Multiple Object Tracking (MOT) is widely investigated in computer vision with many applications. Tracking-By-Detection (TBD) is a popular multiple-object tracking paradigm. TBD consists of the first step of object detection and the subsequent of data association, tracklet generation, and update. We propose a Similarity Learning Module (SLM) motivated from the Siamese network to extract important object appearance features and a procedure to combine object motion and appearance features effectively. This design strengthens the modeling of object motion and appearance features for data association. We design a Similarity Matching Cascade (SMC) for the data association of our SMILEtrack tracker. SMILEtrack achieves 81.06 MOTA and 80.5 IDF1 on the MOTChallenge and the MOT17 test set, respectively.

Generic unstructured neural networks have been shown to struggle on out-of-distribution compositional generalization. Compositional data augmentation via example recombination has transferred some prior knowledge about compositionality to such black-box neural models for several semantic parsing tasks, but this often required task-specific engineering or provided limited gains.We present a more powerful data recombination method using a model called Compositional Structure Learner (CSL). CSL is a generative model with a quasi-synchronous context-free grammar backbone, which we induce from the training data. We sample recombined examples from CSL and add them to the fine-tuning data of a pre-trained sequence-tosequence model (T5). This procedure effectively transfers most of CSL's compositional bias to T5 for diagnostic tasks, and results in a model even stronger than a T5-CSL ensemble on two real world compositional generalization tasks. This results in new state-ofthe-art performance for these challenging semantic parsing tasks requiring generalization to both natural language variation and novel compositions of elements. * Equal contribution. † Work done as part of the Google AI Residency program. 1 Also commonly referred to as elements or concepts.

We analyze the grounded SCAN (gSCAN) benchmark, which was recently proposed to study systematic generalization for grounded language understanding. First, we study which aspects of the original benchmark can be solved by commonly used methods in multimodal research. We find that a generalpurpose Transformer-based model with crossmodal attention achieves strong performance on a majority of the gSCAN splits, surprisingly outperforming more specialized approaches from prior work. Furthermore, our analysis suggests that many of the remaining errors reveal the same fundamental challenge in systematic generalization of linguistic constructs regardless of visual context. Second, inspired by this finding, we propose challenging new tasks for gSCAN by generating data to incorporate relations between objects in the visual environment. Finally, we find that current models are surprisingly data inefficient given the narrow scope of commands in gSCAN, suggesting another challenge for future work.

Generic unstructured neural networks have been shown to struggle on out-of-distribution compositional generalization. Compositional data augmentation via example recombination has transferred some prior knowledge about compositionality to such black-box neural models for several semantic parsing tasks, but this often required task-specific engineering or provided limited gains.We present a more powerful data recombination method using a model called Compositional Structure Learner (CSL). CSL is a generative model with a quasi-synchronous context-free grammar backbone, which we induce from the training data. We sample recombined examples from CSL and add them to the fine-tuning data of a pre-trained sequence-tosequence model (T5). This procedure effectively transfers most of CSL's compositional bias to T5 for diagnostic tasks, and results in a model even stronger than a T5-CSL ensemble on two real world compositional generalization tasks. This results in new state-ofthe-art performance for these challenging semantic parsing tasks requiring generalization to both natural language variation and novel compositions of elements. * Equal contribution. † Work done as part of the Google AI Residency program. 1 Also commonly referred to as elements or concepts.

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