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Researcher profile

Yongqiang Yao

Yongqiang Yao contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Computer Visionastro-ph.IMastro-ph.COastro-ph.GAastro-ph.SR

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Published work

7 published item(s)

preprint2026arXiv

SenseNova-U1: Unifying Multimodal Understanding and Generation with NEO-unify Architecture

Recent large vision-language models (VLMs) remain fundamentally constrained by a persistent dichotomy: understanding and generation are treated as distinct problems, leading to fragmented architectures, cascaded pipelines, and misaligned representation spaces. We argue that this divide is not merely an engineering artifact, but a structural limitation that hinders the emergence of native multimodal intelligence. Hence, we introduce SenseNova-U1, a native unified multimodal paradigm built upon NEO-unify, in which understanding and generation evolve as synergistic views of a single underlying process. We launch two native unified variants, SenseNova-U1-8B-MoT and SenseNova-U1-A3B-MoT, built on dense (8B) and mixture-of-experts (30B-A3B) understanding baselines, respectively. Designed from first principles, they rival top-tier understanding-only VLMs across text understanding, vision-language perception, knowledge reasoning, agentic decision-making, and spatial intelligence. Meanwhile, they deliver strong semantic consistency and visual fidelity, excelling in conventional or knowledge-intensive any-to-image (X2I) synthesis, complex text-rich infographic generation, and interleaved vision-language generation, with or without think patterns. Beyond performance, we show detailed model design, data preprocessing, pre-/post-training, and inference strategies to support community research. Last but not least, preliminary evidence demonstrates that our models extend beyond perception and generation, performing strongly in vision-language-action (VLA) and world model (WM) scenarios. This points toward a broader roadmap where models do not translate between modalities, but think and act across them in a native manner. Multimodal AI is no longer about connecting separate systems, but about building a unified one and trusting the necessary capabilities to emerge from within.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Equalized Focal Loss for Dense Long-Tailed Object Detection

Despite the recent success of long-tailed object detection, almost all long-tailed object detectors are developed based on the two-stage paradigm. In practice, one-stage detectors are more prevalent in the industry because they have a simple and fast pipeline that is easy to deploy. However, in the long-tailed scenario, this line of work has not been explored so far. In this paper, we investigate whether one-stage detectors can perform well in this case. We discover the primary obstacle that prevents one-stage detectors from achieving excellent performance is: categories suffer from different degrees of positive-negative imbalance problems under the long-tailed data distribution. The conventional focal loss balances the training process with the same modulating factor for all categories, thus failing to handle the long-tailed problem. To address this issue, we propose the Equalized Focal Loss (EFL) that rebalances the loss contribution of positive and negative samples of different categories independently according to their imbalance degrees. Specifically, EFL adopts a category-relevant modulating factor which can be adjusted dynamically by the training status of different categories. Extensive experiments conducted on the challenging LVIS v1 benchmark demonstrate the effectiveness of our proposed method. With an end-to-end training pipeline, EFL achieves 29.2% in terms of overall AP and obtains significant performance improvements on rare categories, surpassing all existing state-of-the-art methods. The code is available at https://github.com/ModelTC/EOD.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

New Massive Contact Twin Binary in a Radio-quiet HII Region Associated with the M17 Complex

Early-B stars may create an HII region that appears as radio-quiet. We report the identification of new early-B stars associated with the radio-quiet HII region G014.645--00.606 in the M17 complex. The ratio-quiet HII region G014.645--00.606 is adjacent to three radio-quiet WISE HII region candidates. The ionizing sources of the radio-quiet HII regions are expected to later than B1V, given the sensitivity about 1-2 mJy of the MAGPIS 20 cm survey. The stars were first selected if their parallaxes of GAIA EDR3 match that of the 22 GHz H$_2$O maser source within the same region. We used the color-magnitude diagram made from the ZTF photometric catalog to select the candidates for massive stars because the intrinsic $g-r$ colors of massive stars change little from B-type to O-type stars. Five stars lie in the areas of the color-magnitude diagram where either reddened massive stars or evolved post-main sequence stars of lower masses are commonly found. Three of the five stars, sources 1, 2, and 3, are located at the cavities of the three IR bubbles, and extended H$α$ emission is detected around the three IR bubbles. We suggest that sources 1, 2, and 3 are candidates for early-B stars associated with the radio-quiet region G014.645--00.606. Particularly, source 1 is an EW type eclipsing binary with a short period of 0.825 day, while source 2 is an EA type eclipsing binary with a short period of 0.919 day. The physical parameters of the two binary systems have been derived through the PHOEBE model. Source 1 is a twin binary of two stars with T~23,500 K, and source 2 contains a hotter component (T~20,100 K) and a cooler one (T~15,500 K). The $O-C$ values of source 1 show a trend of decline, implying that the period of the source is deceasing. Source 1 is likely a contacting early-B twin binary, for which mass transfer might cause its orbit to shrink.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

The design of the Ali CMB Polarization Telescope receiver

Ali CMB Polarization Telescope (AliCPT-1) is the first CMB degree-scale polarimeter to be deployed on the Tibetan plateau at 5,250m above sea level. AliCPT-1 is a 90/150 GHz 72 cm aperture, two-lens refracting telescope cooled down to 4 K. Alumina lenses, 800mm in diameter, image the CMB in a 33.4° field of view on a 636mm wide focal plane. The modularized focal plane consists of dichroic polarization-sensitive Transition-Edge Sensors (TESes). Each module includes 1,704 optically active TESes fabricated on a 150mm diameter silicon wafer. Each TES array is read out with a microwave multiplexing readout system capable of a multiplexing factor up to 2,048. Such a large multiplexing factor has allowed the practical deployment of tens of thousands of detectors, enabling the design of a receiver that can operate up to 19 TES arrays for a total of 32,376 TESes. AliCPT-1 leverages the technological advancements in the detector design from multiple generations of previously successful feedhorn-coupled polarimeters, and in the instrument design from BICEP-3, but applied on a larger scale. The cryostat receiver is currently under integration and testing. During the first deployment year, the focal plane will be populated with up to 4 TES arrays. Further TES arrays will be deployed in the following years, fully populating the focal plane with 19 arrays on the fourth deployment year. Here we present the AliCPT-1 receiver design, and how the design has been optimized to meet the experimental requirements.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selection

Object detection has been dominated by anchor-based detectors for several years. Recently, anchor-free detectors have become popular due to the proposal of FPN and Focal Loss. In this paper, we first point out that the essential difference between anchor-based and anchor-free detection is actually how to define positive and negative training samples, which leads to the performance gap between them. If they adopt the same definition of positive and negative samples during training, there is no obvious difference in the final performance, no matter regressing from a box or a point. This shows that how to select positive and negative training samples is important for current object detectors. Then, we propose an Adaptive Training Sample Selection (ATSS) to automatically select positive and negative samples according to statistical characteristics of object. It significantly improves the performance of anchor-based and anchor-free detectors and bridges the gap between them. Finally, we discuss the necessity of tiling multiple anchors per location on the image to detect objects. Extensive experiments conducted on MS COCO support our aforementioned analysis and conclusions. With the newly introduced ATSS, we improve state-of-the-art detectors by a large margin to $50.7\%$ AP without introducing any overhead. The code is available at https://github.com/sfzhang15/ATSS

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Cross-dataset Training for Class Increasing Object Detection

We present a conceptually simple, flexible and general framework for cross-dataset training in object detection. Given two or more already labeled datasets that target for different object classes, cross-dataset training aims to detect the union of the different classes, so that we do not have to label all the classes for all the datasets. By cross-dataset training, existing datasets can be utilized to detect the merged object classes with a single model. Further more, in industrial applications, the object classes usually increase on demand. So when adding new classes, it is quite time-consuming if we label the new classes on all the existing datasets. While using cross-dataset training, we only need to label the new classes on the new dataset. We experiment on PASCAL VOC, COCO, WIDER FACE and WIDER Pedestrian with both solo and cross-dataset settings. Results show that our cross-dataset pipeline can achieve similar impressive performance simultaneously on these datasets compared with training independently.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Optical turbulence at Ali, China -- Results from the first year of lunar scintillometer observations

The location of an astronomical observatory is a key factor that affects its scientific productivity. The best astronomical sites are generally those found at high altitudes. Several such sites in western China and the Tibetan plateau are presently under development for astronomy. One of these is Ali, which at over 5000 m is one of the highest astronomical sites in the world. In order to further investigate the astronomical potential of Ali, we have installed a lunar scintillometer, for the primary purpose of profiling atmospheric turbulence. This paper describes the instrument and technique, and reports results from the first year of observations. We find that ground layer (GL) turbulence at Ali is remarkably weak and relatively thin. The median seeing, from turbulence in the range 11- 500 m above ground is 0.34 arcsec, with seeing better than 0.26 arcsec occurring 25 per cent of the time. Under median conditions, half of the GL turbulence lies below a height of 62 m. These initial results, and the high altitude and relatively low temperatures, suggest that Ali could prove to be an outstanding site for ground-based astronomy.

0 citations0 reviewsNo code linkedOpen access