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Found 25 papers, 13 papers with code
MJ-Bench: Is Your Multimodal Reward Model Really a Good Judge for Text-to-Image Generation?
Compared with open-source VLMs, smaller-sized scoring models can provide better feedback regarding text-image alignment and image quality, while VLMs provide more accurate feedback regarding safety and generation bias due to their stronger reasoning capabilities.
CARES: A Comprehensive Benchmark of Trustworthiness in Medical Vision Language Models
Artificial intelligence has significantly impacted medical applications, particularly with the advent of Medical Large Vision Language Models (Med-LVLMs), sparking optimism for the future of automated and personalized healthcare.
Enhancing Visual-Language Modality Alignment in Large Vision Language Models via Self-Improvement
In this paper, we propose SIMA, a framework that enhances visual and language modality alignment through self-improvement, eliminating the needs for external models or data.
Ranked #108 on
Visual Question Answering
on MM-Vet
Calibrated Self-Rewarding Vision Language Models
In the reward modeling, we employ a step-wise strategy and incorporate visual constraints into the self-rewarding process to place greater emphasis on visual input.
Ranked #63 on
Visual Question Answering
on MM-Vet
Aligning Modalities in Vision Large Language Models via Preference Fine-tuning
This procedure is not perfect and can cause the model to hallucinate - provide answers that do not accurately reflect the image, even when the core LLM is highly factual and the vision backbone has sufficiently complete representations.
How Many Unicorns Are in This Image? A Safety Evaluation Benchmark for Vision LLMs
Different from prior studies, we shift our focus from evaluating standard performance to introducing a comprehensive safety evaluation suite, covering both out-of-distribution (OOD) generalization and adversarial robustness.
Holistic Analysis of Hallucination in GPT-4V(ision): Bias and Interference Challenges
To bridge this gap, we introduce a new benchmark, namely, the Bias and Interference Challenges in Visual Language Models (Bingo).
Analyzing and Mitigating Object Hallucination in Large Vision-Language Models
Large vision-language models (LVLMs) have shown remarkable abilities in understanding visual information with human languages.
Evaluation and Analysis of Hallucination in Large Vision-Language Models
In this paper, we propose Hallucination Evaluation based on Large Language Models (HaELM), an LLM-based hallucination evaluation framework.
Semantic-Human: Neural Rendering of Humans from Monocular Video with Human Parsing
In this paper, we present Semantic-Human, a novel method that achieves both photorealistic details and viewpoint-consistent human parsing for the neural rendering of humans.
Overlap Bias Matching is Necessary for Point Cloud Registration
Specifically, we propose a plug-and-play Overlap Bias Matching Module (OBMM) comprising two integral components, overlap sampling module and bias prediction module.
DualGenerator: Information Interaction-based Generative Network for Point Cloud Completion
To tackle these challenges, we propose an information interaction-based generative network for point cloud completion ($\mathbf{DualGenerator}$).
Contrastive Label Enhancement
Label distribution learning (LDL) is a new machine learning paradigm for solving label ambiguity.
mPLUG-Owl: Modularization Empowers Large Language Models with Multimodality
Our code, pre-trained model, instruction-tuned models, and evaluation set are available at https://github. com/X-PLUG/mPLUG-Owl.
Ranked #1 on
Visual Question Answering (VQA)
on HallusionBench
(Question Pair Acc metric)
Visual Question Answering (VQA)
Zero-Shot Video Question Answer
Semantically Consistent Multi-view Representation Learning
In this work, we devote ourselves to the challenging task of Unsupervised Multi-view Representation Learning (UMRL), which requires learning a unified feature representation from multiple views in an unsupervised manner.
Multi-view Semantic Consistency based Information Bottleneck for Clustering
In this way, the learned semantic consistency from multi-view data can improve the information bottleneck to more exactly distinguish the consistent information and learn a unified feature representation with more discriminative consistent information for clustering.
MCoCo: Multi-level Consistency Collaborative Multi-view Clustering
Further, we designed a multi-level consistency collaboration strategy, which utilizes the consistent information of semantic space as a self-supervised signal to collaborate with the cluster assignments in feature space.
Ranked #1 on
Multiview Clustering
on Fashion-MNIST
Center Feature Fusion: Selective Multi-Sensor Fusion of Center-based Objects
In this work, we propose a novel approach Center Feature Fusion (CFF), in which we leverage center-based detection networks in both the camera and LiDAR streams to identify relevant object locations.
What Matters for 3D Scene Flow Network
Our proposed model surpasses all existing methods by at least 38. 2% on FlyingThings3D dataset and 24. 7% on KITTI Scene Flow dataset for EPE3D metric.
SST-Calib: Simultaneous Spatial-Temporal Parameter Calibration between LIDAR and Camera
With information from multiple input modalities, sensor fusion-based algorithms usually out-perform their single-modality counterparts in robotics.
DetMatch: Two Teachers are Better Than One for Joint 2D and 3D Semi-Supervised Object Detection
While numerous 3D detection works leverage the complementary relationship between RGB images and point clouds, developments in the broader framework of semi-supervised object recognition remain uninfluenced by multi-modal fusion.
A Simple and Efficient Multi-task Network for 3D Object Detection and Road Understanding
Detecting dynamic objects and predicting static road information such as drivable areas and ground heights are crucial for safe autonomous driving.
Labels Are Not Perfect: Inferring Spatial Uncertainty in Object Detection
As a result, an in-depth evaluation among different object detection methods remains challenging, and the training process of object detectors is sub-optimal, especially in probabilistic object detection.
Inferring Spatial Uncertainty in Object Detection
Based on the spatial distribution, we further propose an extension of IoU, called the Jaccard IoU (JIoU), as a new evaluation metric that incorporates label uncertainty.
UrbanLoco: A Full Sensor Suite Dataset for Mapping and Localization in Urban Scenes
Mapping and localization is a critical module of autonomous driving, and significant achievements have been reached in this field.
