17 accepted papers
With the advancement of information retrieval (IR) technologies toward deep semantic understanding, reasoning-based methods—featuring explicit chain-of-thought generation—have demonstrated significant advantages in multi-hop and causal reasoning tasks. However, in complex clinical case retrieval sce
Aerodynamic coefficient prediction is pivotal in aircraft and vehicles' design, performance evaluation, and motion control. Integrating artificial neural networks into aerodynamic coefficient prediction offers a promising alternative to traditional numerical methods burdened by extensive computation…
Electrocardiogram (ECG) signals play a crucial role in diagnosing cardiovascular diseases. To reduce power consumption in wearable or portable devices used for long-term ECG monitoring, super-resolution (SR) techniques have been developed, enabling these devices to collect and transmit signals at a…
Risk assessment is crucial for quantifying driving environment risks and reducing the Safety of the Intended Functionality (SOTIF) uncertainty in Autonomous Vehicles (AVs). Traditional methods, however, often concentrate on single-scenario metrics and are insufficient in capturing the complexities o
Ensuring the Safety of the Intended Functionality (SOTIF) for autonomous vehicles (AVs) is critical. Effective risk assessment helps AVs make decisions and avoid risks. However, existing methods face challenges due to environmental uncertainties, insufficient multi-dimensional risk quantification, a
How humans and machines make sense of current inputs for relation reasoning and question-answering while putting the perceived information into context of our past memories, has been a challenging conundrum in cognitive science and artificial intelligence. Inspired by human brain's memory system and…
Colon endoscopic robots represent a promising screening modality for the visualization of colon cancers with high sensitivity. However, current colonoscopy robots are often characterized by intricate and bulky mechanical structures, which pose practical challenges when moving through the complex and…
"Vision transformers (ViTs) have emerged as a promising alternative to convolutional neural networks (CNNs) for various image analysis tasks, offering comparable or superior performance. However, one significant drawback of ViTs is their resource-intensive nature, leading to increased memory footpri…
Deep learning has seen a movement towards the concepts of modularity, module coordination and sparse interactions to fit the working principles of biological systems. Inspired by Global Workspace Theory and long-term memory system in human brain, both are instrumental in constructing biologically pl…
"Allocating different bit widths to different channels and quantizing them independently bring higher quantization precision and accuracy. Most of prior works use equal bit width to quantize all layers or channels, which is sub-optimal. On the other hand, it is very challenging to explore the hyperp…
Recently, cross-modal retrieval is emerging with the help of deep multimodal learning. However, even for unimodal data, collecting large-scale well-annotated data is expensive and time-consuming, and not to mention the additional challenges from multiple modalities. Although crowd-sourcing annotatio…
As Deep Neural Networks (DNNs) usually are overparameterized and have millions of weight parameters, it is challenging to deploy these large DNN models on resource-constrained hardware platforms, e.g., smartphones. Numerous network compression methods such as pruning and quantization are proposed to…
Non-maximum Suppression (NMS) is an essential post-processing step in modern convolutional neural networks for object detection. Unlike convolutions which are inherently parallel, the de-facto standard for NMS, namely GreedyNMS, cannot be easily parallelized and thus could be the performance bottlen…
With the increasing global popularity of self-driving cars, there is an immediate need for challenging real-world datasets for benchmarking and training various computer vision tasks such as 3D object detection. Existing datasets either represent simple scenarios or provide only day-time data. In th…
Modern convolutional object detectors have improved the detection accuracy significantly, which in turn inspired the development of dedicated hardware accelerators to achieve real-time performance by exploiting inherent parallelism in the algorithm. Non-maximum suppression (NMS) is an indispensable…
Three-dimensional feature descriptors are heavily employed in various 3-D perception applications to find keypoint correspondences between two point clouds. The availability of mobile devices equipped with depth sensors compels the developed applications to be both memory and computationally efficie
With the increasing availability of wearable devices, research on egocentric activity recognition has received much attention recently. In this paper, we build a Multimodal Egocentric Activity dataset which includes egocentric videos and sensor data of 20 fine-grained and diverse activity categories…