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Dong Guo

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5 papers

2 author rows

ICRA Conference 2025 Conference Paper

A Highly Robust Contact Sensor for Precise Contact Detection of Fabric

Zhengrong Ling
Lanxuan Hong
Xiong Yang
Yifeng Tang
Dong Guo
Yajing Shen

Automation in the apparel and textile industry has long been a pursuit. However, accurately locating the surface of a fabric remains a challenge, limiting the automation in sorting, packaging, and other processes. When humans locate clothing, they rely on contact feedback for the exact position of the clothing surface. As existing contact detection solutions are significantly affected by environmental factors, it is essential to develop a sensor with robust contact detection capabilities. In this work, we introduce a contact sensor with high robustness and high force resolution. This contact sensor detects contact by measuring the deformation of an elastomer using a distancemeasuring module. Based on the deformation characteristics of the elastomer, we designed a detection algorithm that not only reduces the noise of data but also extracts features such as trends and elastomer states, enabling reliable contact detection. Through experiments, we validated that this contact sensor can detect contact forces as low as 0. 017 N and is robust to external interference or sensor movement. We also verified that the sensor can process data within 7. 5 ms and return contact detection with 95% accuracy. Additionally, we assessed its effectiveness in real fabric contact scenarios.

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TMLR Journal 2025 Journal Article

LLaVA-OneVision: Easy Visual Task Transfer

Bo Li
Yuanhan Zhang
Dong Guo
Renrui Zhang
Feng Li
Hao Zhang
Kaichen Zhang
Peiyuan Zhang

We present LLaVA-OneVision, a family of open large multimodal models (LMMs) developed by consolidating our insights into data, models, and visual representations in the LLaVA-NeXT blog series. Our experimental results demonstrate that LLaVA-OneVision is the first single model that can simultaneously push the performance boundaries of open LMMs in three important computer vision scenarios: single-image, multi-image, and video scenarios. Importantly, the design of LLaVA-OneVision allows strong transfer learning across different modalities/scenarios, yielding new emerging capabilities. In particular, strong video understanding and cross-scenario capabilities are demonstrated through task transfer from images to videos.

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IJCAI Conference 2024 Conference Paper

InstructME: An Instruction Guided Music Edit Framework with Latent Diffusion Models

Bing Han
Junyu Dai
Weituo Hao
Xinyan He
Dong Guo
Jitong Chen
Yuxuan Wang
Yanmin Qian

Music editing primarily entails the modification of instrument tracks or remixing in the whole, which offers a novel reinterpretation of the original piece through a series of operations. These music processing methods hold immense potential across various applications but demand substantial expertise. Prior methodologies, although effective for image and audio modifications, falter when directly applied to music. This is attributed to music's distinctive data nature, where such methods can inadvertently compromise the intrinsic harmony and coherence of music. In this paper, we develop InstructME, an Instruction guided Music Editing and remixing framework based on latent diffusion models. Our framework fortifies the U-Net with multi-scale aggregation in order to maintain consistency before and after editing. In addition, we introduce chord progression matrix as condition information and incorporate it in the semantic space to improve melodic harmony while editing. For accommodating extended musical pieces, InstructME employs a chunk transformer, enabling it to discern long-term temporal dependencies within music sequences. We tested InstructME in instrument-editing, remixing, and multi-round editing. Both subjective and objective evaluations indicate that our proposed method significantly surpasses preceding systems in music quality, text relevance and harmony. Demo samples are available at https: //musicedit. github. io

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JMLR Journal 2019 Journal Article

Kernel Approximation Methods for Speech Recognition

Avner May
Alireza Bagheri Garakani
Zhiyun Lu
Dong Guo
Kuan Liu
Aurélien Bellet
Linxi Fan
Michael Collins

We study the performance of kernel methods on the acoustic modeling task for automatic speech recognition, and compare their performance to deep neural networks (DNNs). To scale the kernel methods to large data sets, we use the random Fourier feature method of Rahimi and Recht (2007). We propose two novel techniques for improving the performance of kernel acoustic models. First, we propose a simple but effective feature selection method which reduces the number of random features required to attain a fixed level of performance. Second, we present a number of metrics which correlate strongly with speech recognition performance when computed on the heldout set; we attain improved performance by using these metrics to decide when to stop training. Additionally, we show that the linear bottleneck method of Sainath et al. (2013a) improves the performance of our kernel models significantly, in addition to speeding up training and making the models more compact. Leveraging these three methods, the kernel methods attain token error rates between $0.5\%$ better and $0.1\%$ worse than fully-connected DNNs across four speech recognition data sets, including the TIMIT and Broadcast News benchmark tasks. [abs] [ pdf ][ bib ] &copy JMLR 2019. ( edit, beta )

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TCS Journal 2019 Journal Article

RVCCC: A new variational network of cube-connected cycles and its topological properties

Zhen Zhang
Shu-Qiang Huang
Dong Guo
Yong-Hui Li

The CCC( r, n ) network is an extension of the hypercube which replaces each vertex with a cycle of length n, providing that the hypercube is r-dimensional. When n > r, the CCC( r, n ) network contains more vertices than that of CCC( r, r ), which makes it more useful in the construction of a large-scale interconnection network. However, the CCC( r, n ) is irregular when n > r, which makes their properties difficult to be analyzed. In this paper, we propose a new variational network of the cube-connected cycles (RVCCC). The RVCCC networks have the properties of regularity, vertex-symmetry, and low diameter. Compared with the general CCC networks, the RVCCC networks are more suitable for constructing a large-scale interconnection network. After the shortest internode distance between any two vertices in the RVCCC was determined, the exact diameter of this network was calculated and the communication algorithms, including the routing algorithm and the broadcasting algorithm, were also developed.

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