arXiv Computer Vision
Accelerated Decoding of Centroid Positional Encoding for Instance Segmentation
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GVC-Seg: Training-Free 3D Instance Segmentation via Geometric Visual Correspondence
arXiv:2606. 08014v1 Announce Type: cross Abstract: Accurate 3D instance segmentation in point cloud data is critical for machine vision applications.
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Visual Perception Engine: Fast and Flexible Multi-Head Inference for Robotic Vision Tasks
Visual Perception Engine (VPEngine) is a modular framework that enables efficient GPU usage for robotic vision tasks by sharing a foundation model backbone across multiple specialized task heads. It eliminates redundant feature extraction, supports dynamic task prioritization, and achieves up to 3× speedup over sequential execution. The open‑source Python implementation, with ROS2 C++ bindings, delivers real‑time performance (≥50 Hz) on NVIDIA Jetson Orin AGX using TensorRT‑optimized models.
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GTR: Gated Token Recurrence for Efficient Dense Prediction
The paper introduces Gated Token Recurrence (GTR), a softmax‑free recurrent vision backbone that replaces global softmax attention with gated linear attention, alternating scan directions, and enhanced SwiGLU blocks. GTR is distilled from a DINOv3 teacher using only final‑layer patch‑token alignment, and achieves strong performance on COCO object detection (58.9 box AP) with very low latency (1.908 ms on an RTX 4090). The backbone also transfers to multiple dense prediction tasks and runs efficiently on edge hardware via a specialized CUDA operator and TensorRT deployment.
arXiv AI
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A Motion-Aware Vector Quantization Framework with Centroid Reuse for Efficient VLA Inference
arXiv:2607. 24148v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models have demonstrated strong potential for embodied AI, yet their high inference latency on GPUs limits real-time deployment.
Hugging Face Trending Papers
Jun 24
Efficient Remote Sensing Instance Segmentation with Linear-Time State Space Distilled Visual Foundation Models
The computational complexity of Transformers scales quadratically with the number of tokens, which significantly constrains the efficiency of vision models, particularly recent ViT-based foundation models in dense prediction tasks. Instance segmentation, a typical dense visual prediction task in the remote sensing field, faces similar challenges.
arXiv Computer Vision
Aug 24
Enabling Memory-efficient Im2win Convolution with Multi-precision Support on GPU CUDA and Tensor Cores
arXiv:2608.20725v1 Announce Type: cross Abstract: Convolution is a principal computational bottleneck in deep neural networks, and its efficiency depends on tight integration between algorithms and G...