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Found 23 papers, 10 papers with code
Semantic Gaussians: Open-Vocabulary Scene Understanding with 3D Gaussian Splatting
Open-vocabulary 3D scene understanding presents a significant challenge in computer vision, withwide-ranging applications in embodied agents and augmented reality systems.
VideoAgent: A Memory-augmented Multimodal Agent for Video Understanding
We explore how reconciling several foundation models (large language models and vision-language models) with a novel unified memory mechanism could tackle the challenging video understanding problem, especially capturing the long-term temporal relations in lengthy videos.
Parameter-Efficient Fine-Tuning for Pre-Trained Vision Models: A Survey
Large-scale pre-trained vision models (PVMs) have shown great potential for adaptability across various downstream vision tasks.
Muffin or Chihuahua? Challenging Large Vision-Language Models with Multipanel VQA
Our evaluation shows that questions in the MultipanelVQA benchmark pose significant challenges to the state-of-the-art Large Vision Language Models (LVLMs) tested, even though humans can attain approximately 99\% accuracy on these questions.
SSB: Simple but Strong Baseline for Boosting Performance of Open-Set Semi-Supervised Learning
In experiments, SSB greatly improves both inlier classification and outlier detection performance, outperforming existing methods by a large margin.
LLM-Coordination: Evaluating and Analyzing Multi-agent Coordination Abilities in Large Language Models
In this study, we introduce a new LLM-Coordination Benchmark aimed at a detailed analysis of LLMs within the context of Pure Coordination Games, where participating agents need to cooperate for the most gain.
Factored-NeuS: Reconstructing Surfaces, Illumination, and Materials of Possibly Glossy Objects
We develop a method that recovers the surface, materials, and illumination of a scene from its posed multi-view images.
R2H: Building Multimodal Navigation Helpers that Respond to Help Requests
Intelligent navigation-helper agents are critical as they can navigate users in unknown areas through environmental awareness and conversational ability, serving as potential accessibility tools for individuals with disabilities.
SoftMatch: Addressing the Quantity-Quality Trade-off in Semi-supervised Learning
The critical challenge of Semi-Supervised Learning (SSL) is how to effectively leverage the limited labeled data and massive unlabeled data to improve the model's generalization performance.
An Embarrassingly Simple Baseline for Imbalanced Semi-Supervised Learning
While standard SSL assumes uniform data distribution, we consider a more realistic and challenging setting called imbalanced SSL, where imbalanced class distributions occur in both labeled and unlabeled data.
JARVIS: A Neuro-Symbolic Commonsense Reasoning Framework for Conversational Embodied Agents
Building a conversational embodied agent to execute real-life tasks has been a long-standing yet quite challenging research goal, as it requires effective human-agent communication, multi-modal understanding, long-range sequential decision making, etc.
USB: A Unified Semi-supervised Learning Benchmark for Classification
We further provide the pre-trained versions of the state-of-the-art neural models for CV tasks to make the cost affordable for further tuning.
Aerial Vision-and-Dialog Navigation
To this end, we introduce Aerial Vision-and-Dialog Navigation (AVDN), to navigate a drone via natural language conversation.
FreeMatch: Self-adaptive Thresholding for Semi-supervised Learning
Semi-supervised Learning (SSL) has witnessed great success owing to the impressive performances brought by various methods based on pseudo labeling and consistency regularization.
Revisiting Consistency Regularization for Semi-Supervised Learning
Generally, the aim is to train a model that is invariant to various data augmentations.
CoSSL: Co-Learning of Representation and Classifier for Imbalanced Semi-Supervised Learning
In this paper, we propose a novel co-learning framework (CoSSL) with decoupled representation learning and classifier learning for imbalanced SSL.
Multi-Vector Embedding on Networks with Taxonomies
Networks serve as efficient tools to describe close relationships among nodes.
Learn by Observation: Imitation Learning for Drone Patrolling from Videos of A Human Navigator
Extensive experiments are conducted to demonstrate the accuracy of the proposed imitating learning process as well as the reliability of the holistic system for autonomous drone navigation.
Analyzing the Dependency of ConvNets on Spatial Information
In this paper, we are pushing the envelope and aim to further investigate the reliance on spatial information.
CN-CELEB: a challenging Chinese speaker recognition dataset
These datasets tend to deliver over optimistic performance and do not meet the request of research on speaker recognition in unconstrained conditions.
Tag2Vec: Learning Tag Representations in Tag Networks
Network embedding is a method to learn low-dimensional representation vectors for nodes in complex networks.
Parameter-Free Spatial Attention Network for Person Re-Identification
We propose a novel architecture for Person Re-Identification, based on a novel parameter-free spatial attention layer introducing spatial relations among the feature map activations back to the model.
Ranked #20 on
Person Re-Identification
on DukeMTMC-reID
Feature vector regularization in machine learning
Such feature vector regularization inherits a property from function denoising on ${\bf R}^n$, in that accuracy is non-monotonic in the denoising (regularization) parameter $\alpha$.
