.
Task Decoupled Knowledge Distillation For Lightweight Face Detectors
This method decouples the distillation task of face detection into two subtasks, i. e., the classification distillation subtask and the regression distillation subtask.
Improving the spatial resolution of a BOTDA sensor using deconvolution algorithm
The frequency dependency of Brillouin gain temporal envelope is investigated by simulation, and its impact on the recovered results of deconvolution algorithm is thoroughly analyzed.
Identity-Guided Human Semantic Parsing for Person Re-Identification
In this paper, we propose the identity-guided human semantic parsing approach (ISP) to locate both the human body parts and personal belongings at pixel-level for aligned person re-ID only with person identity labels.
Ranked #21 on
Person Re-Identification
on Occluded-DukeMTMC
A Quantitative Analytical Model for Predicting and Optimizing the Rate Performance of Battery Cells
An important objective of designing lithium-ion rechargeable battery cells is to maximize their rate performance without compromising the energy density, which is mainly achieved through computationally expensive numerical simulations at present.
Materials Science Applied Physics
Repositioning Bikes with Carrier Vehicles and Bike Trailers in Bike Sharing Systems
Bike Sharing Systems (BSSs) have been adopted in many major cities of the world due to traffic congestion and carbon emissions.
Learning Feature Embeddings for Discriminant Model based Tracking
After observing that the features used in most online discriminatively trained trackers are not optimal, in this paper, we propose a novel and effective architecture to learn optimal feature embeddings for online discriminative tracking.
Semantic Alignment: Finding Semantically Consistent Ground-truth for Facial Landmark Detection
Despite this, we notice that the semantic ambiguity greatly degrades the detection performance.
Ranked #1 on
Face Alignment
on 300W
(NME_inter-pupil (%, Full) metric)
High-speed Tracking with Multi-kernel Correlation Filters
In this paper, we will introduce the MKL into KCF in a different way than MKCF.
Ranked #34 on
Video Object Tracking
on NT-VOT211
Fast Kernelized Correlation Filters without Boundary Effect
To achieve the fast training and detection, a set of cyclic bases is introduced to construct the filter.
PCN: Part and Context Information for Pedestrian Detection with CNNs
To take advantage of the body parts and context information for pedestrian detection, we propose the part and context network (PCN) in this work.
On the Relations of Correlation Filter Based Trackers and Struck
In recent years, two types of trackers, namely correlation filter based tracker (CF tracker) and structured output tracker (Struck), have exhibited the state-of-the-art performance.
Fast Deep Matting for Portrait Animation on Mobile Phone
Finally, an automatic portrait animation system based on fast deep matting is built on mobile devices, which does not need any interaction and can realize real-time matting with 15 fps.
Joint Background Reconstruction and Foreground Segmentation via A Two-stage Convolutional Neural Network
In the first stage, a convolutional encoder-decoder sub-network is employed to reconstruct the background images and encode rich prior knowledge of background scenes.
Multi-Kernel Correlation Filter for Visual Tracking
In this paper, we will derive a multi-kernel correlation filter (MKCF) based tracker which fully takes advantage of the invariance-discriminative power spectrums of various features to further improve the performance.
