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Yi Yang

Publications and source records attributed to Yi Yang.

At least 685 records · Page 38Linked to original sources
arXiv2023

Segment and Track Anything

This report presents a framework called Segment And Track Anything (SAMTrack) that allows users to precisely and effectively segment and track any object in a video. Additionally, SAM-Track employs multimodal interaction methods that enable users to select multiple objects in videos for tracking, corresponding to their specific requirements. These interaction methods comprise click, stroke, and text, each possessing unique benefits and capable of being employed in combination. As a result, SAM-Track can be used across an array of fields, ranging from drone technology, autonomous driving, medical imaging, augmented reality, to biological analysis. SAM-Track amalgamates Segment Anything Model (SAM), an interactive key-frame segmentation model, with our proposed AOT-based tracking model (DeAOT), which secured 1st place in four tracks of the VOT 2022 challenge, to facilitate object tracking in video. In addition, SAM-Track incorporates Grounding-DINO, which enables the framework to support text-based interaction. We have demonstrated the remarkable capabilities of SAM-Track on DAVIS-2016 Val (92.0%), DAVIS-2017 Test (79.2%)and its practicability in diverse applications. The project page is available at: https://github.com/z-x-yang/Segment-and-Track-Anything.

cs.CV↗
arXiv2023

Towards Medical Artificial General Intelligence via Knowledge-Enhanced Multimodal Pretraining

Medical artificial general intelligence (MAGI) enables one foundation model to solve different medical tasks, which is very practical in the medical domain. It can significantly reduce the requirement of large amounts of task-specific data by sufficiently sharing medical knowledge among different tasks. However, due to the challenges of designing strongly generalizable models with limited and complex medical data, most existing approaches tend to develop task-specific models. To take a step towards MAGI, we propose a new paradigm called Medical-knOwledge-enhanced mulTimOdal pretRaining (MOTOR). In MOTOR, we combine two kinds of basic medical knowledge, i.e., general and specific knowledge, in a complementary manner to boost the general pretraining process. As a result, the foundation model with comprehensive basic knowledge can learn compact representations from pretraining radiographic data for better cross-modal alignment. MOTOR unifies the understanding and generation, which are two kinds of core intelligence of an AI system, into a single medical foundation model, to flexibly handle more diverse medical tasks. To enable a comprehensive evaluation and facilitate further research, we construct a medical multimodal benchmark including a wide range of downstream tasks, such as chest x-ray report generation and medical visual question answering. Extensive experiments on our benchmark show that MOTOR obtains promising results through simple task-oriented adaptation. The visualization shows that the injected knowledge successfully highlights key information in the medical data, demonstrating the excellent interpretability of MOTOR. Our MOTOR successfully mimics the human practice of fulfilling a "medical student" to accelerate the process of becoming a "specialist". We believe that our work makes a significant stride in realizing MAGI.

cs.AI↗
arXiv2023

SN 2020udy: a SN Iax with strict limits on interaction consistent with a helium-star companion

Early observations of transient explosions can provide vital clues to their progenitor origins. In this paper we present the nearby Type Iax (02cx-like) supernova (SN), SN 2020udy that was discovered within hours ($\sim$7 hr) of estimated first light. An extensive dataset of ultra-violet, optical, and near-infrared observations was obtained, covering out to $\sim$150 d after explosion. SN 2020udy peaked at -17.86$\pm$0.43 mag in the r band and evolved similarly to other 'luminous' SNe Iax, such as SNe 2005hk and 2012Z. Its well-sampled early light curve allows strict limits on companion interaction to be placed. Main-sequence companion stars with masses of 2 and 6 M$_\odot$ are ruled out at all viewing angles, while a helium-star companion is allowed from a narrow range of angles (140-180$^\circ$ away from the companion). The spectra and light curves of SN2020udy are in good agreement with those of the 'N5def' deflagration model of a near Chandrasekhar-mass carbon-oxygen white dwarf. However, as has been seen in previous studies of similar luminosity events, SN 2020udy evolves slower than the model. Broad-band linear polarisation measurements taken at and after peak are consistent with no polarisation, in agreement with the predictions of the companion-star configuration from the early light curve measurements. The host galaxy environment is low metallicity and is consistent with a young stellar population. Overall, we find the most plausible explosion scenario to be the incomplete disruption of a CO white dwarf near the Chandrasekhar-mass limit, with a helium-star companion.

astro-ph.HE↗
arXiv2023

DETR with Additional Global Aggregation for Cross-domain Weakly Supervised Object Detection

This paper presents a DETR-based method for cross-domain weakly supervised object detection (CDWSOD), aiming at adapting the detector from source to target domain through weak supervision. We think DETR has strong potential for CDWSOD due to an insight: the encoder and the decoder in DETR are both based on the attention mechanism and are thus capable of aggregating semantics across the entire image. The aggregation results, i.e., image-level predictions, can naturally exploit the weak supervision for domain alignment. Such motivated, we propose DETR with additional Global Aggregation (DETR-GA), a CDWSOD detector that simultaneously makes "instance-level + image-level" predictions and utilizes "strong + weak" supervisions. The key point of DETR-GA is very simple: for the encoder / decoder, we respectively add multiple class queries / a foreground query to aggregate the semantics into image-level predictions. Our query-based aggregation has two advantages. First, in the encoder, the weakly-supervised class queries are capable of roughly locating the corresponding positions and excluding the distraction from non-relevant regions. Second, through our design, the object queries and the foreground query in the decoder share consensus on the class semantics, therefore making the strong and weak supervision mutually benefit each other for domain alignment. Extensive experiments on four popular cross-domain benchmarks show that DETR-GA significantly improves CSWSOD and advances the states of the art (e.g., 29.0% --> 79.4% mAP on PASCAL VOC --> Clipart_all dataset).

cs.CV↗
arXiv2023

Early-Time Ultraviolet and Optical Hubble Space Telescope Spectroscopy of the Type II Supernova 2022wsp

We report early-time ultraviolet (UV) and optical spectroscopy of the young, nearby Type II supernova (SN) 2022wsp obtained by the Hubble Space Telescope (HST)/STIS at about 10 and 20 days after the explosion. The SN 2022wsp UV spectra are compared to those of other well-observed Type II/IIP SNe, including the recently studied Type IIP SN 2021yja. Both SNe exhibit rapid cooling and similar evolution during early phases, indicating a common behavior among SNe II. Radiative-transfer modeling of the spectra of SN 2022wsp with the TARDIS code indicates a steep radial density profile in the outer layer of the ejecta, a supersolar metallicity, and a relatively high total extinction of E(B-V) = 0.35 mag. The early-time evolution of the photospheric velocity and temperature derived from the modeling agree with the behavior observed from other previously studied cases. The strong suppression of hydrogen Balmer lines in the spectra suggests interaction with a pre-existing circumstellar environment could be occurring at early times. In the SN 2022wsp spectra, the absorption component of the Mg II P Cygni profile displays a double-trough feature on day +10 that disappears by day +20. The shape is well reproduced by the model without fine-tuning the parameters, suggesting that the secondary blueward dip is a metal transition that originates in the SN ejecta.

astro-ph.HE↗
arXiv2023

Explanation Strategies for Image Classification in Humans vs. Current Explainable AI

Explainable AI (XAI) methods provide explanations of AI models, but our understanding of how they compare with human explanations remains limited. In image classification, we found that humans adopted more explorative attention strategies for explanation than the classification task itself. Two representative explanation strategies were identified through clustering: One involved focused visual scanning on foreground objects with more conceptual explanations diagnostic for inferring class labels, whereas the other involved explorative scanning with more visual explanations rated higher for effectiveness. Interestingly, XAI saliency-map explanations had the highest similarity to the explorative attention strategy in humans, and explanations highlighting discriminative features from invoking observable causality through perturbation had higher similarity to human strategies than those highlighting internal features associated with higher class score. Thus, humans differ in information and strategy use for explanations, and XAI methods that highlight features informing observable causality match better with human explanations, potentially more accessible to users.

cs.HC↗
arXiv2023

Stringy scaling of n-point hard string scattering amplitudes

Motivated by the recent calculation of the SL(K+3,C) symmetry of n-point Lauricella string scattering amplitudes (SSA) of open bosonic string theory, we calculate ratios of the solvable infinite linear relations of arbitrary n-point hard SSA (HSSA). We discover a general stringy scaling behavior for all n-point HSSA to all string loop orders. For the special case of n=4, the stringy scaling behavior reduces to the infinite linear relations and constant ratios among HSSA conjectured by Gross [8] and later corrected and calculated by the method of decoupling of zero-norm states [11].

hep-th↗
arXiv2023

GIF: A General Graph Unlearning Strategy via Influence Function

With the greater emphasis on privacy and security in our society, the problem of graph unlearning -- revoking the influence of specific data on the trained GNN model, is drawing increasing attention. However, ranging from machine unlearning to recently emerged graph unlearning methods, existing efforts either resort to retraining paradigm, or perform approximate erasure that fails to consider the inter-dependency between connected neighbors or imposes constraints on GNN structure, therefore hard to achieve satisfying performance-complexity trade-offs. In this work, we explore the influence function tailored for graph unlearning, so as to improve the unlearning efficacy and efficiency for graph unlearning. We first present a unified problem formulation of diverse graph unlearning tasks \wrt node, edge, and feature. Then, we recognize the crux to the inability of traditional influence function for graph unlearning, and devise Graph Influence Function (GIF), a model-agnostic unlearning method that can efficiently and accurately estimate parameter changes in response to a $ε$-mass perturbation in deleted data. The idea is to supplement the objective of the traditional influence function with an additional loss term of the influenced neighbors due to the structural dependency. Further deductions on the closed-form solution of parameter changes provide a better understanding of the unlearning mechanism. We conduct extensive experiments on four representative GNN models and three benchmark datasets to justify the superiority of GIF for diverse graph unlearning tasks in terms of unlearning efficacy, model utility, and unlearning efficiency. Our implementations are available at \url{https://github.com/wujcan/GIF-torch/}.

cs.LG↗
arXiv2023

Synthetic non-Abelian gauge fields for non-Hermitian systems

Non-Abelian gauge fields are versatile tools for synthesizing topological phenomena but have so far been mostly studied in Hermitian systems, where gauge flux has to be defined from a closed loop in order for gauge fields, whether Abelian or non-Abelian, to become physically meaningful. We show that this condition can be relaxed in non-Hermitian systems by proposing and studying a generalized Hatano--Nelson model with imbalanced non-Abelian hopping. Despite lacking gauge flux in one dimension, non-Abelian gauge fields create rich non-Hermitian topological consequences. Under only nearest-neighbor coupling, non-Abelian gauge fields enable Hopf-link bulk braiding topology, whose phase transition accompanies the emergence of exceptional points (EPs). At both ends of an open chain, non-Abelian gauge fields lead to the simultaneous presence of non-Hermitian skin modes, whose population can be effectively tuned. Asymptotic analysis shows that this tuning mechanism stems from the interplay between the Abelian Hatano--Nelson coupling and effective high-order hopping, which becomes substantial near the EP phase transition condition. The predicted non-Hermitian phenomena, enabled by non-Abelian gauge fields, could be realized in synthetic dimensional optical platforms such as time-multiplexed photonic mesh lattices and driven ring resonators.

physics.optics↗
arXiv2023

The $SL(K+3,\mathbb{C})$ symmetry of string scatterings from D-branes

By using the solvability of Lauricella function $F_{D}^{(K)}\left( α;β_{1},...,β_{K};γ;x_{1},...,x_{K}\right) $ with nonpositive integer $β_{J}$, we show that each scattering or decay process of string and D-brane states at \textit{arbitrary} mass levels can be expressed in terms of a single Lauricella function. This result extends the previous exact $SL(K+3,\mathbb{C})$ symmetry of tree-level open bosonic string theory to include the D-brane.

hep-th↗
arXiv2023

Minute-Cadence Observations of the LAMOST Fields with the TMTS: II. Catalogues of Short-Period Variable Stars from the First Two-Year Surveys

Over the past few years, wide-field time-domain surveys like ZTF and OGLE have led to discoveries of various types of interesting short-period stellar variables, such as ultracompact eclipsing binary white dwarfs, rapidly rotating magnetised white dwarfs (WDs), transitional cataclysmic variables between hydrogen-rich and helium accretion, and blue large-amplitude pulsators (BLAPs), which greatly enrich our understandings of stellar physics under some extreme conditions. In this paper, we report the first-two-year discoveries of short-period variables (i.e., P<2 hr) by the Tsinghua University-Ma Huateng Telescopes for Survey (TMTS). TMTS is a multi-tube telescope system with a field of view up to 18 deg^2, which started to monitor the LAMOST sky areas since 2020 and generated uninterrupted minute-cadence light curves for about ten million sources within 2 years. Adopting the Lomb-Scargle periodogram with period-dependent thresholds for the maximum powers, we identify over 1 100 sources that exhibit a variation period shorter than 2 hr. Compiling the light curves with the Gaia magnitudes and colours, LAMOST spectral parameters, VSX classifications, and archived observations from other prevailing time-domain survey missions, we identified 1 076 as delta Scuti stars, which allows us study their populations and physical properties in the short-period regime. The other 31 sources include BLAPs, subdwarf B variables (sdBVs), pulsating WDs, ultracompact/short-period eclipsing/ellipsoidal binaries, cataclysmic variables below the period gap, etc., which are highly interesting and worthy of follow-up investigations.

astro-ph.SR↗
arXiv2023

TAP-Vid: A Benchmark for Tracking Any Point in a Video

Generic motion understanding from video involves not only tracking objects, but also perceiving how their surfaces deform and move. This information is useful to make inferences about 3D shape, physical properties and object interactions. While the problem of tracking arbitrary physical points on surfaces over longer video clips has received some attention, no dataset or benchmark for evaluation existed, until now. In this paper, we first formalize the problem, naming it tracking any point (TAP). We introduce a companion benchmark, TAP-Vid, which is composed of both real-world videos with accurate human annotations of point tracks, and synthetic videos with perfect ground-truth point tracks. Central to the construction of our benchmark is a novel semi-automatic crowdsourced pipeline which uses optical flow estimates to compensate for easier, short-term motion like camera shake, allowing annotators to focus on harder sections of video. We validate our pipeline on synthetic data and propose a simple end-to-end point tracking model TAP-Net, showing that it outperforms all prior methods on our benchmark when trained on synthetic data.

cs.CV↗
arXiv2023

Joint Video Multi-Frame Interpolation and Deblurring under Unknown Exposure Time

Natural videos captured by consumer cameras often suffer from low framerate and motion blur due to the combination of dynamic scene complexity, lens and sensor imperfection, and less than ideal exposure setting. As a result, computational methods that jointly perform video frame interpolation and deblurring begin to emerge with the unrealistic assumption that the exposure time is known and fixed. In this work, we aim ambitiously for a more realistic and challenging task - joint video multi-frame interpolation and deblurring under unknown exposure time. Toward this goal, we first adopt a variant of supervised contrastive learning to construct an exposure-aware representation from input blurred frames. We then train two U-Nets for intra-motion and inter-motion analysis, respectively, adapting to the learned exposure representation via gain tuning. We finally build our video reconstruction network upon the exposure and motion representation by progressive exposure-adaptive convolution and motion refinement. Extensive experiments on both simulated and real-world datasets show that our optimized method achieves notable performance gains over the state-of-the-art on the joint video x8 interpolation and deblurring task. Moreover, on the seemingly implausible x16 interpolation task, our method outperforms existing methods by more than 1.5 dB in terms of PSNR.

cs.CV↗
arXiv2023

CRRS: Concentric Rectangles Regression Strategy for Multi-point Representation on Fisheye Images

Modern object detectors take advantage of rectangular bounding boxes as a conventional way to represent objects. When it comes to fisheye images, rectangular boxes involve more background noise rather than semantic information. Although multi-point representation has been proposed, both the regression accuracy and convergence still perform inferior to the widely used rectangular boxes. In order to further exploit the advantages of multi-point representation for distorted images, Concentric Rectangles Regression Strategy(CRRS) is proposed in this work. We adopt smoother mean loss to allocate weights and discuss the effect of hyper-parameter to prediction results. Moreover, an accurate pixel-level method is designed to obtain irregular IoU for estimating detector performance. Compared with the previous work for muti-point representation, the experiments show that CRRS can improve the training performance both in accurate and stability. We also prove that multi-task weighting strategy facilitates regression process in this design.

cs.CV↗
arXiv2023

Sector Patch Embedding: An Embedding Module Conforming to The Distortion Pattern of Fisheye Image

Fisheye cameras suffer from image distortion while having a large field of view(LFOV). And this fact leads to poor performance on some fisheye vision tasks. One of the solutions is to optimize the current vision algorithm for fisheye images. However, most of the CNN-based methods and the Transformer-based methods lack the capability of leveraging distortion information efficiently. In this work, we propose a novel patch embedding method called Sector Patch Embedding(SPE), conforming to the distortion pattern of the fisheye image. Furthermore, we put forward a synthetic fisheye dataset based on the ImageNet-1K and explore the performance of several Transformer models on the dataset. The classification top-1 accuracy of ViT and PVT is improved by 0.75% and 2.8% with SPE respectively. The experiments show that the proposed sector patch embedding method can better perceive distortion and extract features on the fisheye images. Our method can be easily adopted to other Transformer-based models. Source code is at https://github.com/IN2-ViAUn/Sector-Patch-Embedding.

cs.CV↗
arXiv2023

Global-to-Local Modeling for Video-based 3D Human Pose and Shape Estimation

Video-based 3D human pose and shape estimations are evaluated by intra-frame accuracy and inter-frame smoothness. Although these two metrics are responsible for different ranges of temporal consistency, existing state-of-the-art methods treat them as a unified problem and use monotonous modeling structures (e.g., RNN or attention-based block) to design their networks. However, using a single kind of modeling structure is difficult to balance the learning of short-term and long-term temporal correlations, and may bias the network to one of them, leading to undesirable predictions like global location shift, temporal inconsistency, and insufficient local details. To solve these problems, we propose to structurally decouple the modeling of long-term and short-term correlations in an end-to-end framework, Global-to-Local Transformer (GLoT). First, a global transformer is introduced with a Masked Pose and Shape Estimation strategy for long-term modeling. The strategy stimulates the global transformer to learn more inter-frame correlations by randomly masking the features of several frames. Second, a local transformer is responsible for exploiting local details on the human mesh and interacting with the global transformer by leveraging cross-attention. Moreover, a Hierarchical Spatial Correlation Regressor is further introduced to refine intra-frame estimations by decoupled global-local representation and implicit kinematic constraints. Our GLoT surpasses previous state-of-the-art methods with the lowest model parameters on popular benchmarks, i.e., 3DPW, MPI-INF-3DHP, and Human3.6M. Codes are available at https://github.com/sxl142/GLoT.

cs.CV↗
arXiv2023

Bidirectional Cross-Modal Knowledge Exploration for Video Recognition with Pre-trained Vision-Language Models

Vision-language models (VLMs) pre-trained on large-scale image-text pairs have demonstrated impressive transferability on various visual tasks. Transferring knowledge from such powerful VLMs is a promising direction for building effective video recognition models. However, current exploration in this field is still limited. We believe that the greatest value of pre-trained VLMs lies in building a bridge between visual and textual domains. In this paper, we propose a novel framework called BIKE, which utilizes the cross-modal bridge to explore bidirectional knowledge: i) We introduce the Video Attribute Association mechanism, which leverages the Video-to-Text knowledge to generate textual auxiliary attributes for complementing video recognition. ii) We also present a Temporal Concept Spotting mechanism that uses the Text-to-Video expertise to capture temporal saliency in a parameter-free manner, leading to enhanced video representation. Extensive studies on six popular video datasets, including Kinetics-400 & 600, UCF-101, HMDB-51, ActivityNet and Charades, show that our method achieves state-of-the-art performance in various recognition scenarios, such as general, zero-shot, and few-shot video recognition. Our best model achieves a state-of-the-art accuracy of 88.6% on the challenging Kinetics-400 using the released CLIP model. The code is available at https://github.com/whwu95/BIKE .

cs.CV↗