Human-object interaction (HOI) detection aims to detect the spatial positions of human-object pairs and recognize their interactions. Existing single-branch, two-branch, and three-branch methods are challenging to make an appropriate trade-off on efficiency, multi-task decoupling, and collaborative learning, while they fail to identify rare and complex interaction categories effectively as well. In this work, we propose a novel Efficient Mamba-based Disentangled Progressive Learning (HOIMamba) for HOI Detection to absorb the advantages of the existing three approaches and adaptively aggregate multi-level interaction semantics guided by cross-task bidirectional information contexts. Specifically, HOIMamba builds an efficient and effective decoder through cascaded Low-Rank Adaptations (LoRAs), with high efficiency, thorough decoupling of tasks, and good multi-task collaborative learning. Furthermore, to alleviate the recognition problem of interactions in difficult HOI samples, a novel Mamba-based comprehensive progressive learning strategy with Cross-enhance Mamba (CEM) blocks and Detection Context Propagation (DCP) blocks is designed to gradually excavate interaction-related discriminative cues from four levels. CEM blocks automatically aggregate context to generate diverse task-shared semantics and simultaneously realize the cross-task interaction between human and object branches, guiding the interaction branch to extract more expressive HOI representation. DCP blocks further transfer the comprehensive interaction context to human and object branches to achieve rich and effective information exchange, facilitating the model to discover more HOI instances. Extensive experimental results on two standard benchmarks demonstrate the effectiveness of our HOIMamba.

This paper presents a 10-b statistical ADC (S-ADC), achieving higher resolution (INL) than any previously reported S-ADC. This resolution requires a large number of statistical observations via comparators (12 k) with offset variation, making code estimation a key challenge. The efficiency of estimation is enhanced by a coarse frontend estimator, employing pipelining and sub-ranging to arrive at a reduced range, which is then provided to a fine backend estimator. The total computations are reduced by 19×, compared to single-stage estimation over the entire analog range. Implemented in a 32 nm process, the S-ADC achieves INLRMS. Designed to run at 20 MHz, excess supply impedance limits comparator speed to 2 MHz. The energy per 10-b conversion for the comparator array (at 2 MHz) is 744 pJ and the energy per 10-b conversion of the digital estimator (at 20 MHz) is 627 pJ.