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In this paper, we present a new sequence-to-sequence learning framework forvisual tracking, dubbed SeqTrack. It casts visual tracking as a sequencegeneration problem, which predicts object bounding boxes in an autoregressivefashion. This is different from prior Siamese trackers and transformertrackers, which rely on designing complicated head networks, such asclassification and regression heads. SeqTrack only adopts a simpleencoder-decoder transformer architecture. The encoder extracts visual featureswith a bidirectional transformer, while the decoder generates a sequence ofbounding box values autoregressively with a causal transformer. The lossfunction is a plain cross-entropy. Such a sequence learning paradigm not onlysimplifies tracking framework, but also achieves competitive performance onbenchmarks. For instance, SeqTrack gets 72.5% AUC on LaSOT, establishing a newstate-of-the-art performance. Code and models are available at here.

SeqTrack: Sequence to Sequence Learning for Visual Object Tracking