Vivienne Huiling Wang

I'm generally interested in reinforcement learning, deep learning, 2D/3D computer vision. Selected papers and patent are listed as follows. Full list of publications can be found in my Google Scholar page.

We propose a novel adversarially guided subgoal generation framework for goal-conditioned HRL to mitigate the issue of non-stationarity in off-policy training.

We propose a novel recurrent graph neural network to tackle hyperspectral image classification problem.

We propose a novel pruning loss to explicitly enforces the optimizer to focus on promising candidate filters while suppressing contributions of less relevant ones. In the meanwhile, we further propose to enforce the diversities between filters and this diversity-based regularization term improves the trade-off between model sizes and accuracies.

We address semantic video object segmentation via a novel cross-granularity hierarchical graphical model to integrate tracklet and object proposal reasoning with superpixel labeling.

We present a novel algorithm that detects recurring primary object and learns cohort object proposals over space-time in video. Our core contribution is a graph transduction process that exploits both appearance cues learned from rudimentary detections of object-like regions, and the intrinsic structures within video data.

We propose a semi-supervised approach to adapting CNN image recognition model trained from labeled image data to the target domain exploiting both semantic evidence learned from CNN, and the intrinsic structures of video data.

This paper presents a method to detect and segment recurring object from multi-view images. By harnessing a top-down explicit notion of object, our method overcomes the limitations of previous bottom-up methods that often mis-segment an object and de- livers high quality segmentation.

We combine geodesic distance information with the flexibility of level set methods in energy minimization, leveraging the complementary strengths of each to promote accurate boundary placement and strong region connectivity while requiring less user interaction.

We present a a robust and efficient approach for segmenting images with less and intuitive user interaction, particularly targeted for mobile touch screen devices.