David Y. Yang

Sethuraman Sankaran, David Yang, Ser-Nam Lim

Tasks that rely on multi-modal information typically include a fusion module that combines information from different modalities. In this work, we develop a Refiner Fusion Network (ReFNet) that enables fusion modules to combine strong unimodal representation with strong multimodal representations. ReFNet combines the fusion network with a decoding/defusing module, which imposes a modality-centric responsibility condition. This approach addresses a big gap in existing multimodal fusion frameworks by ensuring that both unimodal and fused representations are strongly encoded in the latent fusion space. We demonstrate that the Refiner Fusion Network can improve upon performance of powerful baseline fusion modules such as multimodal transformers. The refiner network enables inducing graphical representations of the fused embeddings in the latent space, which we prove under certain conditions and is supported by strong empirical results in the numerical experiments. These graph structures are further strengthened by combining the ReFNet with a Multi-Similarity contrastive loss function. The modular nature of Refiner Fusion Network lends itself to be combined with different fusion architectures easily, and in addition, the refiner step can be applied for pre-training on unlabeled datasets, thus leveraging unsupervised data towards improving performance. We demonstrate the power of Refiner Fusion Networks on three datasets, and further show that they can maintain performance with only a small fraction of labeled data.

Li Chen, David Yang, Purvi Goel et al.

This paper proposes CANC, a Co-teaching Active Noise Cancellation method, applied in spatial computing to address deep learning trained with extreme noisy labels. Deep learning algorithms have been successful in spatial computing for land or building footprint recognition. However a lot of noise exists in ground truth labels due to how labels are collected in spatial computing and satellite imagery. Existing methods to deal with extreme label noise conduct clean sample selection and do not utilize the remaining samples. Such techniques can be wasteful due to the cost of data retrieval. Our proposed CANC algorithm not only conserves high-cost training samples but also provides active label correction to better improve robust deep learning with extreme noisy labels. We demonstrate the effectiveness of CANC for building footprint recognition for spatial computing.