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Found 16 papers, 7 papers with code
EnerVerse: Envisioning Embodied Future Space for Robotics Manipulation
We introduce EnerVerse, a comprehensive framework for embodied future space generation specifically designed for robotic manipulation tasks.
GATE OpenING: A Comprehensive Benchmark for Judging Open-ended Interleaved Image-Text Generation
While the progress in unified models offers new solutions, existing benchmarks are insufficient for evaluating these methods due to data size and diversity limitations.
DIETS: Diabetic Insulin Management System in Everyday Life
People with diabetes need insulin delivery to effectively manage their blood glucose levels, especially after meals, because their bodies either do not produce enough insulin or cannot fully utilize it.
Language-centered Human Activity Recognition
To address this, we propose LanHAR, a novel system that leverages Large Language Models (LLMs) to generate semantic interpretations of sensor readings and activity labels for cross-dataset HAR.
Invisible Optical Adversarial Stripes on Traffic Sign against Autonomous Vehicles
This paper presents an attack that uses light-emitting diodes and exploits the camera's rolling shutter effect to create adversarial stripes in the captured images to mislead traffic sign recognition.
Unleashing the Power of Unlabeled Data: A Self-supervised Learning Framework for Cyber Attack Detection in Smart Grids
Different from existing approaches, the proposed framework does not rely on large amounts of well-curated labeled data but makes use of the massive unlabeled data in the wild which are easily accessible.
DiffHarmony: Latent Diffusion Model Meets Image Harmonization
To deal with these issues, in this paper, we first adapt a pre-trained latent diffusion model to the image harmonization task to generate the harmonious but potentially blurry initial images.
Synthesizing Knowledge-enhanced Features for Real-world Zero-shot Food Detection
The complexity of food semantic attributes further makes it more difficult for current ZSD methods to distinguish various food categories.
CMMU: A Benchmark for Chinese Multi-modal Multi-type Question Understanding and Reasoning
However, the mastery of domain-specific knowledge, which is essential for evaluating the intelligence of MLLMs, continues to be a challenge.
SeeDS: Semantic Separable Diffusion Synthesizer for Zero-shot Food Detection
To tackle this, we propose the Semantic Separable Diffusion Synthesizer (SeeDS) framework for Zero-Shot Food Detection (ZSFD).
Ranked #1 on
Generalized Zero-Shot Object Detection
on MS-COCO
ISDA: Position-Aware Instance Segmentation with Deformable Attention
Most instance segmentation models are not end-to-end trainable due to either the incorporation of proposal estimation (RPN) as a pre-processing or non-maximum suppression (NMS) as a post-processing.
Ranked #19 on
Instance Segmentation
on COCO test-dev
(APL metric)
Boltzmann machines as two-dimensional tensor networks
Using numerical experiments, we demonstrate that the proposed algorithm is much more accurate than the state-of-the-art machine learning methods in estimating the partition function of restricted Boltzmann machines and deep Boltzmann machines, and have potential applications in training deep Boltzmann machines for general machine learning tasks.
Dynamic Virtual Graph Significance Networks for Predicting Influenza
In this study, we develop a novel method, Dynamic Virtual Graph Significance Networks (DVGSN), which can supervisedly and dynamically learn from similar "infection situations" in historical timepoints.
Contracting Arbitrary Tensor Networks: general approximate algorithm and applications in graphical models and quantum circuit simulations
We present a general method for approximately contracting tensor networks with an arbitrary connectivity.
Computational Physics Statistical Mechanics Strongly Correlated Electrons Quantum Physics
Phase transitions and optimal algorithms for semi-supervised classifications on graphs: from belief propagation to graph convolution network
For the first time, well-controlled benchmark datasets with asymptotially exact properties and optimal solutions could be produced for the evaluation of graph convolution neural networks, and for the theoretical understanding of their strengths and weaknesses.
Solving Statistical Mechanics on Sparse Graphs with Feedback Set Variational Autoregressive Networks
We propose a method for solving statistical mechanics problems defined on sparse graphs.
