Jingchao Wang

Jingchao Wang, Hong Wang, Wenlong Zhang et al.

Multi-task visual grounding (MTVG) includes two sub-tasks, i.e., Referring Expression Comprehension (REC) and Referring Expression Segmentation (RES). The existing representative approaches generally follow the research pipeline which mainly consists of three core procedures, including independent feature extraction for visual and linguistic modalities, respectively, cross-modal interaction module, and independent prediction heads for different sub-tasks. Albeit achieving remarkable performance, this research line has two limitations: 1) The linguistic content has not been fully injected into the entire visual backbone for boosting more effective visual feature extraction and it needs an extra cross-modal interaction module; 2) The relationship between REC and RES tasks is not effectively exploited to help the collaborative prediction for more accurate output. To deal with these problems, in this paper, we propose a Progressive Language-guided Visual Learning framework for multi-task visual grounding, called PLVL, which not only finely mine the inherent feature expression of the visual modality itself but also progressively inject the language information to help learn linguistic-related visual features. In this manner, our PLVL does not need additional cross-modal fusion module while fully introducing the language guidance. Furthermore, we analyze that the localization center for REC would help identify the to-be-segmented object region for RES to some extent. Inspired by this investigation, we design a multi-task head to accomplish collaborative predictions for these two sub-tasks. Extensive experiments conducted on several benchmark datasets comprehensively substantiate that our PLVL obviously outperforms the representative methods in both REC and RES tasks. https://github.com/jcwang0602/PLVL

Wenhui Liu, Zhijian Wu, Jingchao Wang et al.

Offline reinforcement learning seeks to derive improved policies entirely from historical data but often struggles with over-optimistic value estimates for out-of-distribution (OOD) actions. This issue is typically mitigated via policy constraint or conservative value regularization methods. However, these approaches may impose overly constraints or biased value estimates, potentially limiting performance improvements. To balance exploitation and restriction, we propose an Imagination-Limited Q-learning (ILQ) method, which aims to maintain the optimism that OOD actions deserve within appropriate limits. Specifically, we utilize the dynamics model to imagine OOD action-values, and then clip the imagined values with the maximum behavior values. Such design maintains reasonable evaluation of OOD actions to the furthest extent, while avoiding its over-optimism. Theoretically, we prove the convergence of the proposed ILQ under tabular Markov decision processes. Particularly, we demonstrate that the error bound between estimated values and optimality values of OOD state-actions possesses the same magnitude as that of in-distribution ones, thereby indicating that the bias in value estimates is effectively mitigated. Empirically, our method achieves state-of-the-art performance on a wide range of tasks in the D4RL benchmark.

Shuhang Wang, Szu-Yeu Hu, Eugene Cheah et al.

This paper proposes a novel U-Net variant using stacked dilated convolutions for medical image segmentation (SDU-Net). SDU-Net adopts the architecture of vanilla U-Net with modifications in the encoder and decoder operations (an operation indicates all the processing for feature maps of the same resolution). Unlike vanilla U-Net which incorporates two standard convolutions in each encoder/decoder operation, SDU-Net uses one standard convolution followed by multiple dilated convolutions and concatenates all dilated convolution outputs as input to the next operation. Experiments showed that SDU-Net outperformed vanilla U-Net, attention U-Net (AttU-Net), and recurrent residual U-Net (R2U-Net) in all four tested segmentation tasks while using parameters around 40% of vanilla U-Net's, 17% of AttU-Net's, and 15% of R2U-Net's.

Szu-Yeu Hu, Shuhang Wang, Wei-Hung Weng et al.

Modern deep learning algorithms geared towards clinical adaption rely on a significant amount of high fidelity labeled data. Low-resource settings pose challenges like acquiring high fidelity data and becomes the bottleneck for developing artificial intelligence applications. Ultrasound images, stored in Digital Imaging and Communication in Medicine (DICOM) format, have additional metadata data corresponding to ultrasound image parameters and medical exams. In this work, we leverage DICOM metadata from ultrasound images to help learn representations of the ultrasound image. We demonstrate that the proposed method outperforms the non-metadata based approaches across different downstream tasks.