8 accepted papers
Parameter-efficient Mixture-of-Experts (MoE) architectures, such as LoRA-MoE, enable strong and generalizable fine-tuning. However, a critical problem arises when fine-tuning these architectures with advanced reinforcement learning algorithms such as Group Relative Policy Optimization (GRPO). Tradit…
Multimodal Large Language Models (MLLMs) have recently achieved remarkable progress in video understanding. However, their effectiveness in real-time streaming scenarios remains limited due to storage constraints of historical visual features and insufficient real-time spatiotemporal reasoning. To a…
BEV perception is of great importance in the field of autonomous driving, serving as the cornerstone of planning, controlling, and motion prediction. The quality of the BEV feature highly affects the performance of BEV perception. However, taking the noises in camera parameters and LiDAR scans into…
High-definition maps are crucial perception elements for autonomous robot navigation systems, which can provide accurate scene layout and environment information for downstream motion prediction and planning control tasks. Traditional methods based on manual annotation or SLAM algorithms require mas…
Audio-driven talking face, driving talking face by audio, has received considerable attention in multi-modal learning due to its widespread use in virtual reality. However, long-time recording of target high-quality video is needed by most existing audio-driven talking face studies, which significan…
"Stereo depth estimation is particularly amenable to local textured regions while echoes have good depth estimations for global textureless regions, thus the two modalities complement each other. Motivated by the reciprocal relationship between both modalities, in this paper, we propose an end-to-en…
With the thriving of deep learning, CNN-based object detectors have made great progress in the past decade. However, the domain gap between training and testing data leads to a prominent performance degradation and thus hinders their application in the real world. To alleviate this problem, Knowledg…
Person search aims to locate and identify the query person from a gallery of original scene images. Almost all previous methods only consider single high-level semantic information, ignoring that the essence of identification task is to learn rich and expressive features. Additionally, large pose va…