arXiv AI
ActiveSAM: Fast and Accurate Open-Vocabulary Semantic Segmentation with Frozen SAM 3
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
arXiv AI
Jun 16
ActiveSAM: Image-Conditional Class Pruning for Fast and Accurate Open-Vocabulary Segmentation
arXiv:2606. 16996v1 Announce Type: cross Abstract: Segment Anything Model 3 (SAM 3) provides a strong frozen backbone for concept-prompted segmentation, but applying it directly to open-vocabulary semantic segmentation (OVSS) is inefficient: full-resolution decoding is typically run over the entire dataset vocabulary, whereas each image contains only a small active subset of classes.
arXiv Computer Vision
Sep 4
Efficient Semantic Understanding from Digital Foveation
The paper proposes a lightweight active‑vision pipeline that mimics biological foveation to perform semantic segmentation more efficiently. By selecting salient fixations, observing high‑resolution foveal patches, and using low‑resolution context, the method achieves 95.9% of baseline Top‑1 accuracy with only 4.7% of the computational cost, and recovers 90.6% of baseline object recall using 58.6% of the computation. The results demonstrate that sparse, selectively allocated observations can yield substantial semantic understanding, suggesting active vision as a viable alternative to uniform dense processing.
arXiv Machine Learning
Aug 4
AdaDINO: Context-Adaptive DINO-Distilled Vision Foundation Models for Efficient Open-Vocabulary Edge Inference
arXiv:2604. 15622v3 Announce Type: replace-cross Abstract: Always-on contextual AI runs language-aligned vision foundation models (VFMs) on edge devices, where the on-device model is the dominant continuous compute cost under strict latency and power limits.
arXiv AI
Aug 19
Exploring Efficient Open-Vocabulary Segmentation in the Remote Sensing
The paper introduces OVRSISBench, a unified benchmark for open‑vocabulary remote sensing image segmentation, and evaluates existing OVS/OVRSIS models, uncovering their shortcomings in remote sensing contexts. Leveraging insights from this evaluation, the authors propose RSKT‑Seg, a new framework featuring a Multi‑Directional Cost Map Aggregation module, an Efficient Cost Map Fusion transformer, and a Remote Sensing Knowledge Transfer module. Experiments on the benchmark demonstrate that RSKT‑Seg outperforms strong baselines by +3.8 mIoU and +5.9 mACC while achieving twice the inference speed.
Hugging Face Trending Papers
Sep 3
Efficient Semantic Understanding from Digital Foveation
The paper proposes a lightweight active‑vision pipeline that mimics biological foveation to perform dense semantic segmentation more efficiently. By selecting salient fixations, it uses high‑resolution foveal views, low‑resolution context, and adaptive computation to accumulate semantic information. On ADE20K‑Object, a single foveated observation attains 95.9% of baseline Top‑1 accuracy with only 4.7% of the computational cost, and semantic accumulation recovers 90.6% of baseline recall using 58.6% of the computation.
arXiv AI
Aug 28
PACE: A Unified Condense-and-Extract Paradigm for Fast VLM Inference
PACE introduces a training‑free Condense‑and‑Extract framework that speeds up Vision‑Language Model inference by first adaptively downsampling visual inputs before encoding and then selectively retaining essential tokens during decoding. The Adaptive Pixel Compressor (APC) reduces encoder workload while preserving global context, and the Dynamic Dual‑Attention Extractor (DDAE) keeps task‑critical details by fusing visual and language signals. Applied to Qwen2.5‑VL‑7B, PACE maintains 93.8% of performance using only 10% of visual tokens, achieving a 3.1× speedup in time to first token.