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Li Liu 0002

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4 papers

1 author row

IROS Conference 2025 Conference Paper

ETA: Learning Optical Flow with Efficient Temporal Attention

Bo Wang 0144
Zhenping Sun
Yang Yu 0014
Li Liu 0002
Jian Li 0003
Dewen Hu

Considering the potential of using multi-frame information to solve the occlusion problem, we introduce a novel idea of multi-frame information integration, which uses the attention mechanism to fuse the temporal information from the previous frame. The idea can effectively improve the estimation accuracy in occluded regions and optimize the inference speed under multi-frame settings. Meanwhile, we suggest the concept of attention confidence to provide an explicit value criterion for the model to utilize useful attention information more efficiently. Furthermore, we propose an Efficient Temporal Attention network (ETA), which achieves promising results on Sintel and KITTI benchmarks, especially with a 9. 4% error reduction compared to the baseline method GMA on Sintel (test) Clean.

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ICLR Conference 2024 Conference Paper

DFormer: Rethinking RGBD Representation Learning for Semantic Segmentation

Bowen Yin
Xuying Zhang
Zhong-Yu Li 0006
Li Liu 0002
Ming-Ming Cheng
Qibin Hou

We present DFormer, a novel RGB-D pretraining framework to learn transferable representations for RGB-D segmentation tasks. DFormer has two new key innovations: 1) Unlike previous works that encode RGB-D information with RGB pretrained backbone, we pretrain the backbone using image-depth pairs from ImageNet-1K, and thus the DFormer is endowed with the capacity to encode RGB-D representations; 2) DFormer comprises a sequence of RGB-D blocks, which are tailored for encoding both RGB and depth information through a novel building block design. DFormer avoids the mismatched encoding of the 3D geometry relationships in depth maps by RGB pretrained backbones, which widely lies in existing methods but has not been resolved. We finetune the pretrained DFormer on two popular RGB-D tasks, i.e., RGB-D semantic segmentation and RGB-D salient object detection, with a lightweight decoder head. Experimental results show that our DFormer achieves new state-of-the-art performance on these two tasks with less than half of the computational cost of the current best methods on two RGB-D semantic segmentation datasets and five RGB-D salient object detection datasets. Code will be made publicly available.

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ICLR Conference 2023 Conference Paper

Evidential Uncertainty and Diversity Guided Active Learning for Scene Graph Generation

Shuzhou Sun
Shuaifeng Zhi
Janne Heikkilä
Li Liu 0002

Scene Graph Generation (SGG) has already shown its great potential in various downstream tasks, but it comes at the price of a prohibitively expensive annotation process. To reduce the annotation cost, we propose using Active Learning (AL) for sampling the most informative data. However, directly porting current AL methods to the SGG task poses the following challenges: 1) unreliable uncertainty estimates, and 2) data bias problems. To deal with these challenges, we propose EDAL (\textbf{E}vidential Uncertainty and \textbf{D}iversity Guided Deep \textbf{A}ctive \textbf{L}earning), a novel AL framework tailored for the SGG task. For challenge 1), we start with Evidential Deep Learning (EDL) coupled with a global relationship mining approach to estimate uncertainty, which can effectively overcome the perturbations of open-set relationships and background-relationships to obtain reliable uncertainty estimates. To address challenge 2), we seek the diversity-based method and design the Context Blocking Module (CBM) and Image Blocking Module (IBM) to alleviate context-level bias and image-level bias, respectively. Experiments show that our AL framework can approach the performance of a fully supervised SGG model with only about $10\%$ annotation cost. Furthermore, our ablation studies indicate that introducing AL into the SGG will face many challenges not observed in other vision tasks that are successfully overcome by our new modules.

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ICML Conference 2021 Conference Paper

One Pass Late Fusion Multi-view Clustering

Xinwang Liu 0002
Li Liu 0002
Qing Liao 0001
Siwei Wang 0001
Yi Zhang 0104
Wenxuan Tu
Chang Tang
Jiyuan Liu 0003

Existing late fusion multi-view clustering (LFMVC) optimally integrates a group of pre-specified base partition matrices to learn a consensus one. It is then taken as the input of the widely used k-means to generate the cluster labels. As observed, the learning of the consensus partition matrix and the generation of cluster labels are separately done. These two procedures lack necessary negotiation and can not best serve for each other, which may adversely affect the clustering performance. To address this issue, we propose to unify the aforementioned two learning procedures into a single optimization, in which the consensus partition matrix can better serve for the generation of cluster labels, and the latter is able to guide the learning of the former. To optimize the resultant optimization problem, we develop a four-step alternate algorithm with proved convergence. We theoretically analyze the clustering generalization error of the proposed algorithm on unseen data. Comprehensive experiments on multiple benchmark datasets demonstrate the superiority of our algorithm in terms of both clustering accuracy and computational efficiency. It is expected that the simplicity and effectiveness of our algorithm will make it a good option to be considered for practical multi-view clustering applications.

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