AxisQ introduces a depth‑adaptive bit‑allocation framework for camouflaged object detection (COD) that allocates precision in the channel domain for shallow features and in the local‑frequency domain for deep features, guided by COD‑aware sensitivity. It also incorporates a boundary‑aware Hessian approximation to better preserve subtle foreground regions and ambiguous boundaries during post‑training quantization. Experiments on multiple COD models and benchmarks show significant accuracy gains at low bit widths, with relative weighted F‑measure improvements of 12.7% for CFRN and 33.3% for CamoFormer‑P on the CAMO dataset under W2A2 quantization.
arXiv:2610.15092v1 Announce Type: cross Abstract: Quantization plays a crucial role in efficiently deploying deep learning models on resource-constrained devices. Post-training quantization does not...
arXiv:2605.15828v4 Announce Type: replace Abstract: Feed-forward 3D reconstruction models, represented by Visual Geometry Grounded Transformer (VGGT), jointly predict multiple visual geometry tasks s...
arXiv:2609.16656v1 Announce Type: new Abstract: State space models (SSMs), particularly Mamba, have emerged as efficient alternatives to attention-based architectures and have been extended to vision...
arXiv:2607. 01870v1 Announce Type: new Abstract: Camouflaged Object Detection (COD) aims to locate and segment objects that blend into their surroundings, presenting challenges due to weak edge cues and ill-defined boundaries.
arXiv:2607. 06600v1 Announce Type: cross Abstract: Line segment detection is a key building block in visual SLAM, 3D reconstruction, and industrial inspection.
arXiv:2603.11521v2 Announce Type: replace-cross Abstract: Unsupervised Camouflaged Object Detection (UCOD) remains a challenging task due to the high intrinsic similarity between target objects and t...
State space models (SSMs), particularly Mamba, have emerged as efficient alternatives to attention-based architectures and have been extended to vision through ViM, VMamba, and Visual State Space Dual...
arXiv:2607. 18540v1 Announce Type: cross Abstract: Robotic perception pipelines increasingly rely on large vision backbones deployed on SWaP-constrained edge platforms, making post-training quantization (PTQ) attractive for real-time inference.
arXiv:2607. 28589v1 Announce Type: cross Abstract: Post-training quantization (PTQ) has emerged as an effective solution for deploying Vision Transformers (ViTs) on resource-constrained devices.
VQ-Transplant is a framework that allows new vector‑quantization (VQ) modules to be inserted into frozen, pre‑trained visual tokenizers without retraining the entire model. By preserving all encoder‑decoder parameters and adding a lightweight decoder adaptation trained for only five epochs on ImageNet‑1k, the method mitigates decoder‑quantization mismatch. Experiments show that VQ-Transplant achieves near state‑of‑the‑art reconstruction fidelity for industry‑level models such as VAR while cutting training costs by 95%.
The paper introduces QMapAnything, a post‑training quantization framework tailored for feed‑forward geometry transformers that use Transformers to predict camera poses, depth, and structure in a single pass. It addresses the challenge that heterogeneous token types share a single activation quantization grid, which can cause high‑leverage tokens to dominate outputs. By treating rotation and smoothing as token‑blind reparameterizations and defining a statistic γ to gauge when differentiated bias correction is needed, the method achieves W4A8 quantization that closely matches full‑precision performance while W8A8 reduces peak memory by 2.2–3.1× and speeds up inference by up to 3.1× on real hardware.
LiG-DETR introduces a Global-Local Reassembly framework for aerial object detection that captures high‑fidelity local features before compression and integrates them into a unified end‑to‑end DETR decoder. The method uses a shared encoder to extract both global and locally magnified features, reassembles the local features according to their spatial positions, and employs Context‑Preserved Selective Reassembly and Density‑Aware Adaptive Query Allocation to reduce redundant computation. Experiments demonstrate significant improvements on small and medium objects while maintaining strong performance on large objects, with favorable accuracy–efficiency trade‑offs and better cross‑domain generalization.