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Video object detection is a demanding computer vision topic that extends static image-based detection by introducing camera motion and temporal dynamics. This brings significant challenges, such as occlusion, scene blurriness, and dynamic shape changes caused by object and camera movement. Nevertheless, the temporal correlations between frames and the motion patterns of objects also provide rich and valuable information. The object detection and tracking over time enables machines to understand dynamic scenes and make informed decisions in complex environments. Nowadays, video object detection has become essential for many real-world applications, including autonomous driving, intelligent surveillance, human-computer interaction, and video content analysis.

Existing image-based detection models have achieved remarkable success, offering excellent accuracy and real-time detection capabilities in static scenarios. However, directly applying these models to video introduces several issues. Specifically, image-based models treat video frames independently, ignoring temporal relationships across frames, which often leads to unstable detection results in complex scenes and redundant computations for similar consecutive frames. Moreover, in real-world scenarios, videos are typically stored in compressed formats before being uploaded or transmitted. Fully decompression of the video further increases the computational overhead.

We propose Motion-Assisted YOLO (MA-YOLO), an efficient video object detection strategy that leverages the motion information naturally embedded in compressed video streams while utilizing existing image-based detection models to address the aforementioned challenges. Specifically, we adopt YOLO-X variants as our base detector for static images. Rather than performing detection on every video frame, we detect objects only on selected keyframes and propagate the predictions to estimate detection results for intermediate frames. The proposed framework consists of three modules: (1) keyframe selection and sparse inference, (2) motion vector extraction and pixel-wise assignment, and (3) motion-guided decision propagation. By incorporating the keyframe-based detection and motion [...]

We are very happy to welcome a new MCL member, James Zhan. Here is a quick interview with James:

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

My name is Yunkai Zhan, and people always call me James. I’m currently a rising junior undergraduate student major in computer science. My research interest includes computer vision, machine learning, and computational social science. Outside of academia, I’m a basketball and football (soccer but it should be football) fan and player. I also play poker and love watching movies.

2.        What is your impression of MCL and USC?

I’m very grateful to join MCL to work with Dr. Kuo and all the students. Dr. Kuo is always supporting his students and providing meaning advises and inspiring ideas. The lab has a very vibrant vibe. I enjoy talking to everyone during the pizza time before the seminar. If I haven’t talk to you, feel free to reach out to me next time.

3.        What is your future expectation and plan in MCL?

I’m looking forward to contribute to projects starting this fall to learn from the process of self studying and collaborate with experienced researchers. I’m really excited to work on transparent and interpretable Green Learning.

We are very happy to welcome a new MCL member, Jimmy Xiao. Here is a quick interview with Jimmy:

1. Could you briefly introduce yourself and your research interests?My name is Jimmy Xiao, and I’m a rising senior undergraduate student double majoring in Economics and Computer Science at USC. I joined MCL as a summer intern. My research interests include machine learning and computer vision. Outside of academics, I enjoy swimming and golfing as a way to stay balanced and energized.

2. What is your impression of MCL and USC?

MCL is an intellectually vibrant and supportive group where people are willing to exchange ideas, offer guidance, and work together toward solving challenging problems. I enjoy interacting with people at MCL and am excited about the chance to collaborate and contribute to meaningful research in the future. USC is dynamic and opportunity-rich, and is always filled with enthusiasm.3. What is your future expectation and plan in MCL?

I look forward to deepening my engagement with MCL by continuing to participate in and contribute to challenging and intriguing research at MCL. I hope to learn from experienced researchers in MCL and continue to develop my technical and analytical skills. I also would like to interact with individuals in MCL and make lasting connections. 

We are very happy to welcome a new MCL member, Qi Cao. Here is a quick interview with Qi:

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

My name is Qi Cao, and I’m currently a PhD student in the ECE department at USC. I joined the MCL at the end of 2024. My research interests span machine learning, computer vision, and time-series analysis. Since joining MCL, I’ve been eager to contribute to ongoing projects, collaborate with lab members who have diverse technical backgrounds, and continue refining my skills through both hands-on experiments and regular discussions.

2. What is your impression of MCL and USC?

MCL comes across as a supportive, close-knit lab where the collaborative atmosphere helps make challenging projects feel manageable. USC strikes me as the kind of campus that buzzes with energy—entrepreneurial, diverse, and always open to new perspectives.

3. What is your future expectation and plan in MCL?

I’m looking forward to collaborating closely with everyone in MCL,  sharing ideas during group meetings, and learning from the lab’s diverse expertise. I hope to contribute actively to interesting projects by participating in regular discussions and offering support whenever it’s needed. I aim to both grow my own skills and help the whole team move our research forward.

Congratulations to Wei Wang for passing her defense today. Wei’s thesis title is “Explainable and Lightweight Machine Learning Models for Image Super-Resolution and Denoising.” Her Dissertation Committee includes Jay Kuo (Chair), Antonio Ortega, and Jernej Barbic (Outside Member). The Committee members were pleased with Wei’s thesis work. Here is a summary of her thesis:Image signal processors convert camera sensor data into images via digital image processing operations. In recent years, we have witnessed an increased interest in using Deep Learning to enhance the ISP raw images for improved quality. The main challenge is the regularization of this ill-posed problem.

Practically, the trade-off between quality improvement and the computational complexity is the key issue of widely applying the existing state-of-the-art methods. In this dissertation defense talk, we propose effective and efficient techniques for the typical low-level image enhancement problem, image super-resolution (SR) based Green Learning. First, we propose LSR and LSR++, a light-weight super-resolution green learning method with feedforward design without backpropagation, and its advanced version with significantly smaller computational complexity. Second, we develop Green U-Shaped Learning (GUSL) method for super-resolution with multi-level coarse-to-fine semantic estimation and effective conditional residual prediction. Third, we introduce Green U-Shaped Learning method to medical computed tomography (CT) images for low-dose CT image denoising enhancement to demonstrate its generalization capability. All above, these methods contribute to advanced efficiency with competitiveperformance, mathematical transparency, and robust generalizability in low-level computer vision problems.

Wei generously shared her experiences in the MCL Lab with us: It has been a long journey. First and foremost, I would like to extend my sincere thanks to my Ph.D. advisor, Professor C.-C. Jay Kuo, for his outstanding mentorship and consistent support throughout my doctoral journey. His dedication to research, strong work ethic, and sense [...]