20 accepted papers
Feature matching across video streams remains a cornerstone challenge in computer vision. Increasingly, robust multimodal matching has garnered interest in robotics, surveillance, remote sensing, and medical imaging. While traditional rely on detecting and matching spatial features, they break down…
One important direction of Federated Foundation Models (FedFMs) is leveraging data from small client models to enhance the performance of a large server‑side foundation model. Existing methods based on model level or representation level knowledge transfer either require expensive local training or
Model-based reinforcement learning (MBRL) improves sample efficiency by leveraging learned dynamics models, but often suffers from unstable training due to dynamics model learning mismatch: models are trained on data from historical policies while being queried under the continually updated current …
As publicly available data dwindles, synthetic data generation (SDG) has become a practical solution for privacy-preserving data sharing. By training generative models on private data, SDG creates samples that retain task-relevant features while obfuscating sensitive content. However, recent work sh
Image-based 3D Genetation has made significant progress in typical scenarios, achieving high fidelity in capturing intricate textures. However, in the Architecture, Engineering, and Construction (AEC) design stages, existing technologies still face considerable challenges, particularly in handling s…
The rapid advancement of large-language models (LLMs) has driven extensive research into parameter compression after training has been completed, yet compression during the training phase remains largely unexplored. In this work, we introduce Rate-Constrained Training (BackSlash), a novel training-t…
The Segment Anything Model (SAM) excels at generating high-quality object masks with various prompts but struggles in ultra-low light. We developed EP-SAM (Edge-Detection Prompt SAM) with a Low Light Edge-Detection Network (LLEN), offering strong robustness and lightweight performance in ultra-low l…
Federated Learning (FL) is a promising approach for training machine learning models on decentralized data while preserving privacy. However, privacy risks, particularly Membership Inference Attacks (MIAs), which aim to determine whether a specific data point belongs to a target client's training se…
Large language models (LLMs) have shown increasing effectiveness in Text-to-SQL tasks. However, another closely related problem, Cross-System SQL Translation (a.k.a., SQL-to-SQL), which adapts a query written for one database system (e.g., MySQL) into its equivalent one for another system (e.g., Cli…
The recent success of 3D Gaussian Splatting (3DGS) on the task of novel view synthesis has amazed every one with its photorealistic results with high training and rendering speed. This paper aims to increase the interpretability of the model in both geometric attribute and appearances by a simple ye…
Generative retrieval (GR) has emerged as a transformative paradigm in search and recommender systems, leveraging numeric-based identifier representations to enhance efficiency and generalization. Notably, methods like TIGER, which employ Residual Quantization-based Semantic Identifiers (RQ-SID), hav…
Zero-shot text-to-speech (TTS) synthesis aims to clone any unseen speaker’s voice without adaptation parameters. By quantizing speech waveform into discrete acoustic tokens and modeling these tokens with the language model, recent language model-based TTS models show zero-shot speaker adaptation cap…
In recent years, Federated Learning (FL) has garnered significant attention as a distributed machine learning paradigm. To facilitate the implementation of the "right to be forgotten," the concept of federated machine unlearning (FMU) has also emerged. However, current FMU approaches often involve a…
Low-light visual perception, such as SLAM or SfM at night, has received increasing attention, in which keypoint detection and local feature description play an important role. Both handcraft designs and machine learning methods have been widely studied for local feature detection and description, ho…
Video semantic segmentation (VSS) is a computationally expensive task due to the per-frame prediction for videos of high frame rates. In recent work, compact models or adaptive network strategies have been proposed for efficient VSS. However, they did not consider a crucial factor that affects the c…
Rate control is a critical part for video compression, especially in bandwidth-limited tasks such as live and broadcast. The newly-rising learned video compression has shown advantageous rate-distortion (RD) performance in previous research, but lack of rate control heavily limits its usage in real…
Denoisers trained with synthetic noises often fail to cope with the diversity of real noises, giving way to methods that can adapt to unknown noise without noise modeling or ground truth. Previous image-based method leads to noise overfitting if directly applied to temporal denoising, and has inadeq…
Light-field cameras capture sub-views from multiple perspectives simultaneously, with possibly reflectance variations that can be used to augment material recognition in remote sensing, autonomous driving, etc. Existing approaches for light-field based material recognition suffer from the entangleme…
In this paper we propose to use a denoising autoencoder (DAE) prior to simultaneously solve a linear inverse problem and estimate its noise parameter. Existing DAE-based methods estimate the noise parameter empirically or treat it as a tunable hyper-parameter. We instead propose autoencoder guided E…
A novel HEVC-based efficient video denoising algorithm is proposed in this paper. It uses a spatial Gaussian filter for the chrominance components and then utilizes the HEVC motion estimation process to find the best temporal correspondence for low-pass filtering. Other HEVC tools such as quantizati…