arXiv AI
Aug 25

Budget-Constrained Embodied Perception: Four Resource Walls and a Pre-Registered Evaluation of Access-Structured Perception on Open Models at less than 31B

arXiv:2608.22975v1 Announce Type: new Abstract: Embodied multimodal agents must answer from growing observation streams under a fixed per-decision token budget. We formalize this constraint through f...

By Defu Lin, Wenhui Chen, Ziyao Lin, Jianlin Chen, Peiji Long, Chi Man Vong
arXiv Computer Vision
Sep 15

HGSQ: Heatmap-Guided Sparse Query Detector for Real-Time Aerial Small Object Detection

HGSQ is a real‑time aerial small‑object detector that uses a heatmap‑guided sparse query strategy to focus computation on foreground regions. It introduces a lightweight Heatmap Budget Predictor to generate a foreground budget map, and then employs Heatmap‑Guided Sparse Query Selection, Heatmap‑Gated Lite Snake Convolution, and Adaptive Query‑Decoder Budgeting to efficiently process only small‑object areas. On NWPU VHR‑10 and VisDrone2019, HGSQ achieves 95.10 mAP50 and 54.8 mAP50 respectively while running at 96 FPS with only 48.6 GFLOPs on an RTX 4070.

By Yangchen Zeng
arXiv Machine Learning
Oct 9

Read What Matters: Query-Adaptive Quantization for KV Caches

The paper introduces ReadKV, a query‑adaptive quantization method for KV caches that stores keys and values in progressive codes, allowing each decoding query to allocate different precision prefixes for keys and values. By optimizing a calibrated distortion objective under fixed budgets, ReadKV achieves near‑optimal allocation and demonstrates that query‑dependent access can outperform any query‑independent reader at the same read budget. Experiments on six base models show that reading only four bits on average from an eight‑bit cache increases perplexity by at most 0.66% while reducing logical reads and retained capacity compared to a 16‑bit cache, and it achieves lower latency than existing codecs on an NVIDIA A10G.

By Siddharth Bhandari, Lucas Gretta, Krishna Balasubramanian, Shiva Kasiviswanathan
arXiv Computer Vision
Oct 9

RT-DETRv4: Painlessly Furthering Real-Time Object Detection with Vision Foundation Models

RT-DETRv4 introduces a cost‑effective distillation framework that leverages Vision Foundation Models to improve lightweight real‑time object detectors. The method uses a Deep Semantic Injector to embed high‑level VFM representations into deep detector layers and a Gradient‑guided Adaptive Modulation strategy to adjust semantic transfer intensity based on gradient norms. These techniques yield consistent performance gains across DETR‑based models without adding inference overhead, achieving state‑of‑the‑art COCO AP scores at high FPS.

By Zijun Liao, Yian Zhao, Xin Shan, Yu Yan, Chang Liu, Lei Lu, Xiangyang Ji, Jie Chen