1 citations · 3 across the 10 of their papers we have counts for
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MIRAGE: Assessing Hallucination in Multimodal Reasoning Chains of MLLM
Bowen Dong, Minheng Ni, Zitong Huang +3
Multimodal hallucination in multimodal large language models (MLLMs) restricts the correctness of MLLMs. However, multimodal hallucinations are multi-sourced and arise from diverse…
Segmenting Objectiveness and Task-awareness Unknown Region for Autonomous Driving
Mi Zheng, Guanglei Yang, Zitong Huang +3
With the emergence of transformer-based architectures and large language models (LLMs), the accuracy of road scene perception has substantially advanced. Nonetheless, current road…
MetricDepth: Enhancing Monocular Depth Estimation with Deep Metric Learning
Chunpu Liu, Guanglei Yang, Wangmeng Zuo +1
Deep metric learning aims to learn features relying on the consistency or divergence of class labels. However, in monocular depth estimation, the absence of a natural definition of…
Geo-ConvGRU: Geographically Masked Convolutional Gated Recurrent Unit for Bird-Eye View Segmentation
Guanglei Yang, Yongqiang Zhang, Wanlong Li +5
Convolutional Neural Networks (CNNs) have significantly impacted various computer vision tasks, however, they inherently struggle to model long-range dependencies explicitly due to…
Multi-Modality Driven LoRA for Adverse Condition Depth Estimation
Guanglei Yang, Rui Tian, Yongqiang Zhang +3
The autonomous driving community is increasingly focused on addressing corner case problems, particularly those related to ensuring driving safety under adverse conditions (e.g., n…
Unprejudiced Training Auxiliary Tasks Makes Primary Better: A Multi-Task Learning Perspective
Yuanze Li, Chun-Mei Feng, Qilong Wang +2
Human beings can leverage knowledge from relative tasks to improve learning on a primary task. Similarly, multi-task learning methods suggest using auxiliary tasks to enhance a neu…
MR-GDINO: Efficient Open-World Continual Object Detection
Bowen Dong, Zitong Huang, Guanglei Yang +2
Open-world (OW) recognition and detection models show strong zero- and few-shot adaptation abilities, inspiring their use as initializations in continual learning methods to improv…
ConSept: Continual Semantic Segmentation via Adapter-based Vision Transformer
Bowen Dong, Guanglei Yang, Wangmeng Zuo +1
In this paper, we delve into the realm of vision transformers for continual semantic segmentation, a problem that has not been sufficiently explored in previous literature. Empiric…
FILP-3D: Enhancing 3D Few-shot Class-incremental Learning with Pre-trained Vision-Language Models
Wan Xu, Tianyu Huang, Tianyu Qu +3
Few-shot class-incremental learning (FSCIL) aims to mitigate the catastrophic forgetting issue when a model is incrementally trained on limited data. However, many of these works l…
DualAug: Exploiting Additional Heavy Augmentation with OOD Data Rejection
Zehao Wang, Yiwen Guo, Qizhang Li +2
Data augmentation is a dominant method for reducing model overfitting and improving generalization. Most existing data augmentation methods tend to find a compromise in augmenting…