No abstract

No abstract

Transformer trackers have achieved impressive advancements recently, where the attention mechanism plays an important role. However, the independent correlation computation in the attention mechanism could result in noisy and ambiguous attention weights, which inhibits further performance improvement. To address this issue, we propose an attention in attention (AiA) module, which enhances appropriate correlations and suppresses erroneous ones by seeking consensus among all correlation vectors. Our AiA module can be readily applied to both self-attention blocks and cross-attention blocks to facilitate feature aggregation and information propagation for visual tracking. Moreover, we propose a streamlined Transformer tracking framework, dubbed AiA-Track, by introducing efficient feature reuse and target-background embeddings to make full use of temporal references. Experiments show that our tracker achieves state-of-the-art performance on six tracking benchmarks while running at a real-time speed. Code and models are publicly available at https://github.com/Little-Podi/AiATrack.

No abstract

IC50 值为 31 nmol/L, 并能显著抑制异柠檬酸脱氢酶 1 突变(R132H)型人脑星形胶质母细胞瘤细胞中 2-HG 的产生. 根据 实验数据进行了初步的构效关系讨论和分子模拟研究. 关键词 3-(吲哚-3-基)-4-(吡唑并[3,4-c]哒嗪-3-基)马来酰亚胺; 异柠檬酸脱氢酶 1 突变体(R132H)抑制剂; 合成; 生物 活性 Synthesis and Evaluation of 3-(Indol-3-yl)-4-(pyrazolo[3,4c]pyridazin-3-yl)maleimides as Potent Mutant Isocitrate Dehydrogenase-1 Inhibitors