Neural Architecture Search (NAS) aims to automatically find effective architectures within a predefined search space. However, the search space is often extremely large. As a result, directly searching in such a large search space is non-trivial and also very time-consuming. To address the above issues, in each search step, we seek to limit the search space to a small but effective subspace to boost both the search performance and search efficiency. To this end, we propose a novel Neural Architecture Search method via Dominative Subspace Mining (DSM-NAS) that finds promising architectures in automatically mined subspaces. Specifically, we first perform a global search,i.e., dominative subspace mining, to find a good subspace from a set of candidates. Then, we perform a local search within the mined subspace to find effective architectures. More critically, we further boost search performance by taking well-designed/ searched architectures to initialize candidate subspaces. Experimental results demonstrate that DSM-NAS not only reduces the search cost but also discovers better architectures than state-of-the-art methods in various benchmark search spaces.

Human motion prediction is to predict future human states based on the observed human states. However, current research ignores the semantic correlations between body parts (joints and bones) in the observed human states and motion time; thus, the prediction accuracy is limited. To address this issue, we propose a novel semantic correlation attention-based multiorder multiscale feature fusion network (SCAFF), which includes an encoder and a decoder. In the encoder, a multiorder difference calculation module (MODC) is designed to calculate the multiorder difference information of joint and bone attributes in the observed human states. Then, multiple semantic correlation attention-based graph calculation operators (SCA-GCOs) are stacked to extract the multiscale features of the multiorder difference information. Each SCA-GCO captures joint and bone dependencies of the multiorder difference information, refines them with a semantic correlation attention module (SCAM), and captures temporal dynamics of the refined joint and bone dependencies as the output features. Note that SCAM learns a semantic attention mask describing the semantic correlations between body parts and motion time for feature refinement. Afterward, multiple multiorder feature fusion modules (MOFFs) and multiscale feature fusion modules (MSFFs) are designed to fuse the multiscale features of the multiorder difference information extracted by multiple SCA-GCOs, thus obtaining the motion features of the observed human states. Based on the obtained motion features, the decoder recurrently recruits a composite gated recurrent module (CGRM) and multilayer perceptrons (MLPs) to predict future human states. As far as we know, this is the first attempt to consider the semantic correlations between body parts and motion time in human motion prediction. The results on public datasets demonstrate that SCAFF outperforms existing models.

Zero-shot quantization (ZSQ) is promising for compressing and accelerating deep neural networks when the data for training full-precision models are inaccessible. In ZSQ, network quantization is performed using synthetic samples, thus, the performance of quantized models depends heavily on the quality of synthetic samples. Nonetheless, we find that the synthetic samples constructed in existing ZSQ methods can be easily fitted by models. Accordingly, quantized models obtained by these methods suffer from significant performance degradation on hard samples. To address this issue, we propose HArd sample Synthesizing and Training (HAST). Specifically, HAST pays more attention to hard samples when synthesizing samples and makes synthetic samples hard to fit when training quantized models. HAST aligns features extracted by full-precision and quantized models to ensure the similarity between features extracted by these two models. Extensive experiments show that HAST significantly outperforms existing ZSQ methods, achieving performance comparable to models that are quantized with real data.

Open-world object detection (OWOD), as a more general and challenging goal, requires the model trained from data on known objects to detect both known and unknown objects and incrementally learn to identify these unknown objects. The existing works which employ standard detection framework and fixed pseudo-labelling mechanism$(PLM)$have the following problems: (i) The inclusion of detecting unknown objects substantially reduces the model's ability to detect known ones. (ii) The$PLM$does not adequately utilize the priori knowledge of inputs. (iii) The fixed selection manner of$PLM$cannot guarantee that the model is trained in the right direction. We observe that humans subconsciously prefer to focus on all foreground objects and then identify each one in detail, rather than localize and identify a single object simultaneously, for alleviating the confusion. This motivates us to propose a novel solution called CAT: LoCalization and IdentificAtion Cascade Detection Transformer which decouples the detection process via the shared decoder in the cascade decoding way. In the meanwhile, we propose the self-adaptive pseudo-labelling mechanism which combines the model-driven with input-driven$PLM$and self-adaptively generates robust pseudo-labels for unknown objects, significantly improving the ability of CAT to retrieve unknown objects. Experiments on two benchmarks, i.e., MS-COCO and PASCAL VOC, show that our model outperforms the state-of-the-art methods. The code is publicly available at https://github.com/xiaomabufei/CAT.