COSMIC: Compress Satellite Images Efficiently via Diffusion Compensation

With the rapidly increasing number of satellites in space and their enhanced capabilities, the amount of earth observation images collected by satellites is exceeding the transmission limits of satellite-to-ground links. Although existing learned image compression solutions achieve remarkable performance by using a sophisticated encoder to extract fruitful features as compression and using a decoder to reconstruct, it is still hard to directly deploy those complex encoders on current satellites' embedded GPUs with limited computing capability and power supply to compress images in orbit. In this paper, we propose COSMIC, a simple yet effective learned compression solution to transmit satellite images. We first design a lightweight encoder (i.e. reducing FLOPs by $2.6\sim 5\times $) on satellite to achieve a high image compression ratio to save satellite-to-ground links. Then, for reconstructions on the ground, to deal with the feature extraction ability degradation due to simplifying encoders, we propose a diffusion-based model to compensate image details when decoding. Our insight is that satellite's earth observation photos are not just images but indeed multi-modal data with a nature of Text-to-Image pairing since they are collected with rich sensor data (e.g. coordinates, timestamp, etc.) that can be used as the condition for diffusion generation. Extensive experiments show that COSMIC outperforms state-of-the-art baselines on both perceptual and distortion metrics.

Language Generation via Combinatorial Constraint Satisfaction: A Tree Search Enhanced Monte-Carlo Approach

Generating natural language under complex constraints is a principled formulation towards controllable text generation. We present a framework to allow specification of combinatorial constraints for sentence generation. We propose TSMH, an efficient method to generate high likelihood sentences with respect to a pre-trained language model while satisfying the constraints. Our approach is highly flexible, requires no task-specific training, and leverages efficient constraint satisfaction solving techniques. To better handle the combinatorial constraints, a tree search algorithm is embedded into the proposal process of the Markov chain Monte Carlo (MCMC) to explore candidates that satisfy more constraints. Compared to existing MCMC approaches, our sampling approach has a better mixing performance. Experiments show that TSMH achieves consistent and significant improvement on multiple language generation tasks.

Looking Beyond Label Noise: Shifted Label Distribution Matters in Distantly Supervised Relation Extraction

In recent years there is surge of interest in applying distant supervision (DS) to automatically generate training data for relation extraction. However, despite extensive efforts have been done on constructing advanced neural models, our experiments reveal that these neural models demonstrate only similar (or even worse) performance as compared with simple, feature-based methods. In this paper, we conduct thorough analysis to answer the question what other factors limit the performance of DS-trained neural models? Our results show that shifted labeled distribution commonly exists on real-world DS datasets, and impact of such issue is further validated using synthetic datasets for all models. Building upon the new insight, we develop a simple yet effective adaptation method for DS methods, called bias adjustment, to update models learned over source domain (i.e., DS training set) with label distribution statistics estimated on target domain (i.e., evaluation set). Experiments demonstrate that bias adjustment achieves consistent performance gains on all methods, especially on neural models, with up to a 22% relative F1 improvement.