Search Results for author:
Found 171 papers, 93 papers with code
Local Correlation Consistency for Knowledge Distillation
Sufficient knowledge extraction from the teacher network plays a critical role in the knowledge distillation task to improve the performance of the student network.
Explainable and Interpretable Multimodal Large Language Models: A Comprehensive Survey
The rapid development of Artificial Intelligence (AI) has revolutionized numerous fields, with large language models (LLMs) and computer vision (CV) systems driving advancements in natural language understanding and visual processing, respectively.
Quantum Hamiltonian Descent for Graph Partition
We introduce Quantum Hamiltonian Descent as a novel approach to solve the graph partition problem.
From Transparent to Opaque: Rethinking Neural Implicit Surfaces with $α$-NeuS
Our method leverages the observation that transparent surfaces induce local extreme values in the learned distance fields during neural volumetric rendering, contrasting with opaque surfaces that align with zero level sets.
KptLLM: Unveiling the Power of Large Language Model for Keypoint Comprehension
To bridge this gap, we introduce the novel challenge of Semantic Keypoint Comprehension, which aims to comprehend keypoints across different task scenarios, including keypoint semantic understanding, visual prompt-based keypoint detection, and textual prompt-based keypoint detection.
GraphTeam: Facilitating Large Language Model-based Graph Analysis via Multi-Agent Collaboration
Graphs are widely used for modeling relational data in real-world scenarios, such as social networks and urban computing.
Quasi-Medial Distance Field (Q-MDF): A Robust Method for Approximating and Discretizing Neural Medial Axis
The medial axis, a lower-dimensional shape descriptor, plays an important role in the field of digital geometry processing.
DEAN: Deactivating the Coupled Neurons to Mitigate Fairness-Privacy Conflicts in Large Language Models
Extensive experimental results demonstrate that DEAN eliminates the trade-off phenomenon and significantly improves LLMs' fairness and privacy awareness simultaneously, \eg improving Qwen-2-7B-Instruct's fairness awareness by 12. 2\% and privacy awareness by 14. 0\%.
REEF: Representation Encoding Fingerprints for Large Language Models
Therefore, model owners and third parties need to identify whether a suspect model is a subsequent development of the victim model.
Optima: Optimizing Effectiveness and Efficiency for LLM-Based Multi-Agent System
Large Language Model (LLM) based multi-agent systems (MAS) show remarkable potential in collaborative problem-solving, yet they still face critical challenges: low communication efficiency, poor scalability, and a lack of effective parameter-updating optimization methods.
Improving Data Efficiency via Curating LLM-Driven Rating Systems
Instruction tuning is critical for adapting large language models (LLMs) to downstream tasks, and recent studies have demonstrated that small amounts of human-curated data can outperform larger datasets, challenging traditional data scaling laws.
Can Large Language Models Analyze Graphs like Professionals? A Benchmark, Datasets and Models
Our results underscore that the capabilities of LLMs in handling structured data are still under-explored, and show the effectiveness of LLM4Graph in enhancing LLMs' proficiency of graph analysis.
CAS-ViT: Convolutional Additive Self-attention Vision Transformers for Efficient Mobile Applications
In this paper, we introduce CAS-ViT: Convolutional Additive Self-attention Vision Transformers, to achieve a balance between efficiency and performance in mobile applications.
Ranked #541 on
Image Classification
on ImageNet
MAO: A Framework for Process Model Generation with Multi-Agent Orchestration
Our framework MAO leverages large language models as the cornerstone for multi-agent, employing an innovative prompt strategy to ensure efficient collaboration among multi-agent.
The Better Angels of Machine Personality: How Personality Relates to LLM Safety
In this paper, we discover that LLMs' personality traits are closely related to their safety abilities, i. e., toxicity, privacy, and fairness, based on the reliable MBTI-M scale.
TCFormer: Visual Recognition via Token Clustering Transformer
Our dynamic tokens possess two crucial characteristics: (1) Representing image regions with similar semantic meanings using the same vision token, even if those regions are not adjacent, and (2) concentrating on regions with valuable details and represent them using fine tokens.
When Pedestrian Detection Meets Multi-Modal Learning: Generalist Model and Benchmark Dataset
With multi-modal joint training, our model achieves state-of-the-art performance on a wide range of pedestrian detection benchmarks, surpassing leading models tailored for specific sensor modality.
Ranked #1 on
Object Detection
on STCrowd
Internet of Agents: Weaving a Web of Heterogeneous Agents for Collaborative Intelligence
The rapid advancement of large language models (LLMs) has paved the way for the development of highly capable autonomous agents.
MIRReS: Multi-bounce Inverse Rendering using Reservoir Sampling
We present MIRReS, a novel two-stage inverse rendering framework that jointly reconstructs and optimizes the explicit geometry, material, and lighting from multi-view images.
Autonomous Agents for Collaborative Task under Information Asymmetry
Together with InfoNav, iAgents organizes human information in a mixed memory to provide agents with accurate and comprehensive information for exchange.
Self-Supervised Vision Transformer for Enhanced Virtual Clothes Try-On
Virtual clothes try-on has emerged as a vital feature in online shopping, offering consumers a critical tool to visualize how clothing fits.
Multi-Agent Software Development through Cross-Team Collaboration
We anticipate that our work will guide LLM agents towards a cross-team paradigm and contribute to their significant growth in but not limited to software development.
Scaling Large-Language-Model-based Multi-Agent Collaboration
Pioneering advancements in large language model-powered agents have underscored the design pattern of multi-agent collaboration, demonstrating that collective intelligence can surpass the capabilities of each individual.
KerasCV and KerasNLP: Vision and Language Power-Ups
We present the Keras domain packages KerasCV and KerasNLP, extensions of the Keras API for Computer Vision and Natural Language Processing workflows, capable of running on either JAX, TensorFlow, or PyTorch.
Iterative Experience Refinement of Software-Developing Agents
We propose two fundamental patterns: the successive pattern, refining based on nearest experiences within a task batch, and the cumulative pattern, acquiring experiences across all previous task batches.
UniFS: Universal Few-shot Instance Perception with Point Representations
In this paper, we propose UniFS, a universal few-shot instance perception model that unifies a wide range of instance perception tasks by reformulating them into a dynamic point representation learning framework.
Ranked #1 on
Few-Shot Object Detection
on MS-COCO (5-shot)
LocalMamba: Visual State Space Model with Windowed Selective Scan
This paper posits that the key to enhancing Vision Mamba (ViM) lies in optimizing scan directions for sequence modeling.
DevBench: A Comprehensive Benchmark for Software Development
Recent advancements in large language models (LLMs) have significantly enhanced their coding capabilities.
DEMOS: Dynamic Environment Motion Synthesis in 3D Scenes via Local Spherical-BEV Perception
To handle this problem, we propose the first Dynamic Environment MOtion Synthesis framework (DEMOS) to predict future motion instantly according to the current scene, and use it to dynamically update the latent motion for final motion synthesis.
Towards Tracing Trustworthiness Dynamics: Revisiting Pre-training Period of Large Language Models
This research provides an initial exploration of trustworthiness modeling during LLM pre-training, seeking to unveil new insights and spur further developments in the field.
Beyond Natural Language: LLMs Leveraging Alternative Formats for Enhanced Reasoning and Communication
Natural language (NL) has long been the predominant format for human cognition and communication, and by extension, has been similarly pivotal in the development and application of Large Language Models (LLMs).
AutoMMLab: Automatically Generating Deployable Models from Language Instructions for Computer Vision Tasks
Automated machine learning (AutoML) is a collection of techniques designed to automate the machine learning development process.
A Study on the Vulnerability of Test Questions against ChatGPT-based Cheating
In this paper, we try to provide an answer to an important question: how well ChatGPT can answer test questions and how we can detect whether the questions of a test can be answered correctly by ChatGPT.
Fairness Without Harm: An Influence-Guided Active Sampling Approach
In this work, we aim to train models that mitigate group fairness disparity without causing harm to model accuracy.
MetaTra: Meta-Learning for Generalized Trajectory Prediction in Unseen Domain
Trajectory prediction has garnered widespread attention in different fields, such as autonomous driving and robotic navigation.
DiffSpeaker: Speech-Driven 3D Facial Animation with Diffusion Transformer
We suspect this is due to a shortage of paired audio-4D data, which is crucial for the Transformer to effectively perform as a denoiser within the Diffusion framework.
Lens: A Foundation Model for Network Traffic
Network traffic refers to the amount of data being sent and received over the internet or any system that connects computers.
Topology-Aware Latent Diffusion for 3D Shape Generation
By strategically incorporating topological features into the diffusion process, our generative module is able to produce a richer variety of 3D shapes with different topological structures.
From GPT-4 to Gemini and Beyond: Assessing the Landscape of MLLMs on Generalizability, Trustworthiness and Causality through Four Modalities
Multi-modal Large Language Models (MLLMs) have shown impressive abilities in generating reasonable responses with respect to multi-modal contents.
Experiential Co-Learning of Software-Developing Agents
Recent advancements in large language models (LLMs) have brought significant changes to various domains, especially through LLM-driven autonomous agents.
Not All Steps are Equal: Efficient Generation with Progressive Diffusion Models
In the initial stage, a base denoising model is trained to encompass all timesteps.
Robust Geometry and Reflectance Disentanglement for 3D Face Reconstruction from Sparse-view Images
This paper presents a novel two-stage approach for reconstructing human faces from sparse-view images, a task made challenging by the unique geometry and complex skin reflectance of each individual.
You Only Learn One Query: Learning Unified Human Query for Single-Stage Multi-Person Multi-Task Human-Centric Perception
Human-centric perception (e. g. detection, segmentation, pose estimation, and attribute analysis) is a long-standing problem for computer vision.
Identity-Obscured Neural Radiance Fields: Privacy-Preserving 3D Facial Reconstruction
Neural radiance fields (NeRF) typically require a complete set of images taken from multiple camera perspectives to accurately reconstruct geometric details.
Unlock the Power: Competitive Distillation for Multi-Modal Large Language Models
The first stage pre-trains the student model on a large number of filtered multi-modal datasets.
Minimum Snap Trajectory Generation and Control for an Under-actuated Flapping Wing Aerial Vehicle
This work firstly achieves the closed-loop integration of trajectory generation and control for real 3-dimensional flight of an underactuated FWAV to a practical level.
Virtual Accessory Try-On via Keypoint Hallucination
Specifically, our approach learns the human body priors and hallucinates the target locations of specified foreground keypoints in the background.
Secure Decentralized Learning with Blockchain
To avoid the single point of failure problem in FL, decentralized federated learning (DFL) has been proposed to use peer-to-peer communication for model aggregation, which has been considered an attractive solution for machine learning tasks on distributed personal devices.
Parameterization-driven Neural Surface Reconstruction for Object-oriented Editing in Neural Rendering
The advancements in neural rendering have increased the need for techniques that enable intuitive editing of 3D objects represented as neural implicit surfaces.
DiffNAS: Bootstrapping Diffusion Models by Prompting for Better Architectures
Diffusion models have recently exhibited remarkable performance on synthetic data.
Bloch Equation Enables Physics-informed Neural Network in Parametric Magnetic Resonance Imaging
PINN enables learning the Bloch equation, estimating the T2 parameter, and generating a series of physically synthetic data.
GKGNet: Group K-Nearest Neighbor based Graph Convolutional Network for Multi-Label Image Recognition
Multi-Label Image Recognition (MLIR) is a challenging task that aims to predict multiple object labels in a single image while modeling the complex relationships between labels and image regions.
Ranked #3 on
Multi-Label Classification
on PASCAL VOC 2007
CoNe: Contrast Your Neighbours for Supervised Image Classification
Moreover, to further boost the performance, we propose ``distributional consistency" as a more informative regularization to enable similar instances to have a similar probability distribution.
AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors
Autonomous agents empowered by Large Language Models (LLMs) have undergone significant improvements, enabling them to generalize across a broad spectrum of tasks.
SimMatchV2: Semi-Supervised Learning with Graph Consistency
Semi-Supervised image classification is one of the most fundamental problem in computer vision, which significantly reduces the need for human labor.
Taming the Power of Diffusion Models for High-Quality Virtual Try-On with Appearance Flow
Our approach, namely Diffusion-based Conditional Inpainting for Virtual Try-ON (DCI-VTON), effectively utilizes the power of the diffusion model, and the incorporation of the warping module helps to produce high-quality and realistic virtual try-on results.
DNA-Rendering: A Diverse Neural Actor Repository for High-Fidelity Human-centric Rendering
Realistic human-centric rendering plays a key role in both computer vision and computer graphics.
ChatDev: Communicative Agents for Software Development
Numerous studies used deep learning to improve specific phases in a waterfall model, such as design, coding, and testing.
Can Large Language Models Empower Molecular Property Prediction?
Molecular property prediction has gained significant attention due to its transformative potential in multiple scientific disciplines.
Knowledge Diffusion for Distillation
To address this, we propose to denoise student features using a diffusion model trained by teacher features.
RenderMe-360: A Large Digital Asset Library and Benchmarks Towards High-fidelity Head Avatars
It is a large-scale digital library for head avatars with three key attributes: 1) High Fidelity: all subjects are captured by 60 synchronized, high-resolution 2K cameras in 360 degrees.
Can GPT-4 Perform Neural Architecture Search?
We investigate the potential of GPT-4~\cite{gpt4} to perform Neural Architecture Search (NAS) -- the task of designing effective neural architectures.
SynBody: Synthetic Dataset with Layered Human Models for 3D Human Perception and Modeling
Synthetic data has emerged as a promising source for 3D human research as it offers low-cost access to large-scale human datasets.
Deformable Model-Driven Neural Rendering for High-Fidelity 3D Reconstruction of Human Heads Under Low-View Settings
To address this, we propose geometry decomposition and adopt a two-stage, coarse-to-fine training strategy, allowing for progressively capturing high-frequency geometric details.
OmniObject3D: Large-Vocabulary 3D Object Dataset for Realistic Perception, Reconstruction and Generation
Recent advances in modeling 3D objects mostly rely on synthetic datasets due to the lack of large-scale realscanned 3D databases.
A Dynamics Theory of Implicit Regularization in Deep Low-Rank Matrix Factorization
Recent theory starts to explain implicit regularization with the model of deep matrix factorization (DMF) and analyze the trajectory of discrete gradient dynamics in the optimization process.
Adaptive Control of Client Selection and Gradient Compression for Efficient Federated Learning
Federated learning (FL) allows multiple clients cooperatively train models without disclosing local data.
CloudBrain-ReconAI: An Online Platform for MRI Reconstruction and Image Quality Evaluation
Efficient collaboration between engineers and radiologists is important for image reconstruction algorithm development and image quality evaluation in magnetic resonance imaging (MRI).
A Faithful Deep Sensitivity Estimation for Accelerated Magnetic Resonance Imaging
To understand the behavior of the network, the mutual promotion of sensitivity estimation and image reconstruction is revealed through the visualization of network intermediate results.
Physics-informed Deep Diffusion MRI Reconstruction with Synthetic Data: Break Training Data Bottleneck in Artificial Intelligence
In conclusion, PIDD presents a novel deep learning framework by exploiting the power of MRI physics, providing a cost-effective and explainable way to break the data bottleneck in deep learning medical imaging.
Weak-shot Semantic Segmentation via Dual Similarity Transfer
Proposal segmentation allows proposal-pixel similarity transfer from base classes to novel classes, which enables the mask learning of novel classes.
ZoomNAS: Searching for Whole-body Human Pose Estimation in the Wild
We propose a single-network approach, termed ZoomNet, to take into account the hierarchical structure of the full human body and solve the scale variation of different body parts.
Ranked #5 on
2D Human Pose Estimation
on COCO-WholeBody
PoseTrans: A Simple Yet Effective Pose Transformation Augmentation for Human Pose Estimation
Human pose estimation aims to accurately estimate a wide variety of human poses.
3D Interacting Hand Pose Estimation by Hand De-occlusion and Removal
Unlike most previous works that directly predict the 3D poses of two interacting hands simultaneously, we propose to decompose the challenging interacting hand pose estimation task and estimate the pose of each hand separately.
Pose for Everything: Towards Category-Agnostic Pose Estimation
In this paper, we introduce the task of Category-Agnostic Pose Estimation (CAPE), which aims to create a pose estimation model capable of detecting the pose of any class of object given only a few samples with keypoint definition.
Ranked #2 on
Category-Agnostic Pose Estimation
on MP100
Structure-aware Editable Morphable Model for 3D Facial Detail Animation and Manipulation
Previous works on morphable models mostly focus on large-scale facial geometry but ignore facial details.
Dual Adaptive Transformations for Weakly Supervised Point Cloud Segmentation
Weakly supervised point cloud segmentation, i. e. semantically segmenting a point cloud with only a few labeled points in the whole 3D scene, is highly desirable due to the heavy burden of collecting abundant dense annotations for the model training.
ScaleNet: Searching for the Model to Scale
Recently, community has paid increasing attention on model scaling and contributed to developing a model family with a wide spectrum of scales.
LightViT: Towards Light-Weight Convolution-Free Vision Transformers
Vision transformers (ViTs) are usually considered to be less light-weight than convolutional neural networks (CNNs) due to the lack of inductive bias.
HEAD: HEtero-Assists Distillation for Heterogeneous Object Detectors
We observe that the core difficulty for heterogeneous KD (hetero-KD) is the significant semantic gap between the backbone features of heterogeneous detectors due to the different optimization manners.
Submission to Generic Event Boundary Detection Challenge@CVPR 2022: Local Context Modeling and Global Boundary Decoding Approach
Local context modeling sub-network is proposed to perceive diverse patterns of generic event boundaries, and it generates powerful video representations and reliable boundary confidence.
Masked Distillation with Receptive Tokens
To obtain a group of masks, the receptive tokens are learned via the regular task loss but with teacher fixed, and we also leverage a Dice loss to enrich the diversity of learned masks.
Green Hierarchical Vision Transformer for Masked Image Modeling
We present an efficient approach for Masked Image Modeling (MIM) with hierarchical Vision Transformers (ViTs), allowing the hierarchical ViTs to discard masked patches and operate only on the visible ones.
Knowledge Distillation from A Stronger Teacher
In this paper, we show that simply preserving the relations between the predictions of teacher and student would suffice, and propose a correlation-based loss to capture the intrinsic inter-class relations from the teacher explicitly.
Ranked #3 on
Knowledge Distillation
on ImageNet
(using extra training data)
StyleGAN-Human: A Data-Centric Odyssey of Human Generation
In addition, a model zoo and human editing applications are demonstrated to facilitate future research in the community.
Joint-Modal Label Denoising for Weakly-Supervised Audio-Visual Video Parsing
This paper focuses on the weakly-supervised audio-visual video parsing task, which aims to recognize all events belonging to each modality and localize their temporal boundaries.
Generalizable Neural Performer: Learning Robust Radiance Fields for Human Novel View Synthesis
Specifically, we compress the light fields for novel view human rendering as conditional implicit neural radiance fields from both geometry and appearance aspects.
Simulating Fluids in Real-World Still Images
In this work, we tackle the problem of real-world fluid animation from a still image.
Not All Tokens Are Equal: Human-centric Visual Analysis via Token Clustering Transformer
Vision transformers have achieved great successes in many computer vision tasks.
Ranked #7 on
2D Human Pose Estimation
on COCO-WholeBody
A Keypoint-based Global Association Network for Lane Detection
Earlier works follow a top-down roadmap to regress predefined anchors into various shapes of lane lines, which lacks enough flexibility to fit complex shapes of lanes due to the fixed anchor shapes.
Ranked #4 on
Lane Detection
on TuSimple
(F1 score metric)
CholecTriplet2021: A benchmark challenge for surgical action triplet recognition
In this paper, we present the challenge setup and assessment of the state-of-the-art deep learning methods proposed by the participants during the challenge.
Ranked #1 on
Action Triplet Recognition
on CholecT50 (Challenge)
(using extra training data)
A Paired Phase and Magnitude Reconstruction for Advanced Diffusion-Weighted Imaging
Conclusion: The explicit phase model PAIR with complementary priors has a good performance on challenging reconstructions under inter-shot motions between shots and a low signal-to-noise ratio.
Learning Where to Learn in Cross-View Self-Supervised Learning
In this paper, we present a new approach, Learning Where to Learn (LEWEL), to adaptively aggregate spatial information of features, so that the projected embeddings could be exactly aligned and thus guide the feature learning better.
Searching for Network Width with Bilaterally Coupled Network
In BCNet, each channel is fairly trained and responsible for the same amount of network widths, thus each network width can be evaluated more accurately.
Bailando: 3D Dance Generation by Actor-Critic GPT with Choreographic Memory
With the learned choreographic memory, dance generation is realized on the quantized units that meet high choreography standards, such that the generated dancing sequences are confined within the spatial constraints.
Ranked #4 on
Motion Synthesis
on AIST++
DyRep: Bootstrapping Training with Dynamic Re-parameterization
Structural re-parameterization (Rep) methods achieve noticeable improvements on simple VGG-style networks.
Weak Augmentation Guided Relational Self-Supervised Learning
Self-supervised Learning (SSL) including the mainstream contrastive learning has achieved great success in learning visual representations without data annotations.
Ranked #64 on
Self-Supervised Image Classification
on ImageNet
SimMatch: Semi-supervised Learning with Similarity Matching
Learning with few labeled data has been a longstanding problem in the computer vision and machine learning research community.
Pseudo-Labeled Auto-Curriculum Learning for Semi-Supervised Keypoint Localization
PL approaches apply pseudo-labels to unlabeled data, and then train the model with a combination of the labeled and pseudo-labeled data iteratively.
Efficient and Reliable Overlay Networks for Decentralized Federated Learning
As such, our proposed overlay networks accelerate convergence, improve generalization, and enhance robustness to clients failures in DFL with theoretical guarantees.
Progressive Attention on Multi-Level Dense Difference Maps for Generic Event Boundary Detection
Generic event boundary detection is an important yet challenging task in video understanding, which aims at detecting the moments where humans naturally perceive event boundaries.
One-dimensional Deep Low-rank and Sparse Network for Accelerated MRI
Deep learning has shown astonishing performance in accelerated magnetic resonance imaging (MRI).
Exploring Versatile Prior for Human Motion via Motion Frequency Guidance
Prior plays an important role in providing the plausible constraint on human motion.
GreedyNASv2: Greedier Search with a Greedy Path Filter
In this paper, we leverage an explicit path filter to capture the characteristics of paths and directly filter those weak ones, so that the search can be thus implemented on the shrunk space more greedily and efficiently.
Weakly Supervised Contrastive Learning
Specifically, our proposed framework is based on two projection heads, one of which will perform the regular instance discrimination task.
Weak-shot Semantic Segmentation by Transferring Semantic Affinity and Boundary
As a result, we find that pixel-level annotation of base categories can facilitate affinity learning and propagation, leading to higher-quality CAMs of novel categories.
Graph-Based 3D Multi-Person Pose Estimation Using Multi-View Images
Following the top-down paradigm, we decompose the task into two stages, i. e. person localization and pose estimation.
Ranked #2 on
3D Multi-Person Pose Estimation
on Panoptic
(using extra training data)
Counterfactual Inference for Text Classification Debiasing
In inference, given a factual input document, Corsair imagines its two counterfactual counterparts to distill and mitigate the two biases captured by the poisonous model.
ReSSL: Relational Self-Supervised Learning with Weak Augmentation
Self-supervised Learning (SSL) including the mainstream contrastive learning has achieved great success in learning visual representations without data annotations.
Ranked #82 on
Self-Supervised Image Classification
on ImageNet
ViTAS: Vision Transformer Architecture Search
Vision transformers (ViTs) inherited the success of NLP but their structures have not been sufficiently investigated and optimized for visual tasks.
Pareidolia Face Reenactment
We present a new application direction named Pareidolia Face Reenactment, which is defined as animating a static illusory face to move in tandem with a human face in the video.
K-shot NAS: Learnable Weight-Sharing for NAS with K-shot Supernets
The operation weight for each path is represented as a convex combination of items in a dictionary with a simplex code.
BCNet: Searching for Network Width with Bilaterally Coupled Network
In BCNet, each channel is fairly trained and responsible for the same amount of network widths, thus each network width can be evaluated more accurately.
ViPNAS: Efficient Video Pose Estimation via Neural Architecture Search
Human pose estimation has achieved significant progress in recent years.
Ranked #23 on
Pose Estimation
on COCO test-dev
When Human Pose Estimation Meets Robustness: Adversarial Algorithms and Benchmarks
However, unlike human vision that is robust to various data corruptions such as blur and pixelation, current pose estimators are easily confused by these corruptions.
Invariance and Contraction in Geometrically Periodic Systems with Differential Inclusions
The objective of this paper is to derive the essential invariance and contraction properties for the geometric periodic systems, which can be formulated as a category of differential inclusions, and primarily rendered in the phase coordinate, or the cycle coordinate.
XCloud-pFISTA: A Medical Intelligence Cloud for Accelerated MRI
Machine learning and artificial intelligence have shown remarkable performance in accelerated magnetic resonance imaging (MRI).
Everything's Talkin': Pareidolia Face Reenactment
We present a new application direction named Pareidolia Face Reenactment, which is defined as animating a static illusory face to move in tandem with a human face in the video.
Prioritized Architecture Sampling with Monto-Carlo Tree Search
One-shot neural architecture search (NAS) methods significantly reduce the search cost by considering the whole search space as one network, which only needs to be trained once.
Reformulating HOI Detection as Adaptive Set Prediction
To attain this, we map a trainable interaction query set to an interaction prediction set with a transformer.
Ranked #30 on
Human-Object Interaction Detection
on HICO-DET
(using extra training data)
Locally Free Weight Sharing for Network Width Search
In this paper, to better evaluate each width, we propose a locally free weight sharing strategy (CafeNet) accordingly.
Towards Improving the Consistency, Efficiency, and Flexibility of Differentiable Neural Architecture Search
Our method enables differentiable sparsification, and keeps the derived architecture equivalent to that of Engine-cell, which further improves the consistency between search and evaluation.
EnTranNAS: Towards Closing the Gap between the Architectures in Search and Evaluation
The Engine-cell is differentiable for architecture search, while the Transit-cell only transits the current sub-graph by architecture derivation.
Explicit Learning Topology for Differentiable Neural Architecture Search
Differentiable neural architecture search (NAS) has gained much success in discovering more flexible and diverse cell types.
Learning With Privileged Tasks
Concretely, we introduce a privileged parameter so that the optimization direction does not necessarily follow the gradient from the privileged tasks, but concentrates more on the target tasks.
GAHNE: Graph-Aggregated Heterogeneous Network Embedding
Heterogeneous network embedding aims to embed nodes into low-dimensional vectors which capture rich intrinsic information of heterogeneous networks.
Agree to Disagree: Adaptive Ensemble Knowledge Distillation in Gradient Space
In this paper, we examine the diversity of teacher models in the gradient space and regard the ensemble knowledge distillation as a multi-objective optimization problem so that we can determine a better optimization direction for the training of student network.
Directed Graph Attention Neural Network Utilizing 3D Coordinates for Molecular Property Prediction
DGANN distinguishes from previous models with those features: (1) It learns the local chemical environment encoding by graph attention mechanism on chemical bonds.
Stretchable Cells Help DARTS Search Better
However, even for this consistent search, the searched cells often suffer from poor performance, especially for the supernet with fewer layers, as current DARTS methods are prone to wide and shallow cells, and this topology collapse induces sub-optimal searched cells.
AOT: Appearance Optimal Transport Based Identity Swapping for Forgery Detection
However, due to the lack of Deepfakes datasets with large variance in appearance, which can be hardly produced by recent identity swapping methods, the detection algorithm may fail in this situation.
Data Agnostic Filter Gating for Efficient Deep Networks
To deploy a well-trained CNN model on low-end computation edge devices, it is usually supposed to compress or prune the model under certain computation budget (e. g., FLOPs).
ISTA-NAS: Efficient and Consistent Neural Architecture Search by Sparse Coding
By doing so, our network for search at each update satisfies the sparsity constraint and is efficient to train.
HMOR: Hierarchical Multi-Person Ordinal Relations for Monocular Multi-Person 3D Pose Estimation
The HMOR encodes interaction information as the ordinal relations of depths and angles hierarchically, which captures the body-part and joint level semantic and maintains global consistency at the same time.
3D Multi-Person Pose Estimation (absolute)
3D Multi-Person Pose Estimation (root-relative)
+2
Whole-Body Human Pose Estimation in the Wild
This paper investigates the task of 2D human whole-body pose estimation, which aims to localize dense landmarks on the entire human body including face, hands, body, and feet.
Ranked #12 on
2D Human Pose Estimation
on COCO-WholeBody
Differentiable Hierarchical Graph Grouping for Multi-Person Pose Estimation
The modules of HGG can be trained end-to-end with the keypoint detection network and is able to supervise the grouping process in a hierarchical manner.
Ranked #4 on
Keypoint Detection
on OCHuman
Bi-directional Cross-Modality Feature Propagation with Separation-and-Aggregation Gate for RGB-D Semantic Segmentation
Depth information has proven to be a useful cue in the semantic segmentation of RGB-D images for providing a geometric counterpart to the RGB representation.
Ranked #1 on
Semantic Segmentation
on TLCGIS
Fast and Accurate: Structure Coherence Component for Face Alignment
In this paper, we propose a fast and accurate coordinate regression method for face alignment.
Ranked #15 on
Face Alignment
on COFW
Effects of Horizons on Entanglement Harvesting
We study the effects of horizons on the entanglement harvested between two Unruh-DeWitt detectors via the use of moving mirrors with and without strict horizons.
General Relativity and Quantum Cosmology High Energy Physics - Theory
TAM: Temporal Adaptive Module for Video Recognition
Video data is with complex temporal dynamics due to various factors such as camera motion, speed variation, and different activities.
Multiple uncertainty relation for accelerated quantum information
The uncertainty principle, first introduced by Heisenberg in inertial frames, clearly distinguishes quantum theories from classical mechanics.
Quantum Physics General Relativity and Quantum Cosmology
3D Sketch-aware Semantic Scene Completion via Semi-supervised Structure Prior
To this end, we first propose a novel 3D sketch-aware feature embedding to explicitly encode geometric information effectively and efficiently.
3D Semantic Scene Completion from a single RGB image
Hallucination
TransMoMo: Invariance-Driven Unsupervised Video Motion Retargeting
We present a lightweight video motion retargeting approach TransMoMo that is capable of transferring motion of a person in a source video realistically to another video of a target person.
GreedyNAS: Towards Fast One-Shot NAS with Greedy Supernet
The training efficiency is thus boosted since the training space has been greedily shrunk from all paths to those potentially-good ones.
Ranked #72 on
Neural Architecture Search
on ImageNet