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We are so happy to welcome a new MCL member, Dingyi Nie joining MCL this semester. Here is a quick interview with Dingyi:

1. Could you briefly introduce yourself and your research interests?

My name is Dingyi Nie. I am a current Master of Science student in Computer Science at USC. I am joining MCL as a research intern starting from April 2024. My research interests mainly include digital signal processing and machine learning, particularly real-world AI. In my spare time, I enjoy music and sports. I’m a keyboard and drum player, and I play soccer and volleyball.

2. What is your impression about MCL and USC?

I got to know Professor Jay Kuo and his MCL from my professor who teaches multimedia systems. I personally have a long-standing interest in multimedia, DSP and machine learning. I find MCL’s focus on Green Learning particularly fascinating because it seamlessly integrates these areas. I am very excited to explore its potential as an emerging tool. I love LA as a city of culture and diversity, and I feel that USC is a good reflection of it. I am excited to interact with all the creative people here.

3. What are your future expectations and plans in MCL?

Starting from the fall semester, I will be working with Yixing Wu on a project exploring Green Learning solutions for irregularly sampled time series modeling. I look forward to building connections with all the members in the lab.

Camouflage object detection (COD)is a challenging task that aims to identify targets “seamlessly” concealed within their surrounding environment, presenting a more challenging task compared to traditional object detection[1]. While under the Video camouflage object detection (VCOD), the intrinsic high variations and increased complexity in the scene poses new obstacles for the detection with videos. We proposed a green method, termed GreenCOD, that leverages gradient boosting and deep features extracted from pre-trained Deep Neural Networks (DNNs), efficiently detects the camouflage objects without back-propagation. In this quarter, based on the GreenCOD model, we further move on to explore how to deal with object detection in camouflage videos in a light-weighted, explainable way.

Inspired by the GreenCOD pipeline, our architecture integrates the EfficientNetB4 backbone in the feature extraction module for each frame. Initially, the input frames are first reshaped into a standard size of 672x672x3 and the processed through the 8-block EfficientNetB4 backbone for feature extraction under different size of reception fields. To well consider the information across different reception spatial sizes, all the features are resized and concatenated to form a rich set of features. A hierarchical architecture is implemented in decision learning module.

Then XGBoost is trained based on the initial prediction maps and temporal information. The short term temporal information is considered by extracting the motion among consecutive frames. The motion flow maps are extracted at a higher resolution, then followed by the utilization of neighborhood reconstruction. This approach ensures that each prediction location takes into account the information from a corresponding 4×4 window in the motion map. The initial result show a satisfying result on VCOD problems.

[1] Fan, Deng-Ping, et al. “Camouflaged object detection.” Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. [...]

Understanding and retrieving information in 3D scenes poses a significant challenge in artificial intelligence (AI) and machine learning (ML), particularly in grasping complex spatial relationships and detailed properties of objects in 3D spaces. While large foundational models such as CLIP [1] have made impressive progress in the 2D domain, their direct application to 3D scene understanding is not straightforward. Therefore, we aim to leverage existing large foundation models to understand 3D scenes and extract 3D information, avoiding building an entirely new 3D model from scratch.
The advancement of large foundation models has inspired recent studies to establish connections among images, text, and 3D data. The work in [2] extracts features from 3D objects with a 3D encoder and aligns them with features extracted by a visual encoder and a text encoder from corresponding rendered 2D images and text, enabling information retrieval of 3D objects from different modalities. However, the model mainly focuses on 3D objects and is limited in understanding complicated 3D spaces like 3D indoor scenes.
[3] Reconstructed compact 3D indoor scenes from multi-view images using DVGO and aligned them with semantic segmentation extracted from corresponding 2D multiview images using the CLIP-LSeg model. A visual reasoning pipeline is applied for reasoning tasks in 3D spaces.
Presently, we are going to develop a ML model that leverages the existing large foundation models to get a better understanding of 3D scenes with lower computational complexity.

Reference:
[1]Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. “Learning transferable visual models from natural language supervision,” In International Conference on Machine Learning, pages 8748–8763. PMLR, 2021.
[2] Hegde, Deepti, Jeya Maria Jose Valanarasu, and Vishal [...]

Surface reconstruction from point cloud scans plays a pivotal role in 3D vision and graphics, finding diverse applications in areas such as AR/VR games, cultural heritage preservation, and building information modeling (BIM). This task is inherently challenging due to the ill-posed nature of reconstructing continuous surfaces from discrete points. Moreover, real-world point cloud scans introduce quite a few obstacles such as varying densities and sensor noise. These properties make the problem a long standing one, which keeps driving researchers to look for more effective solutions.
Early research focused on combinatorial methods [1]–[2], which inferred the connectivity between points directly. The mainstream of surface reconstruction adopts an implicit surface approach [3]–[4]. That is, the surface is represented as an unknown continuous function which is solved by the associated partial differential equations (PDEs). Although these methods offer good quality, they often require oriented normals or additional constraints. Recently, people develop deep learning (DL) models [5]–[6] to solve this problem based on a supervised learning framework. DL methods exploits the training data to learn an implicit function representation.
Despite their high reconstruction quality, the generalizability and complexity remain to be challenges for DL models. In scenarios such as point cloud compression, quality assessment, and dynamic point cloud processing, there is a growing need for low-complexity, low-latency surface reconstruction methods. However, existing PDE and DL-based methods tend to sacrifice simplicity for high reconstruction quality, leaving a gap for low-complexity solutions.

We adopt the unsupervised framework, propose a lightweight high-performance method, and name it green point-cloud surface reconstruction (GPSR). It can be categorized to the family of implicit surface reconstruction methods. The main idea lies in building a signed distance field (SDF) through approximated heat diffusion and fine tuning it iteratively. [...]

Saliency detection research predominantly falls within two categories: human eye fixation prediction, which involves the prediction of human gaze locations on images where attention is most concentrated [1], and salient object detection (SOD) [2], which aims to identify salient object regions within an image. Our study specifically focuses on the former saliency prediction, which predicts human gaze from visual stimuli.

Saliency detection constitutes a crucial task in predicting human gaze patterns from visual stimuli. The escalating demand for research in saliency detection is driven by the growing necessity to incorporate such techniques into various computer vision tasks and to understand human visual system. Many existing saliency detection methodologies rely on deep neural networks (DNNs) to achieve good performance. However, the extensive model sizes associated with these approaches impede their integration with other modules or deployment on mobile devices.

To address this need, our study introduces a novel saliency detection method, named “GreenSaliency”, which eschews the use of DNNs while emphasizing a small model footprint and low computational complexity. GreenSaliency comprises two primary steps: 1) multi-layer hybrid feature extraction, and 2) multi-path saliency prediction. Empirical findings demonstrate that GreenSaliency achieves performance levels comparable to certain deep-learning-based (DL-based) methods, while necessitating a considerably smaller model size and significantly reduced computational complexity.

[1] Zhaohui Che, Ali Borji, Guangtao Zhai, Xiongkuo Min, Guodong Guo, and Patrick Le Callet, “How is gaze influenced by image transformations? dataset and model”, IEEE Transactions on Image Processing, 29, 2287–300.

[2] Dingwen Zhang, Junwei Han, Yu Zhang, and Dong Xu, “Synthesizing supervision for learning deep saliency network without human annotation”, IEEE transactions on pattern analysis and machine intelligence, 42(7), 1755–69.