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Linfeng Gao

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ICRA Conference 2024 Conference Paper

EfficientDPS: Efficient and End-to-End Depth-aware Panoptic Segmentation

Shengkai Wu
Liangliang Ren
Linfeng Gao
Yupeng Li
Wen-Yu Liu 0003

Depth-aware panoptic segmentation (DPS) combines image segmentation and monocular depth estimation in a single model to achieve semantic and geometry perception simultaneously. DPS task has important applications in the robot area but the previous DPS models are too heavy to be applied. Thus, we propose EfficientDPS, an efficient, end-to-end, and unified model for DPS. In our method, query features extracted with convolution networks are used to represent things/stuff. In this way, different vision tasks such as classification, segmentation, and depth estimation can be realized in a unified manner, leading to a compact and efficient model. EfficientDPS can be trained and tested in an end-to-end manner via bipartite matching and complex post-process is not needed at inference. To enhance the supervision signal, group query representation is proposed, leading to better performance without affecting the inference speed. Extensive experiments on Cityscapes-DPS and SemKITTI-DPS show that EfficientDPS can achieve the best trade-off between speed and accuracy than the state-of-the-art methods.

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AAAI Conference 2024 Conference Paper

Response Enhanced Semi-supervised Dialogue Query Generation

Jianheng Huang
Ante Wang
Linfeng Gao
Linfeng Song
Jinsong Su

Leveraging vast and continually updated knowledge from the Internet has been considered an important ability for a dialogue system. Therefore, the dialogue query generation task is proposed for generating search queries from dialogue histories, which will be submitted to a search engine for retrieving relevant websites on the Internet. In this regard, previous efforts were devoted to collecting conversations with annotated queries and training a query producer (QP) via standard supervised learning. However, these studies still face the challenges of data scarcity and domain adaptation. To address these issues, in this paper, we propose a semi-supervised learning framework -- SemiDQG, to improve model performance with unlabeled conversations. Based on the observation that the search query is typically related to the topic of dialogue response, we train a response-augmented query producer (RA) to provide rich and effective training signals for QP. We first apply a similarity-based query selection strategy to select high-quality RA-generated pseudo queries, which are used to construct pseudo instances for training QP and RA. Then, we adopt the REINFORCE algorithm to further enhance QP, with RA-provided rewards as fine-grained training signals. Experimental results and in-depth analysis of three benchmarks show the effectiveness of our framework in cross-domain and low-resource scenarios. Particularly, SemiDQG significantly surpasses ChatGPT and competitive baselines. Our code is available at \url{https://github.com/DeepLearnXMU/SemiDQG}.

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