8 accepted papers
As spectrum congestion intensifies in wireless communication, efficient spectrum utilization through advanced sensing techniques has become increasingly important. This paper proposes a joint carrier frequency and two-dimensional (2-D) Direction of Arrival (DOA) estimation algorithm with signal reco…
Knowledge distillation aims at transferring knowledge from the teacher model to the student one by aligning their distributions. Feature-level distillation often uses L2 distance or its variants as the loss function, based on the assumption that outputs follow normal distributions. This poses a si…
Vision-Language models (VLMs) pre-trained on large corpora have demonstrated notable success across a range of downstream tasks. In light of the rapidly increasing size of pre-trained VLMs, parameter-efficient transfer learning (PETL) has garnered attention as a viable alternative to full fine-tunin…
Space-filling curves (SFCs) act as a linearization approach to map data in higher dimensional space to lower dimensional space, which is used comprehensively in computer vision, such as image/point cloud compression, hashing and etc. Currently, researchers formulate the problem of searching for an o…
Meetings typically involve multiple participants and lengthy conversations, resulting in redundant and trivial content. To overcome these challenges, we propose a two-step framework, Reconstruct before Summarize (RbS), for effective and efficient meeting summarization. RbS first leverages a self-sup…
Pursuing accurate and robust recognizers has been a long-lasting goal for scene text recognition (STR) researchers. Recently, attention-based methods have demonstrated their effectiveness and achieved impressive results on public benchmarks. The attention mechanism enables models to recognize scene…
Domain generalization refers to the problem of training a model from a collection of different source domains that can directly generalize to the unseen target domains. A promising solution is contrastive learning, which attempts to learn domain-invariant representations by exploiting rich semantic…
Deep neural networks (DNNs) have been widely used recently while their hardware deployment optimizations are very time-consuming and the historical deployment knowledge is not utilized efficiently. In this paper, to accelerate the optimization process and find better deployment configurations, we pr…