16 accepted papers
Information retrieval (IR) systems play a critical role in navigating information overload across various applications. Existing IR benchmarks primarily focus on simple queries that are semantically analogous to single- and multi-hop relations, overlooking complex logical queries involving first-ord
Contrastive learning has advanced the representation learning across domains, yet its success relies on data augmentations that preserve semantic contents while providing the view diversities. Multivariate time series, however, are inherently noisy, non-stationary, and lack such intuitive semantic c…
Vision-Language Models (VLMs) extend Large Language Models (LLMs) with visual perception capabilities, unlocking broad applications across many domains. However, ensuring their safety remains a critical challenge, as adversarial visual inputs can easily bypass built-in safeguards and elicit harmful
Motion planning is a difficult task, especially when generating feasible future trajectories in complex and interactive scenarios. While recent advancements in imitation-based planning have shown significant progress, this approach often encounters causal confusion in dynamic traffic environments. T
Collecting and annotating data for semantic segmentation can end up costing a lot of time and energy. Unsupervised Domain Adaptation (UDA) for semantic segmentation allows models trained on certain source domain data (such as the GTA synthetic dataset) to be applied to certain target data (like the…
The significant communication overhead and client data heterogeneity have posed an important challenge to current federated learning (FL) paradigm. Existing compression-based and optimization-based FL algorithms typically focus on addressing either the model compression challenge or the data heterog…
Federated learning (FL) is greatly challenged by the communication bottleneck and computation limitation on clients. Existing methods based on quantization for FL cannot simultaneously reduce the uplink and downlink communication cost and mitigate the computation burden on clients. To address this p…
Information retrieval plays a crucial role in resource localization. Current dense retrievers retrieve the relevant documents within a corpus via embedding similarities, which compute similarities between dense vectors mainly depending on word co-occurrence between queries and documents, but overloo…
Local feature matching remains a fundamental challenge in computer vision. Recent Area to Point Matching (A2PM) methods have improved matching accuracy. However, existing research based on this framework relies on inefficient pixel-level comparisons and complex graph matching that limit scalability.…
Deep learning models often suffer from performance degradation in unseen domains, posing a risk for safety-critical applications such as autonomous driving. To tackle this problem, recent studies have leveraged pre-trained Visual Foundation Models (VFMs) to enhance generalization. However, exsiting…
Pre-trained language models have become an integral component of question-answering systems, achieving remarkable performance. However, for practical deployment, it is crucial to perform knowledge distillation to maintain high performance while operating under computational constraints. In this pape…
In ophthalmic surgery, particularly in procedures involving the posterior segment, clinicians face significant challenges in maintaining precise control of hand-held instruments without damaging the fundus tissue. Typical targets of this type of surgery are the internal limiting membrane (ILM) and t
In retinal surgery clinicians access the internal volume of the eyeball through small scale trocar ports, typically 0.65 mm in diameter, to treat vitreoretinal disorders like idiopathic epiretinal membrane and age-related macular holes. The treatment of these conditions involves the removal of thin
Fast and accurate segmentation of stroke lesions is highly desirable to help specialists in lesion measurements and making treatment plans. Segmentation of stroke lesions are challenging due to evolvement of stroke, low contrast and highly variable grayscale distributions. In this paper, we propose…
Answer selection task requires finding appropriate answers to questions from informative but crowdsourced candidates. A key factor impeding its solution by current answer selection approaches is the redundancy and lengthiness issues of crowdsourced answers. Recently, Deng et al. (2020) constructed a…
At present, there are many challenges for robotic systems to grasp in a dynamic and unstructured environment. The Robotic Grasping and Manipulation Competition (RGMC) aims to encourage researchers to focus on these challenges. The solution proposed in this letter was used to compete in the 2019 and