Semi-attention Partition for Occluded Person Re-identification

This paper proposes a Semi-Attention Partition (SAP) method to learn well-aligned part features for occluded person reidentification (re-ID). Currently, the mainstream methods employ either external semantic partition or attention-based partition, and the latter manner is usually better than the former one. Under this background, this paper explores a potential that the "weak" semantic partition can be a good teacher for the "strong" attention-based partition. In other words, the attention-based student can substantially surpass its noisy semantic-based teacher, contradicting the common sense that the student usually achieves inferior (or comparable) accuracy. A key to this effect is: the proposed SAP encourages the attention-based partition of the (transformer) student to be partially consistent with the semantic-based teacher partition through knowledge distillation, yielding the so-called semi-attention. Such partial consistency allows the student to have both consistency and reasonable conflict with the noisy teacher. More specifically, on the one hand, the attention is guided by the semantic partition from the teacher. On the other hand, the attention mechanism itself still has some degree of freedom to comply with the inherent similarity between different patches, thus gaining resistance against noisy supervision. Moreover, we integrate a battery of well-engineered designs into SAP to reinforce their cooperation (e.g., multiple forms of teacherstudent consistency), as well as to promote reasonable conflict (e.g., mutual absorbing partition refinement and a supervision signal dropout strategy). Experimental results confirm that the transformer student achieves substantial improvement after this semi-attention learning scheme, and produces new stateof-the-art accuracy on several standard re-ID benchmarks.