#01Nov 8, 2026
cs.RO
Single-Radar Bilateral 3D Near-Field Imaging for Mobile Robots
Yingqi Wang, Tingwei Chen, Zhongqin Wang and 1 more
Mobile robots operating in homes need reliable 3D perception of nearby furniture and surrounding structures, yet recovering this information on both sides of the robot's path with a single forward-mounted millimeter-wave radar remains challenging because of near-field wavefront curvature, viewpoint-dependent target visibility, and limited elevation diversity. This paper presents a motion-assisted framework for reconstructing structures on both sides of a robot's trajectory. Our near-field model relates scene reflectivity to stepped-frequency multiple-input multiple-output (MIMO) observations across frequencies and robot positions using spherical paths from transmitter (Tx) to scatterer to receiver (Rx). Bilateral Tx combining provides wideband directional weighting, while sliding-aperture back-projection coherently focuses the measurements using near-field propagation distances. Top-K power fusion combines local images with different visibility. The reconstruction provides lateral-forward support for multi-layer height extraction, followed by guarded adaptive height refinement using the original MIMO channels. Experiments in controlled, office/study, and corridor environments produce mean lateral-forward localization errors of 4.65, 5.63, and 5.98 cm, respectively, with peak-to-background ratios (PBRs) exceeding 13 dB. The framework recovers bilateral 3D scattering structure from one forward pass using a single radar, without relying on learned representations of the scene.
#02Nov 8, 2026
cs.CV
Let the Gallery Learn Itself: Training-Free Iterative Side-View Face Recognition
Zijun Huang, Suncheng Xiang
What if a face recognition system could learn from every encounter without a single gradient update? We present Gallery Self-Evolution (GSE), a training-free framework where the gallery itself adapts at test time. The core mechanism---dedup-FIFO curation---archives novel appearance samples after successful recognition while permanently locking the enrollment template, bounded at 30 embeddings per identity. On CFP-FP and IJB-C benchmarks, GSE closes the side-view gap by +23 points at 45° yaw and boosts TAR@FAR=0.001 by +9.4 points, all at CPU-level cost. No retraining. No GPU. Just a gallery that grows smarter every time it meets you.
#03Nov 8, 2026
cs.CV
Auto-GlaKG: Automated Biomarker Reasoning for Explainable Glaucoma Detection
Cheng Huang, Zeyu Han, Yian Hu and 3 more
Interpretable glaucoma detection requires a transparent connection between fundus images, clinically meaningful biomarkers, and diagnostic predictions. Knowledge graph (KG) reasoning provides this structure, but performance measured using annotated biomarkers does not establish performance when only images are available. We present Auto-GlaKG, an automated framework that combines image-based biomarker prediction with fixed clinical-prior rules. A ResNet50 network predicts the cup-to-disc ratio (CDR) and six morphological signs using biomarker supervision, and a KG reasoner aggregates their signed rule contributions into a glaucoma score. On 689 fundus images evaluated using stratified, group-aware five-fold cross-validation, Auto-GlaKG achieves a pooled area under the receiver operating characteristic curve (AUC) of 0.951 (95\% confidence interval: 0.931--0.969), with sensitivity of 0.970 and specificity of 0.779. The matched image classifier also achieves an AUC of 0.951, while the rule stage adds 0.2~ms per image. Replacing predicted CDR with its annotation increases fold-mean AUC from 0.953 to 0.995. The framework provides an explicit rule-level account of image-based predictions and identifies CDR estimation as a priority for validation using independently measured biomarkers.
#04Nov 8, 2026
cs.CV
Flex-DDCM: Zero-Shot Task-Oriented Image Compression via Diffusion Trajectory Guidance
Yixin Gao, Runsen Feng, Xin Li and 1 more
Recent zero-shot image compression methods exploit the powerful priors of pretrained diffusion models to produce perceptually pleasing reconstructions without compression-specific training. However, image compression serves not only human viewing but also machine analysis, while existing zero-shot codecs have limited flexibility in meeting the diverse reconstruction requirements of these applications. In this paper, we propose Flex-DDCM, a framework for zero-shot task-oriented image compression via diffusion trajectory guidance. Building on Turbo-DDCM, we adapt the coded diffusion trajectory through task-guided noise selection, embedding the effects of task guidance into the compressed representation. Guidance terms can be applied individually or combined, with timestep-dependent strengths controlling their influence throughout denoising. This design enables task adaptation without introducing additional decoding complexity. Experiments demonstrate flexible trade-offs among pixel fidelity, perceptual quality, and downstream task performance, with competitive performance against trained task-oriented codecs and faster decoding than Diff-ICMH.
#05Nov 8, 2026
cs.CV
Beyond Similarity Entropy: A Rank-Guided Data-Free Quantization Framework for Vision Transformers
Runqing Jiang, Ye Zhang, Pengpeng Yu and 4 more
Vision Transformers (ViTs) have demonstrated remarkable performance, yet their computational and memory demands pose significant challenges for deployment on mobile devices. Data-free quantization (DFQ) enables the compression of ViTs without access to their original training data. Existing DFQ methods often synthesize calibration samples by maximizing the entropy of patch similarity. However, similarity entropy describes the dispersion of similarity values without ensuring that the generated samples cover diverse feature directions, potentially limiting their effectiveness for quantization calibration. To address this limitation, we propose RankQ-ViT, a rank-guided DFQ framework for ViTs. We first observe that patch similarity matrices derived from Gaussian noise tend to have lower ranks than those derived from real images. Motivated by this observation, we introduce a block-aware self-contrastive objective that promotes feature diversity during synthetic data generation without explicitly computing matrix rank at each optimization step. We then select calibration samples based on their rank and semantic consistency and use them for block-wise reconstruction of the quantized model. Experiments on ImageNet, COCO, ModelNet40, and ShapeNet demonstrate the effectiveness of RankQ-ViT across multiple architectures and tasks, particularly under low-bit quantization. The code is available at https://github.com/Runqing-forMost/rankq-vit.git