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Found 26 papers, 3 papers with code
OpenEarthSensing: Large-Scale Fine-Grained Benchmark for Open-World Remote Sensing
In open-world remote sensing, deployed models must continuously adapt to a steady influx of new data, which often exhibits various shifts compared to what the model encountered during the training phase.
EDENet: Echo Direction Encoding Network for Place Recognition Based on Ground Penetrating Radar
Ground penetrating radar (GPR) based localization has gained significant recognition in robotics due to its ability to detect stable subsurface features, offering advantages in environments where traditional sensors like cameras and LiDAR may struggle.
Vision Language Models are In-Context Value Learners
Instead, GVL poses value estimation as a temporal ordering problem over shuffled video frames; this seemingly more challenging task encourages VLMs to more fully exploit their underlying semantic and temporal grounding capabilities to differentiate frames based on their perceived task progress, consequently producing significantly better value predictions.
Mobility VLA: Multimodal Instruction Navigation with Long-Context VLMs and Topological Graphs
The high-level policy consists of a long-context VLM that takes the demonstration tour video and the multimodal user instruction as input to find the goal frame in the tour video.
Ghost imaging-based Non-contact Heart Rate Detection
Remote heart rate measurement is an increasingly concerned research field, usually using remote photoplethysmography (rPPG) to collect heart rate information through video data collection.
HC-GAE: The Hierarchical Cluster-based Graph Auto-Encoder for Graph Representation Learning
To this end, during the encoding process, we commence by utilizing the hard node assignment to decompose a sample graph into a family of separated subgraphs.
SSHPool: The Separated Subgraph-based Hierarchical Pooling
We commence by assigning the nodes of a sample graph into different clusters, resulting in a family of separated subgraphs.
MATRIX: Multi-Agent Trajectory Generation with Diverse Contexts
Data-driven methods have great advantages in modeling complicated human behavioral dynamics and dealing with many human-robot interaction applications.
PIVOT: Iterative Visual Prompting Elicits Actionable Knowledge for VLMs
In each iteration, the image is annotated with a visual representation of proposals that the VLM can refer to (e. g., candidate robot actions, localizations, or trajectories).
AutoRT: Embodied Foundation Models for Large Scale Orchestration of Robotic Agents
We experimentally show that such "in-the-wild" data collected by AutoRT is significantly more diverse, and that AutoRT's use of LLMs allows for instruction following data collection robots that can align to human preferences.
SpatialVLM: Endowing Vision-Language Models with Spatial Reasoning Capabilities
By training a VLM on such data, we significantly enhance its ability on both qualitative and quantitative spatial VQA.
Ranked #5 on
Spatial Reasoning
on 6-DoF SpatialBench
RT-Trajectory: Robotic Task Generalization via Hindsight Trajectory Sketches
Generalization remains one of the most important desiderata for robust robot learning systems.
Collaborative Knowledge Graph Fusion by Exploiting the Open Corpus
To alleviate the challenges of building Knowledge Graphs (KG) from scratch, a more general task is to enrich a KG using triples from an open corpus, where the obtained triples contain noisy entities and relations.
Writing Style Aware Document-level Event Extraction
This verifies the writing style contains valuable information that could improve the performance of the event extraction task.
Ghost Imaging Based on Recurrent Neural Network
The testing results show that the proposed method can achieve image reconstruction at a very low sampling rate (0. 38$\%$).
Grouptron: Dynamic Multi-Scale Graph Convolutional Networks for Group-Aware Dense Crowd Trajectory Forecasting
Accurate, long-term forecasting of pedestrian trajectories in highly dynamic and interactive scenes is a long-standing challenge.
History Encoding Representation Design for Human Intention Inference
In this extended abstract, we investigate the design of learning representation for human intention inference.
Speckles-Training-Based Denoising Convolutional Neural Network Ghost Imaging
Ghost imaging (GI) has been paid attention gradually because of its lens-less imaging capability, turbulence-free imaging and high detection sensitivity.
Generative-Adversarial-Networks-based Ghost Recognition
The proposed method can improve the problems caused by conventional recognition methods that based on target image information, and provide a certain turbulence-free ability.
RoBERTa-wwm-ext Fine-Tuning for Chinese Text Classification
Bidirectional Encoder Representations from Transformers (BERT) have shown to be a promising way to dramatically improve the performance across various Natural Language Processing tasks [Devlin et al., 2019].
COCOI: Contact-aware Online Context Inference for Generalizable Non-planar Pushing
General contact-rich manipulation problems are long-standing challenges in robotics due to the difficulty of understanding complicated contact physics.
Cross-Supervised Joint-Event-Extraction with Heterogeneous Information Networks
To verify the effectiveness of our proposed method, we conduct extensive experiments on four real-world datasets as well as compare our method with state-of-the-art methods.
End-to-end Autonomous Driving Perception with Sequential Latent Representation Learning
Current autonomous driving systems are composed of a perception system and a decision system.
Zero-shot Deep Reinforcement Learning Driving Policy Transfer for Autonomous Vehicles based on Robust Control
Although deep reinforcement learning (deep RL) methods have lots of strengths that are favorable if applied to autonomous driving, real deep RL applications in autonomous driving have been slowed down by the modeling gap between the source (training) domain and the target (deployment) domain.
Imaging around corners with single-pixel detector by computational ghost imaging
We have designed a single-pixel camera with imaging around corners based on computational ghost imaging.
