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Found 24 papers, 11 papers with code
Human-centered In-building Embodied Delivery Benchmark
This environment also includes autonomous human characters and robots with grasping and mobility capabilities, as well as a large number of interactive items.
SpatialBot: Precise Spatial Understanding with Vision Language Models
Vision Language Models (VLMs) have achieved impressive performance in 2D image understanding, however they are still struggling with spatial understanding which is the foundation of Embodied AI.
AIC MLLM: Autonomous Interactive Correction MLLM for Robust Robotic Manipulation
The ability to reflect on and correct failures is crucial for robotic systems to interact stably with real-life objects. Observing the generalization and reasoning capabilities of Multimodal Large Language Models (MLLMs), previous approaches have aimed to utilize these models to enhance robotic systems accordingly. However, these methods typically focus on high-level planning corrections using an additional MLLM, with limited utilization of failed samples to correct low-level contact poses which is particularly prone to occur during articulated object manipulation. To address this gap, we propose an Autonomous Interactive Correction (AIC) MLLM, which makes use of previous low-level interaction experiences to correct SE(3) pose predictions for articulated object.
GasTrace: Detecting Sandwich Attack Malicious Accounts in Ethereum
The openness and transparency of Ethereum transaction data make it easy to be exploited by any entities, executing malicious attacks.
NaturalVLM: Leveraging Fine-grained Natural Language for Affordance-Guided Visual Manipulation
To address these challenges, we introduce a comprehensive benchmark, NrVLM, comprising 15 distinct manipulation tasks, containing over 4500 episodes meticulously annotated with fine-grained language instructions.
ManipLLM: Embodied Multimodal Large Language Model for Object-Centric Robotic Manipulation
By fine-tuning the injected adapters, we preserve the inherent common sense and reasoning ability of the MLLMs while equipping them with the ability for manipulation.
LiDAR-LLM: Exploring the Potential of Large Language Models for 3D LiDAR Understanding
In this paper, we introduce LiDAR-LLM, which takes raw LiDAR data as input and harnesses the remarkable reasoning capabilities of LLMs to gain a comprehensive understanding of outdoor 3D scenes.
ImageManip: Image-based Robotic Manipulation with Affordance-guided Next View Selection
This framework is designed to capture multiple perspectives of the target object and infer depth information to complement its geometry.
Distribution-Aware Continual Test-Time Adaptation for Semantic Segmentation
In this paper, we propose a distribution-aware tuning (DAT) method to make the semantic segmentation CTTA efficient and practical in real-world applications.
Discuss Before Moving: Visual Language Navigation via Multi-expert Discussions
The performances on the representative VLN task R2R show that our method surpasses the leading zero-shot VLN model by a large margin on all metrics.
RenderOcc: Vision-Centric 3D Occupancy Prediction with 2D Rendering Supervision
3D occupancy prediction holds significant promise in the fields of robot perception and autonomous driving, which quantifies 3D scenes into grid cells with semantic labels.
Find What You Want: Learning Demand-conditioned Object Attribute Space for Demand-driven Navigation
The task of Visual Object Navigation (VON) involves an agent's ability to locate a particular object within a given scene.
An Overview of AI and Blockchain Integration for Privacy-Preserving
In conclusion, this paper outlines the future directions of privacy protection technologies emerging from AI and blockchain integration, including enhancing efficiency and security to achieve a more comprehensive privacy protection of privacy.
Exploring Sparse Visual Prompt for Domain Adaptive Dense Prediction
The visual prompts have provided an efficient manner in addressing visual cross-domain problems.
BEVUDA: Multi-geometric Space Alignments for Domain Adaptive BEV 3D Object Detection
In this paper, we propose a Multi-space Alignment Teacher-Student (MATS) framework to ease the domain shift accumulation, which consists of a Depth-Aware Teacher (DAT) and a Geometric-space Aligned Student (GAS) model.
Unsupervised Spike Depth Estimation via Cross-modality Cross-domain Knowledge Transfer
Neuromorphic spike data, an upcoming modality with high temporal resolution, has shown promising potential in autonomous driving by mitigating the challenges posed by high-velocity motion blur.
Efficient Meta-Tuning for Content-aware Neural Video Delivery
Our method significantly reduces the computational cost and achieves even better performance, paving the way for applying neural video delivery techniques to practical applications.
Adaptive Patch Exiting for Scalable Single Image Super-Resolution
Once the incremental capacity is below the threshold, the patch can exit at the specific layer.
A State-of-the-art Survey of U-Net in Microscopic Image Analysis: from Simple Usage to Structure Mortification
Image analysis technology is used to solve the inadvertences of artificial traditional methods in disease, wastewater treatment, environmental change monitoring analysis and convolutional neural networks (CNN) play an important role in microscopic image analysis.
What Can Machine Vision Do for Lymphatic Histopathology Image Analysis: A Comprehensive Review
In the past ten years, the computing power of machine vision (MV) has been continuously improved, and image analysis algorithms have developed rapidly.
SamplingAug: On the Importance of Patch Sampling Augmentation for Single Image Super-Resolution
However, existing methods mostly train the DNNs on uniformly sampled LR-HR patch pairs, which makes them fail to fully exploit informative patches within the image.
Overfitting the Data: Compact Neural Video Delivery via Content-aware Feature Modulation
Internet video delivery has undergone a tremendous explosion of growth over the past few years.
A Comprehensive Review of Computer-aided Whole-slide Image Analysis: from Datasets to Feature Extraction, Segmentation, Classification, and Detection Approaches
This paper reviews the methods of WSI analysis based on machine learning.
CLUE: Towards Discovering Locked Cryptocurrencies in Ethereum
Because the locked cryptocurrencies can never be controlled by users, avoid interacting with the accounts discovered by CLUE and repeating the same mistakes again can help users to save money.
Cryptography and Security
