Author name cluster

Beiwen Tian

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

4 papers

2 author rows

ICRA Conference 2023 Conference Paper

From Semi-supervised to Omni-supervised Room Layout Estimation Using Point Clouds

Huan-ang Gao
Beiwen Tian
Pengfei Li 0007
Xiaoxue Chen
Hao Zhao 0002
Guyue Zhou
Yurong Chen 0001
Hongbin Zha

Room layout estimation is a long-existing robotic vision task that benefits both environment sensing and motion planning. However, layout estimation using point clouds (PCs) still suffers from data scarcity due to annotation difficulty. As such, we address the semi-supervised setting of this task based upon the idea of model exponential moving averaging. But adapting this scheme to the state-of-the-art (SOTA) solution for PC-based layout estimation is not straightforward. To this end, we define a quad set matching strategy and several consistency losses based upon metrics tailored for layout quads. Besides, we propose a new online pseudo-label harvesting algorithm that decomposes the distribution of a hybrid distance measure between quads and PC into two components. This technique does not need manual threshold selection and intuitively encourages quads to align with reliable layout points. Surprisingly, this framework also works for the fully-supervised setting, achieving a new SOTA on the ScanNet benchmark. Last but not least, we also push the semi-supervised setting to the realistic omni-supervised setting, demonstrating significantly promoted performance on a newly annotated ARKitScenes testing set. Our codes, data and models are made publicly available**Code: https://github.com/AIR-DISCOVER/Omni-PQ.

Details

ICRA Conference 2023 Conference Paper

Unsupervised Road Anomaly Detection with Language Anchors

Beiwen Tian
Mingdao Liu
Huan-ang Gao
Pengfei Li 0007
Hao Zhao 0002
Guyue Zhou

Road anomaly detection is critical to safe autonomous driving, because current road scene understanding models are usually trained in a closed-set manner and fail to identify unknown objects. What's worse, it is difficult, if not impossible, to collect a large-scale dataset with anomaly annotations. So this paper studies unsupervised anomaly detection which finds out anomaly regions using scene parsing logits solely. While former methods depend on the weights learned from the closed training set as anchors for logit generation, we resort to language anchors that are learned from enormous paired vision and language data. Thanks to rich open-set semantic information contained in these language anchors, our method performs better than former unsupervised counterparts while maintaining the advantage of training without accessing any out-of-distribution data. We delve into this new paradigm and identify the superiority of using pair-wise binary logits, which we credit to a better understanding of the negation language anchor. Last but not least, we find that the former top-1 selection of semantic labels for uncertainty measurement is problematic in many cases and a new blended standardization strategy brings clear improvements to our solution. We report state-of-the-art performance on FS LostAndFound, LostAndFound and RoadAnomaly datasets among comparable methods. The codes are publicly available at https://github.com/TB5z035/URAD-LA.git

Details

NeurIPS Conference 2022 Conference Paper

TOIST: Task Oriented Instance Segmentation Transformer with Noun-Pronoun Distillation

Pengfei Li
Beiwen Tian
Yongliang Shi
Xiaoxue Chen
Hao Zhao
Guyue Zhou
Ya-Qin Zhang

Current referring expression comprehension algorithms can effectively detect or segment objects indicated by nouns, but how to understand verb reference is still under-explored. As such, we study the challenging problem of task oriented detection, which aims to find objects that best afford an action indicated by verbs like sit comfortably on. Towards a finer localization that better serves downstream applications like robot interaction, we extend the problem into task oriented instance segmentation. A unique requirement of this task is to select preferred candidates among possible alternatives. Thus we resort to the transformer architecture which naturally models pair-wise query relationships with attention, leading to the TOIST method. In order to leverage pre-trained noun referring expression comprehension models and the fact that we can access privileged noun ground truth during training, a novel noun-pronoun distillation framework is proposed. Noun prototypes are generated in an unsupervised manner and contextual pronoun features are trained to select prototypes. As such, the network remains noun-agnostic during inference. We evaluate TOIST on the large-scale task oriented dataset COCO-Tasks and achieve +10. 7% higher $\rm{mAP^{box}}$ than the best-reported results. The proposed noun-pronoun distillation can boost $\rm{mAP^{box}}$ and $\rm{mAP^{mask}}$ by +2. 6% and +3. 6%. Codes and models are publicly available.

PDF Details

NeurIPS Conference 2022 Conference Paper

Unsupervised Cross-Task Generalization via Retrieval Augmentation

Bill Yuchen Lin
Kangmin Tan
Chris Miller
Beiwen Tian
Xiang Ren

Humans can perform unseen tasks by recalling relevant skills acquired previously and then generalizing them to the target tasks, even if there is no supervision at all. In this paper, we aim to improve this kind of cross-task generalization ability of massive multi-task language models, such as T0 and FLAN, in an unsupervised setting. We propose a retrieval-augmentation method named ReCross that takes a few unlabelled examples as queries to retrieve a small subset of upstream data and uses them to update the multi-task model for better generalization. ReCross is a straightforward yet effective retrieval method that combines both efficient dense retrieval and effective pair-wise reranking. Our results and analysis show that it significantly outperforms both non-retrieval methods and other baseline methods.

PDF Details