20 accepted papers
Segment Anything Model 2 (SAM2) shows excellent performance in video object segmentation tasks; however, the heavy computational burden hinders its application in real-time video processing. Although there have been efforts to improve the efficiency of SAM2, most of them focus on retraining a lightw…
The rapid development of visual generative models raises the need for more scalable and human-aligned evaluation methods. While the crowdsourced Arena platforms offer human preference assessments by collecting human votes, they are costly and time-consuming, inherently limiting their scalability. Le…
Semantic segmentation is a fundamental task in computer vision with wide-ranging applications, including autonomous driving and robotics. While RGB-based methods have achieved strong performance with CNNs and Transformers, their effectiveness degrades under fast motion, low-light, or high dynamic ra
Large Language Models (LLMs) have demonstrated remarkable capabilities in understanding and generation tasks. However, their massive parameter scale leads to significant resource consumption and latency during inference. Post-training weight-only quantization offers a promising solution by reducing …
AutoRegressive Visual Generation (ARVG) models retain an architecture compatible with language models, while achieving performance comparable to diffusion-based models. Quantization is commonly employed in neural networks to reduce model size and computational latency. However, applying quantization…
Segment Anything Model (SAM) exhibits remarkable zero-shot segmentation capability; however, its prohibitive computational costs make edge deployment challenging. Although post-training quantization (PTQ) offers a promising compression solution, existing methods yield unsatisfactory results when app
Diffusion models have gradually gained prominence in the field of image synthesis, showcasing remarkable generative capabilities. Nevertheless, the slow inference and complex networks, resulting from redundancy at both temporal and structural levels, hinder their low-latency applications in real-wor…
The rapid advancement of visual generative models necessitates efficient and reliable evaluation methods. Arena platform, which gathers user votes on model comparisons, can rank models with human preferences. However, traditional Arena methods, while established, require an excessive number of compa…
Event-based semantic segmentation has great potential in autonomous driving and robotics due to the advantages of event cameras, such as high dynamic range, low latency, and low power cost. Unfortunately, current artificial neural network (ANN)-based segmentation methods suffer from high computation
In recent years, wide-area visual surveillance systems have been widely applied in various industrial and transportation scenarios. These systems, however, face significant challenges when implementing multi-object detection due to conflicts arising from the need for high-resolution imaging, efficie…
Vision Transformers (ViTs) have achieved state-of-the-art performance on various computer vision applications. However, these models have considerable storage and computational overheads, making their deployment and efficient inference on edge devices challenging. Quantization is a promising approac…
Post-training quantization (PTQ), which only requires a tiny dataset for calibration without end-to-end retraining, is a light and practical model compression technique. Recently, several PTQ schemes for vision transformers (ViTs) have been presented; unfortunately, they typically suffer from non-tr…
Traditional gimbal-based bionic eye systems usually use a multi-degree-of-freedom mechanical platform to move the camera freely, which makes the structure complex and bulky. The galvanometer-based reflective bionic eye system uses a galvanometer to replace the traditional mechanical rotation structu…
"Vision transformers have recently gained great success on various computer vision tasks; nevertheless, their high model complexity makes it challenging to deploy on resource-constrained devices. Quantization is an effective approach to reduce model complexity, and data-free quantization, which can…
In this work, we present a simple and general search space shrinking method, called Angle-Based search space Shrinking (ABS), for Neural Architecture Search (NAS). Our approach progressively simplifies the original search space by dropping unpromising candidates, thus can reduce difficulties for exi…
As an external manifestation of human emotions, expression recognition plays an important role in human-computer interaction. Although existing expression recognition methods performs perfectly on constrained frontal faces, there are still many challenges in expression recognition in natural scenes…
This paper describes a high-frame-rate (HFR) vision system that can detect multiple objects in an image of 512 × 512 pixels at 12,000 frames per seconds (fps). An optimized algorithm is proposed based on conventional Histograms of Oriented Gradient (HOG) descriptor and Support Vector Machine (SVM) c…
The research field of nongrasping manipulation is a maturing area in robotic motion control. However, the common principles of motion planning for nongrasping manipulation systems have not yet been established. This paper proposes the concept of virtual connecting manipulation as a generalized motio
We propose a novel scheme for manipulating a passive object using an active plate. The objective of this study is to control an object's orientation with respect to the gravitational force direction by using an active plate for realizing hitherto unrealized object motion. In this context, motions of…
Flower stick juggling is a dexterous task done by skillful jugglers. We aim to realize dexterous tasks done by humans using robotic systems. This work focuses on flower stick juggling and proposes a feedback control strategy for a flower stick juggling task called “propeller” as one of the robotic d…