Search Results for author:
Found 16 papers, 11 papers with code
The Instinctive Bias: Spurious Images lead to Hallucination in MLLMs
In this paper, we identify a typical class of inputs that baffles MLLMs, which consist of images that are highly relevant but inconsistent with answers, causing MLLMs to suffer from hallucination.
MLLM-Protector: Ensuring MLLM's Safety without Hurting Performance
The deployment of multimodal large language models (MLLMs) has brought forth a unique vulnerability: susceptibility to malicious attacks through visual inputs.
R-Tuning: Teaching Large Language Models to Refuse Unknown Questions
This approach is formalized by first identifying the knowledge gap between parametric knowledge and the instruction tuning data.
Towards Generalizable Multi-Camera 3D Object Detection via Perspective Debiasing
In our approach, we render diverse view maps from BEV features and rectify the perspective bias of these maps, leveraging implicit foreground volumes to bridge the camera and BEV planes.
MEDL-U: Uncertainty-aware 3D Automatic Annotation based on Evidential Deep Learning
In this paper, we present the first approach that addresses the inherent ambiguities present in pseudo labels by introducing an Evidential Deep Learning (EDL) based uncertainty estimation framework.
Optimal Sample Selection Through Uncertainty Estimation and Its Application in Deep Learning
Our proposed method, COPS (unCertainty based OPtimal Sub-sampling), is designed to minimize the expected loss of a model trained on subsampled data.
Lift3D: Synthesize 3D Training Data by Lifting 2D GAN to 3D Generative Radiance Field
This work explores the use of 3D generative models to synthesize training data for 3D vision tasks.
DORT: Modeling Dynamic Objects in Recurrent for Multi-Camera 3D Object Detection and Tracking
Recent multi-camera 3D object detectors usually leverage temporal information to construct multi-view stereo that alleviates the ill-posed depth estimation.
MV-FCOS3D++: Multi-View Camera-Only 4D Object Detection with Pretrained Monocular Backbones
In this technical report, we present our solution, dubbed MV-FCOS3D++, for the Camera-Only 3D Detection track in Waymo Open Dataset Challenge 2022.
MonoJSG: Joint Semantic and Geometric Cost Volume for Monocular 3D Object Detection
Based on the object depth, the dense coordinates patch together with the corresponding object features is reprojected to the image space to build a cost volume in a joint semantic and geometric error manner.
Contrastive ACE: Domain Generalization Through Alignment of Causal Mechanisms
Domain generalization aims to learn knowledge invariant across different distributions while semantically meaningful for downstream tasks from multiple source domains, to improve the model's generalization ability on unseen target domains.
Exploring Geometric Consistency for Monocular 3D Object Detection
In addition, we demonstrate that the augmentation methods are well suited for semi-supervised training and cross-dataset generalization.
Invariant Batch Normalization for Multi-source Domain Generalization
We consider the domain generalization problem, where the test domain differs from the training domain.
Weakly Supervised Disentangled Generative Causal Representation Learning
This paper proposes a Disentangled gEnerative cAusal Representation (DEAR) learning method under appropriate supervised information.
Constructing Self-motivated Pyramid Curriculums for Cross-Domain Semantic Segmentation: A Non-Adversarial Approach
We propose a new approach, called self-motivated pyramid curriculum domain adaptation (PyCDA), to facilitate the adaptation of semantic segmentation neural networks from synthetic source domains to real target domains.
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Known-class Aware Self-ensemble for Open Set Domain Adaptation
Particularly, in open set domain adaptation, we allow the classes from the source and target domains to be partially overlapped.
