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Yi Yang

Publications and source records attributed to Yi Yang.

At least 631 records · Page 35Linked to original sources
arXiv2023

Is ChatGPT a Financial Expert? Evaluating Language Models on Financial Natural Language Processing

The emergence of Large Language Models (LLMs), such as ChatGPT, has revolutionized general natural language preprocessing (NLP) tasks. However, their expertise in the financial domain lacks a comprehensive evaluation. To assess the ability of LLMs to solve financial NLP tasks, we present FinLMEval, a framework for Financial Language Model Evaluation, comprising nine datasets designed to evaluate the performance of language models. This study compares the performance of encoder-only language models and the decoder-only language models. Our findings reveal that while some decoder-only LLMs demonstrate notable performance across most financial tasks via zero-shot prompting, they generally lag behind the fine-tuned expert models, especially when dealing with proprietary datasets. We hope this study provides foundation evaluations for continuing efforts to build more advanced LLMs in the financial domain.

cs.CL↗
arXiv2023

TacticAI: an AI assistant for football tactics

Identifying key patterns of tactics implemented by rival teams, and developing effective responses, lies at the heart of modern football. However, doing so algorithmically remains an open research challenge. To address this unmet need, we propose TacticAI, an AI football tactics assistant developed and evaluated in close collaboration with domain experts from Liverpool FC. We focus on analysing corner kicks, as they offer coaches the most direct opportunities for interventions and improvements. TacticAI incorporates both a predictive and a generative component, allowing the coaches to effectively sample and explore alternative player setups for each corner kick routine and to select those with the highest predicted likelihood of success. We validate TacticAI on a number of relevant benchmark tasks: predicting receivers and shot attempts and recommending player position adjustments. The utility of TacticAI is validated by a qualitative study conducted with football domain experts at Liverpool FC. We show that TacticAI's model suggestions are not only indistinguishable from real tactics, but also favoured over existing tactics 90% of the time, and that TacticAI offers an effective corner kick retrieval system. TacticAI achieves these results despite the limited availability of gold-standard data, achieving data efficiency through geometric deep learning.

cs.LG↗
arXiv2023

Early-time Spectropolarimetry of the Aspherical Type II Supernova SN 2023ixf

We present six epochs of optical spectropolarimetry of the Type II supernova (SN) 2023ixf ranging from $\sim$ 2 to 15 days after the explosion. Polarimetry was obtained with the Kast double spectrograph on the Shane 3 m telescope at Lick Observatory, representing the earliest such observations ever captured for an SN. We observe a high continuum polarization $p_{\text{cont}} \approx 1$ % on days +1.4 and +2.5 before dropping to 0.5 % on day +3.5, persisting at that level up to day +14.5. Remarkably, this change coincides temporally with the disappearance of highly ionized "flash" features. The decrease of the continuum polarization is accompanied by a $\sim 70^\circ$ rotation of the polarization position angle ($PA$) as seen across the continuum. The early evolution of the polarization may indicate different geometric configurations of the electron-scattering atmosphere as seen before and after the disappearance of the emission lines associated with highly-ionized species (e.g., He II, C IV, N III), which are likely produced by elevated mass loss shortly prior to the SN explosion. We interpret the rapid change of polarization and $PA$ from days +2.5 to +4.5 as the time when the SN ejecta emerge from the dense asymmetric circumstellar material (CSM). The temporal evolution of the continuum polarization and the $PA$ is consistent with an aspherical SN explosion that exhibits a distinct geometry compared to the CSM. The rapid follow-up spectropolarimetry of SN 2023ixf during the shock ionization phase reveals an exceptionally asymmetric mass-loss process leading up to the explosion.

astro-ph.HE↗
arXiv2023

Efficient Emotional Adaptation for Audio-Driven Talking-Head Generation

Audio-driven talking-head synthesis is a popular research topic for virtual human-related applications. However, the inflexibility and inefficiency of existing methods, which necessitate expensive end-to-end training to transfer emotions from guidance videos to talking-head predictions, are significant limitations. In this work, we propose the Emotional Adaptation for Audio-driven Talking-head (EAT) method, which transforms emotion-agnostic talking-head models into emotion-controllable ones in a cost-effective and efficient manner through parameter-efficient adaptations. Our approach utilizes a pretrained emotion-agnostic talking-head transformer and introduces three lightweight adaptations (the Deep Emotional Prompts, Emotional Deformation Network, and Emotional Adaptation Module) from different perspectives to enable precise and realistic emotion controls. Our experiments demonstrate that our approach achieves state-of-the-art performance on widely-used benchmarks, including LRW and MEAD. Additionally, our parameter-efficient adaptations exhibit remarkable generalization ability, even in scenarios where emotional training videos are scarce or nonexistent. Project website: https://yuangan.github.io/eat/

cs.SD↗
arXiv2023

GETAvatar: Generative Textured Meshes for Animatable Human Avatars

We study the problem of 3D-aware full-body human generation, aiming at creating animatable human avatars with high-quality textures and geometries. Generally, two challenges remain in this field: i) existing methods struggle to generate geometries with rich realistic details such as the wrinkles of garments; ii) they typically utilize volumetric radiance fields and neural renderers in the synthesis process, making high-resolution rendering non-trivial. To overcome these problems, we propose GETAvatar, a Generative model that directly generates Explicit Textured 3D meshes for animatable human Avatar, with photo-realistic appearance and fine geometric details. Specifically, we first design an articulated 3D human representation with explicit surface modeling, and enrich the generated humans with realistic surface details by learning from the 2D normal maps of 3D scan data. Second, with the explicit mesh representation, we can use a rasterization-based renderer to perform surface rendering, allowing us to achieve high-resolution image generation efficiently. Extensive experiments demonstrate that GETAvatar achieves state-of-the-art performance on 3D-aware human generation both in appearance and geometry quality. Notably, GETAvatar can generate images at 512x512 resolution with 17FPS and 1024x1024 resolution with 14FPS, improving upon previous methods by 2x. Our code and models will be available.

cs.CV↗
arXiv2023

Probing the low-mass end of core-collapse supernovae using a sample of strongly-stripped Calcium-rich Type IIb Supernovae from the Zwicky Transient Facility

The fate of stars in the zero-age main-sequence (ZAMS) range $\approx 8-12$ Msun is unclear. They could evolve to form white dwarfs or explode as electron-capture supernovae (SNe) or iron core-collapse SNe (CCSNe). Even though the initial mass function indicates that this mass range should account for over 40% of all CCSNe progenitors, few have been observationally confirmed, likely owing to the faintness and rapid evolution of these transients. In this paper, we present a sample of nine Ca-rich/O-poor Type IIb SNe detected by the Zwicky Transient Facility with progenitors likely in this mass range. We perform a holistic analysis of the spectroscopic and photometric properties of the sample. These sources have a flux ratio of [Ca II] $λλ$7291, 7324 to [O I] $λλ$6300, 6364 of $\gtrsim$ 2 in their nebular spectra. Comparing the measured [O I] luminosity ($\lesssim 10^{39} \mathrm{erg\ s^{-1}}$) and derived oxygen mass ($\lesssim 0.1$ Msun) with theoretical models, we infer that the progenitor ZAMS mass for these explosions is less than 12 Msun. These correspond to He-stars with core masses less than around 3 Msun. We find that the ejecta properties (Mej $\lesssim 1$ Msun) are also consistent with those expected for such low mass He-stars. The low ejecta mass of these sources indicates a class of strongly-stripped SNe that is a transition between the regular stripped-envelope SNe and ultra-stripped SNe. The progenitor could be stripped by a main sequence companion and result in the formation of a neutron star $-$ main sequence binary. Such binaries have been suggested to be progenitors of neutron star $-$ white dwarf systems that could merge within a Hubble time, and be detectable with LISA.

astro-ph.HE↗
arXiv2023

Evidence of weak circumstellar medium interaction in the Type II SN 2023axu

We present high-cadence photometric and spectroscopic observations of SN~2023axu, a classical Type II supernova with an absolute $V$-band peak magnitude of $-16.5 \pm 0.1$ mag. SN~2023axu was discovered by the Distance Less Than 40 Mpc (DLT40) survey within 1 day of the last non-detection in the nearby galaxy NGC 2283 at 13.7 Mpc. We modeled the early light curve using a recently updated shock cooling model that includes the effects of line blanketing and found the explosion epoch to be MJD 59971.48 $\pm$ 0.03 and the probable progenitor to be a red supergiant with a radius of 417 $\pm$ 28 $R_\odot$. The shock cooling model cannot match the rise of observed data in the $r$ and $i$ bands and underpredicts the overall UV data which points to possible interaction with circumstellar material. This interpretation is further supported by spectral behavior. We see a ledge feature around 4600 Å in the very early spectra (+1.1 and +1.5 days after the explosion) which can be a sign of circumstellar interaction. The signs of circumstellar material are further bolstered by the presence of absorption features blueward of H$α$ and H$β$ at day $>$40 which is also generally attributed to circumstellar interaction. Our analysis shows the need for high-cadence early photometric and spectroscopic data to decipher the mass-loss history of the progenitor.

astro-ph.HE↗
arXiv2023

LOGICSEG: Parsing Visual Semantics with Neural Logic Learning and Reasoning

Current high-performance semantic segmentation models are purely data-driven sub-symbolic approaches and blind to the structured nature of the visual world. This is in stark contrast to human cognition which abstracts visual perceptions at multiple levels and conducts symbolic reasoning with such structured abstraction. To fill these fundamental gaps, we devise LOGICSEG, a holistic visual semantic parser that integrates neural inductive learning and logic reasoning with both rich data and symbolic knowledge. In particular, the semantic concepts of interest are structured as a hierarchy, from which a set of constraints are derived for describing the symbolic relations and formalized as first-order logic rules. After fuzzy logic-based continuous relaxation, logical formulae are grounded onto data and neural computational graphs, hence enabling logic-induced network training. During inference, logical constraints are packaged into an iterative process and injected into the network in a form of several matrix multiplications, so as to achieve hierarchy-coherent prediction with logic reasoning. These designs together make LOGICSEG a general and compact neural-logic machine that is readily integrated into existing segmentation models. Extensive experiments over four datasets with various segmentation models and backbones verify the effectiveness and generality of LOGICSEG. We believe this study opens a new avenue for visual semantic parsing.

cs.CV↗
arXiv2023

ReLER@ZJU Submission to the Ego4D Moment Queries Challenge 2022

In this report, we present the ReLER@ZJU1 submission to the Ego4D Moment Queries Challenge in ECCV 2022. In this task, the goal is to retrieve and localize all instances of possible activities in egocentric videos. Ego4D dataset is challenging for the temporal action localization task as the temporal duration of the videos is quite long and each video contains multiple action instances with fine-grained action classes. To address these problems, we utilize a multi-scale transformer to classify different action categories and predict the boundary of each instance. Moreover, in order to better capture the long-term temporal dependencies in the long videos, we propose a segment-level recurrence mechanism. Compared with directly feeding all video features to the transformer encoder, the proposed segment-level recurrence mechanism alleviates the optimization difficulties and achieves better performance. The final submission achieved Recall@1,tIoU=0.5 score of 37.24, average mAP score of 17.67 and took 3-rd place on the leaderboard.

cs.CV↗
arXiv2023

Action Sensitivity Learning for the Ego4D Episodic Memory Challenge 2023

This report presents ReLER submission to two tracks in the Ego4D Episodic Memory Benchmark in CVPR 2023, including Natural Language Queries and Moment Queries. This solution inherits from our proposed Action Sensitivity Learning framework (ASL) to better capture discrepant information of frames. Further, we incorporate a series of stronger video features and fusion strategies. Our method achieves an average mAP of 29.34, ranking 1st in Moment Queries Challenge, and garners 19.79 mean R1, ranking 2nd in Natural Language Queries Challenge. Our code will be released.

cs.CV↗
arXiv2023

Design of laser uniform illumination system based on aspheric lens and compound ellipsoidal cavity

In order to achieve uniform laser illumination with small aperture diameter and large field Angle,study laser active illumination system.An aspheric mirror combined with a composite ellipsoidal cavity is designed to achieve uniform illumination in this paper.Through an aspheric mirror,the fundamental mode of Gaussian beam is shaped into double Gaussian radiation and Flat-top radiation.The double Gaussian radiation rays are reflected again by the complex ellipsoidal cavity and decomposed into equal radiation flux,which is superimposed with the through Flat-top radiation rays to form a uniform distribution.The parameters of the complex ellipsoidal cavity are obtained by mapping equalization algorithm.After the superposition of the aspherical transmission Flat-top shaping and the composite ellipsoidal cavity secondary reflection shaping,the aperture is 29.7mm,whose aperture angle is 84.0 degrees,and the uniformity is 92.7% with 2m distance and 3.6m diameter.The optimization of uniformity is influenced by three factors:RMS,transmission and reflection power density ratio MT/R and transmission and reflection overlap degree.RMS and MT/R determine the design effect of the composite ellipsoidal cavity, which depends on the maximum reflection Angle and transmission Angle.MT/R is negatively correlated with the maximum reflection of Angle,and RMS is positively correlated with the transmission Angle.When the maximum reflection Angle is set to 32.0 degrees and the transmission Angle to 8.0 degrees,the minimum root-mean-square focusing radius is 108.6um,and the minimum effective transmission reflection power density ratio is 1.07.The degree overlap of transmission and reflection directly affects the uniformity of the target plane.The degree of transmission and reflection is adjusted by setting an adjustment factor.When the adjustment factor is 0.9,the uniformity of the target plane reaches the maximum.

physics.optics↗
arXiv2023

SN 2022oqm -- a Ca-rich explosion of a compact progenitor embedded in C/O circumstellar material

We present the discovery and analysis of SN\,2022oqm, a Type Ic supernova (SN) detected $<1$\,day after explosion. The SN rises to a blue and short-lived (2\,days) initial peak. Early-time spectral observations of SN\,2022oqm show a hot (40,000\,K) continuum with high-ionization C and O absorption features at velocities of 4000\,km\,s$^{-1}$, while its photospheric radius expands at 20,000\,\kms, indicating a pre-existing distribution of expanding C/O material. After $\sim2.5$\,days, both the spectrum and light curves evolve into those of a typical SN Ic, with line velocities of $\sim10,000$\,km\,s$^{-1}$, in agreement with the photospheric radius evolution. The optical light curves reach a second peak at $t\approx15$\,days. By $t=60$\,days, the spectrum of \oqm\ becomes nearly nebular, displaying strong \ion{Ca}{2} and [\ion{Ca}{2}] emission with no detectable [\ion{O}{1}], marking this event as Ca-rich. The early behavior can be explained by $10^{-3}$\,\msun\ of optically thin circumstellar material (CSM) surrounding either (1) a massive compact progenitor such as a Wolf-Rayet star, (2) a massive stripped progenitor with an extended envelope, or (3) a binary system with a white dwarf. We propose that the early-time light curve is powered by both interaction of the ejecta with the optically thin CSM and shock cooling (in the massive-star scenario). The observations can be explained by CSM that is optically thick to X-ray photons, is optically thick in the lines as seen in the spectra, and is optically thin to visible-light continuum photons that come either from downscattered X-rays or from the shock-heated ejecta. Calculations show that this scenario is self-consistent.

astro-ph.HE↗
arXiv2023

Integrating Boxes and Masks: A Multi-Object Framework for Unified Visual Tracking and Segmentation

Tracking any given object(s) spatially and temporally is a common purpose in Visual Object Tracking (VOT) and Video Object Segmentation (VOS). Joint tracking and segmentation have been attempted in some studies but they often lack full compatibility of both box and mask in initialization and prediction, and mainly focus on single-object scenarios. To address these limitations, this paper proposes a Multi-object Mask-box Integrated framework for unified Tracking and Segmentation, dubbed MITS. Firstly, the unified identification module is proposed to support both box and mask reference for initialization, where detailed object information is inferred from boxes or directly retained from masks. Additionally, a novel pinpoint box predictor is proposed for accurate multi-object box prediction, facilitating target-oriented representation learning. All target objects are processed simultaneously from encoding to propagation and decoding, as a unified pipeline for VOT and VOS. Experimental results show MITS achieves state-of-the-art performance on both VOT and VOS benchmarks. Notably, MITS surpasses the best prior VOT competitor by around 6% on the GOT-10k test set, and significantly improves the performance of box initialization on VOS benchmarks. The code is available at https://github.com/yoxu515/MITS.

cs.CV↗
arXiv2023

CATR: Combinatorial-Dependence Audio-Queried Transformer for Audio-Visual Video Segmentation

Audio-visual video segmentation~(AVVS) aims to generate pixel-level maps of sound-producing objects within image frames and ensure the maps faithfully adhere to the given audio, such as identifying and segmenting a singing person in a video. However, existing methods exhibit two limitations: 1) they address video temporal features and audio-visual interactive features separately, disregarding the inherent spatial-temporal dependence of combined audio and video, and 2) they inadequately introduce audio constraints and object-level information during the decoding stage, resulting in segmentation outcomes that fail to comply with audio directives. To tackle these issues, we propose a decoupled audio-video transformer that combines audio and video features from their respective temporal and spatial dimensions, capturing their combined dependence. To optimize memory consumption, we design a block, which, when stacked, enables capturing audio-visual fine-grained combinatorial-dependence in a memory-efficient manner. Additionally, we introduce audio-constrained queries during the decoding phase. These queries contain rich object-level information, ensuring the decoded mask adheres to the sounds. Experimental results confirm our approach's effectiveness, with our framework achieving a new SOTA performance on all three datasets using two backbones. The code is available at \url{https://github.com/aspirinone/CATR.github.io}

cs.CV↗
arXiv2023

RMP: A Random Mask Pretrain Framework for Motion Prediction

As the pretraining technique is growing in popularity, little work has been done on pretrained learning-based motion prediction methods in autonomous driving. In this paper, we propose a framework to formalize the pretraining task for trajectory prediction of traffic participants. Within our framework, inspired by the random masked model in natural language processing (NLP) and computer vision (CV), objects' positions at random timesteps are masked and then filled in by the learned neural network (NN). By changing the mask profile, our framework can easily switch among a range of motion-related tasks. We show that our proposed pretraining framework is able to deal with noisy inputs and improves the motion prediction accuracy and miss rate, especially for objects occluded over time by evaluating it on Argoverse and NuScenes datasets.

cs.RO↗
arXiv2023

InvestLM: A Large Language Model for Investment using Financial Domain Instruction Tuning

We present a new financial domain large language model, InvestLM, tuned on LLaMA-65B (Touvron et al., 2023), using a carefully curated instruction dataset related to financial investment. Inspired by less-is-more-for-alignment (Zhou et al., 2023), we manually curate a small yet diverse instruction dataset, covering a wide range of financial related topics, from Chartered Financial Analyst (CFA) exam questions to SEC filings to Stackexchange quantitative finance discussions. InvestLM shows strong capabilities in understanding financial text and provides helpful responses to investment related questions. Financial experts, including hedge fund managers and research analysts, rate InvestLM's response as comparable to those of state-of-the-art commercial models (GPT-3.5, GPT-4 and Claude-2). Zero-shot evaluation on a set of financial NLP benchmarks demonstrates strong generalizability. From a research perspective, this work suggests that a high-quality domain specific LLM can be tuned using a small set of carefully curated instructions on a well-trained foundation model, which is consistent with the Superficial Alignment Hypothesis (Zhou et al., 2023). From a practical perspective, this work develops a state-of-the-art financial domain LLM with superior capability in understanding financial texts and providing helpful investment advice, potentially enhancing the work efficiency of financial professionals. We release the model parameters to the research community.

q-fin.GN↗
arXiv2023

AvatarFusion: Zero-shot Generation of Clothing-Decoupled 3D Avatars Using 2D Diffusion

Large-scale pre-trained vision-language models allow for the zero-shot text-based generation of 3D avatars. The previous state-of-the-art method utilized CLIP to supervise neural implicit models that reconstructed a human body mesh. However, this approach has two limitations. Firstly, the lack of avatar-specific models can cause facial distortion and unrealistic clothing in the generated avatars. Secondly, CLIP only provides optimization direction for the overall appearance, resulting in less impressive results. To address these limitations, we propose AvatarFusion, the first framework to use a latent diffusion model to provide pixel-level guidance for generating human-realistic avatars while simultaneously segmenting clothing from the avatar's body. AvatarFusion includes the first clothing-decoupled neural implicit avatar model that employs a novel Dual Volume Rendering strategy to render the decoupled skin and clothing sub-models in one space. We also introduce a novel optimization method, called Pixel-Semantics Difference-Sampling (PS-DS), which semantically separates the generation of body and clothes, and generates a variety of clothing styles. Moreover, we establish the first benchmark for zero-shot text-to-avatar generation. Our experimental results demonstrate that our framework outperforms previous approaches, with significant improvements observed in all metrics. Additionally, since our model is clothing-decoupled, we can exchange the clothes of avatars. Code are available on our project page https://hansenhuang0823.github.io/AvatarFusion.

cs.CV↗
arXiv2023

Action Sensitivity Learning for Temporal Action Localization

Temporal action localization (TAL), which involves recognizing and locating action instances, is a challenging task in video understanding. Most existing approaches directly predict action classes and regress offsets to boundaries, while overlooking the discrepant importance of each frame. In this paper, we propose an Action Sensitivity Learning framework (ASL) to tackle this task, which aims to assess the value of each frame and then leverage the generated action sensitivity to recalibrate the training procedure. We first introduce a lightweight Action Sensitivity Evaluator to learn the action sensitivity at the class level and instance level, respectively. The outputs of the two branches are combined to reweight the gradient of the two sub-tasks. Moreover, based on the action sensitivity of each frame, we design an Action Sensitive Contrastive Loss to enhance features, where the action-aware frames are sampled as positive pairs to push away the action-irrelevant frames. The extensive studies on various action localization benchmarks (i.e., MultiThumos, Charades, Ego4D-Moment Queries v1.0, Epic-Kitchens 100, Thumos14 and ActivityNet1.3) show that ASL surpasses the state-of-the-art in terms of average-mAP under multiple types of scenarios, e.g., single-labeled, densely-labeled and egocentric.

cs.CV↗