13 accepted papers
Accurate user behavior modeling is fundamental to the prediction of click-through rates (CTR) in industrial recommendation systems and online advertising. Traditional discriminative models, which rely on isolated ID features, struggle to capture the evolving nature of user intents across multiple ch
3D semantic occupancy prediction is crucial for autonomous driving perception, offering comprehensive geometric scene understanding and semantic recognition. However, existing methods struggle with geometric misalignment in view transformation due to lack of pixel-level accurate depth estimation, an
Multimodal Web Search Agents demonstrate a practically valuable capability by fusing information from diverse modalities (e.g., text and vision), retrieved iteratively from the internet, to address complex user queries. However, the visual modality is prone to information overload, and the noise con
While Graph Neural Networks (GNNs) have shown outstanding performance in node classification tasks, they are vulnerable to adversarial attacks, which are imperceptible changes to input samples. Adversarial training, as a widely used tool to enhance the adversarial robustness of GNNs, has presented r…
Recently, numerous methods have been proposed to enhance the robustness of the Graph Convolutional Networks (GCNs) for their vulnerability against adversarial attacks. Despite their empirical success, a significant gap remains in understanding GCNs' adversarial robustness from the theoretical perspe
Generative models in Autonomous Driving (AD) enable diverse scenario creation, yet existing methods fall short by only capturing a limited range of modalities, restricting the capability of generating controllable scenes for comprehensive evaluation of AD systems. In this paper, we introduce a multi…
Performing shotcrete operations at construction sites can be hazardous to humans and inefficient. Robots can offer a safer and more efficient alternative to assist in these tasks. We present a new planning strategy for shotcrete robots, including both the spraying and surface finishing phases, that
Compared with traditional in-pipe robots, tensegrity robots have exhibited many advantages such as light-weight, compliant, collapsible, low-cost, and rapidly manufacturable characteristics. However, published tensegrity in-pipe robots still have limited load capacity, because they rely on the stres
Seed-inspired minimalist microflyers are showing potential as dispersal platforms for sensor networks and seeds. Their function relies on the sheer number of low-cost flyers, inevitably raising concerns about the post-operation environmental impact. We propose a biodegradable paper glider platform f…
Despite the elegantly simple operation principle, the design of everting vine robots still presents two complexities: a tip-device required for buckle-free retraction; a pressurised base chamber to maintain inflation while allowing the everted body to be winded onto a motorised reel. We create a new…
Recently, adversarial metric learning has been proposed to enhance the robustness of the learned distance metric against adversarial perturbations. Despite rapid progress in validating its effectiveness empirically, theoretical guarantees on adversarial robustness and generalization are far less und…
Can Transformer perform $2\mathrm{D}$ object- and region-level recognition from a pure sequence-to-sequence perspective with minimal knowledge about the $2\mathrm{D}$ spatial structure? To answer this question, we present You Only Look at One Sequence (YOLOS), a series of object detection models bas…
Nowadays, driven by the increasing concern on diet and health, food computing has attracted enormous attention from both industry and research community. One of the most popular research topics in this domain is Food Retrieval, due to its profound influence on health-oriented applications. In this p…