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Found 38 papers, 24 papers with code
SeqAfford: Sequential 3D Affordance Reasoning via Multimodal Large Language Model
In this paper, we introduce the Sequential 3D Affordance Reasoning task, which extends the traditional paradigm by reasoning from cumbersome user intentions and then decomposing them into a series of segmentation maps.
NLPrompt: Noise-Label Prompt Learning for Vision-Language Models
This matrix effectively partitions datasets into clean and noisy subsets, allowing for the application of cross-entropy loss to the clean subset and MAE loss to the noisy subset.
Understanding Representation of Deep Equilibrium Models from Neural Collapse Perspective
In this paper, we utilize the Neural Collapse ($\mathcal{NC}$) as a tool to systematically analyze the representation of DEQ under both balanced and imbalanced conditions.
Federated Learning from Vision-Language Foundation Models: Theoretical Analysis and Method
Integrating pretrained vision-language foundation models like CLIP into federated learning has attracted significant attention for enhancing generalization across diverse tasks.
Monocular Human-Object Reconstruction in the Wild
Learning the prior knowledge of the 3D human-object spatial relation is crucial for reconstructing human-object interaction from images and understanding how humans interact with objects in 3D space.
StackFLOW: Monocular Human-Object Reconstruction by Stacked Normalizing Flow with Offset
Modeling and capturing the 3D spatial arrangement of the human and the object is the key to perceiving 3D human-object interaction from monocular images.
Uniform Transformation: Refining Latent Representation in Variational Autoencoders
Irregular distribution in latent space causes posterior collapse, misalignment between posterior and prior, and ill-sampling problem in Variational Autoencoders (VAEs).
Diffusion-based Reinforcement Learning via Q-weighted Variational Policy Optimization
To overcome this, we propose a novel model-free diffusion-based online RL algorithm, Q-weighted Variational Policy Optimization (QVPO).
Harmonizing Generalization and Personalization in Federated Prompt Learning
Federated Prompt Learning (FPL) incorporates large pre-trained Vision-Language models (VLM) into federated learning through prompt tuning.
A Unified Diffusion Framework for Scene-aware Human Motion Estimation from Sparse Signals
One of the biggest challenges to this task is the one-to-many mapping from sparse observations to dense full-body motions, which endowed inherent ambiguities.
HOI-M3:Capture Multiple Humans and Objects Interaction within Contextual Environment
Humans naturally interact with both others and the surrounding multiple objects, engaging in various social activities.
A Distributionally Robust Model Predictive Control for Static and Dynamic Uncertainties in Smart Grids
The distinctive contribution of this paper lies in its holistic approach to both static and dynamic uncertainties in smart grids.
Gaze-guided Hand-Object Interaction Synthesis: Dataset and Method
Gaze plays a crucial role in revealing human attention and intention, particularly in hand-object interaction scenarios, where it guides and synchronizes complex tasks that require precise coordination between the brain, hand, and object.
THOR: Text to Human-Object Interaction Diffusion via Relation Intervention
This paper addresses new methodologies to deal with the challenging task of generating dynamic Human-Object Interactions from textual descriptions (Text2HOI).
Global and Local Prompts Cooperation via Optimal Transport for Federated Learning
Specifically, for each client, we learn a global prompt to extract consensus knowledge among clients, and a local prompt to capture client-specific category characteristics.
Unsupervised Cross-Domain Image Retrieval via Prototypical Optimal Transport
This paper introduces ProtoOT, a novel Optimal Transport formulation explicitly tailored for UCIR, which integrates intra-domain feature representation learning and cross-domain alignment into a unified framework.
Guidance with Spherical Gaussian Constraint for Conditional Diffusion
We theoretically show the existence of manifold deviation by establishing a certain lower bound for the estimation error of the loss guidance.
HOI-M^3: Capture Multiple Humans and Objects Interaction within Contextual Environment
Humans naturally interact with both others and the surrounding multiple objects engaging in various social activities.
HybridGait: A Benchmark for Spatial-Temporal Cloth-Changing Gait Recognition with Hybrid Explorations
Existing gait recognition benchmarks mostly include minor clothing variations in the laboratory environments, but lack persistent changes in appearance over time and space.
Fed-CO2: Cooperation of Online and Offline Models for Severe Data Heterogeneity in Federated Learning
Specifically, the online model learns general knowledge that is shared among all clients, while the offline model is trained locally to learn the specialized knowledge of each individual client.
Contextually Affinitive Neighborhood Refinery for Deep Clustering
Previous endeavors in self-supervised learning have enlightened the research of deep clustering from an instance discrimination perspective.
CSOT: Curriculum and Structure-Aware Optimal Transport for Learning with Noisy Labels
However, the current approaches rely heavily on the model's predictions and evaluate each sample independently without considering either the global and local structure of the sample distribution.
Two Sides of The Same Coin: Bridging Deep Equilibrium Models and Neural ODEs via Homotopy Continuation
Deep Equilibrium Models (DEQs) and Neural Ordinary Differential Equations (Neural ODEs) are two branches of implicit models that have achieved remarkable success owing to their superior performance and low memory consumption.
Reduced Policy Optimization for Continuous Control with Hard Constraints
To the best of our knowledge, RPO is the first attempt that introduces GRG to RL as a way of efficiently handling both equality and inequality hard constraints.
IKOL: Inverse kinematics optimization layer for 3D human pose and shape estimation via Gauss-Newton differentiation
This paper presents an inverse kinematic optimization layer (IKOL) for 3D human pose and shape estimation that leverages the strength of both optimization- and regression-based methods within an end-to-end framework.
Ranked #30 on
3D Human Pose Estimation
on 3DPW
NeuralDome: A Neural Modeling Pipeline on Multi-View Human-Object Interactions
We construct a dense multi-view dome to acquire a complex human object interaction dataset, named HODome, that consists of $\sim$75M frames on 10 subjects interacting with 23 objects.
Lifelong Person Re-Identification via Knowledge Refreshing and Consolidation
Lifelong person re-identification (LReID) is in significant demand for real-world development as a large amount of ReID data is captured from diverse locations over time and cannot be accessed at once inherently.
Knowledge-Aware Federated Active Learning with Non-IID Data
In this paper, we propose a federated active learning paradigm to efficiently learn a global model with limited annotation budget while protecting data privacy in a decentralized learning way.
FedTP: Federated Learning by Transformer Personalization
Instead of using a vanilla personalization mechanism that maintains personalized self-attention layers of each client locally, we develop a learn-to-personalize mechanism to further encourage the cooperation among clients and to increase the scablability and generalization of FedTP.
Unified Optimal Transport Framework for Universal Domain Adaptation
Notably, UniOT is the first method with the capability to automatically discover and recognize private categories in the target domain for UniDA.
Ranked #3 on
Universal Domain Adaptation
on Office-Home
Federated Fuzzy Neural Network with Evolutionary Rule Learning
ERL is inspired by the theory of biological evolution; it encourages rule variations while activating superior rules and deactivating inferior rules for local clients with non-IID data.
Federated Reinforcement Learning for Real-Time Electric Vehicle Charging and Discharging Control
This paper develops an optimal EV charging/discharging control strategy for different EV users under dynamic environments to maximize EV users' benefits.
Alternating Differentiation for Optimization Layers
In this paper, we developed a new framework, named Alternating Differentiation (Alt-Diff), that differentiates optimization problems (here, specifically in the form of convex optimization problems with polyhedral constraints) in a fast and recursive way.
Distributed Semi-supervised Fuzzy Regression with Interpolation Consistency Regularization
In this work, we proposed a distributed Fuzzy C-means (DFCM) method and a distributed interpolation consistency regularization (DICR) built on the well-known alternating direction method of multipliers to respectively locate parameters in antecedent and consequent components of DSFR.
Hierarchical fuzzy neural networks with privacy preservation for heterogeneous big data
The network is trained with a two-stage optimization algorithm, and the parameters at low levels of the hierarchy are learned with a scheme based on the well-known alternating direction method of multipliers, which does not reveal local data to other agents.
Mutual Adaptive Reasoning for Monocular 3D Multi-Person Pose Estimation
This method first uses 2. 5D pose and geometry information to infer camera-centric root depths in a forward pass, and then exploits the root depths to further improve representation learning of 2. 5D pose estimation in a backward pass.
Toward multi-target self-organizing pursuit in a partially observable Markov game
The proposed distributed algorithm: fuzzy self-organizing cooperative coevolution (FSC2) is then leveraged to resolve the three challenges in multi-target SOP: distributed self-organizing search (SOS), distributed task allocation, and distributed single-target pursuit.
Learning from Crowds with Sparse and Imbalanced Annotations
Traditional supervised learning requires ground truth labels for the training data, whose collection can be difficult in many cases.
