Defines 3D Instruction Ambiguity Detection as a new task, releases the Ambi3D benchmark, shows state-of-the-art 3D LLMs struggle with it, and proposes the AmbiVer framework that gathers multi-view visual evidence to guide VLMs in judging ambiguity.
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Grounding dino: Marrying dino with grounded pre-training for open-set object detection
22 Pith papers cite this work. Polarity classification is still indexing.
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ToG-Bench is the first benchmark for task-oriented spatio-temporal video grounding in egocentric videos, with explicit-implicit dual grounding and one-to-many object scenarios across 100 ScanNet clips and 2704 instructions.
Thermal-Det is the first LLM-supervised open-vocabulary thermal object detector, created via synthetic data conversion from GroundingCap-1M and RGB-to-thermal distillation, yielding 2-4% AP gains on benchmarks.
OA-VAT improves visual active tracking by combining instance-level prototype discrimination with occlusion-aware diffusion planning, reporting gains over prior SOTA on simulated and real drone benchmarks.
STORM is an end-to-end MLLM for referring multi-object tracking that uses task-composition learning to leverage sub-task data and introduces the STORM-Bench dataset, achieving SOTA results.
InstAP introduces instance-aware pre-training with a new dual-granularity dataset InstVL that improves both fine-grained instance retrieval and global video understanding over standard VLP baselines.
UniSpector organizes visual prompt space with spatial-spectral and contrastive encoders to support open-set defect localization, beating baselines by at least 19.7% AP50b and 15.8% AP50m on the new Inspect Anything benchmark.
SUPERGLASSES is the first VQA benchmark built from actual smart glasses data, and SUPERLENS is an agent using automatic object detection, query decoupling, and multimodal search that outperforms GPT-4o by 2.19% on it.
AlignPose performs generalizable 6D pose estimation by multi-view feature-metric refinement that minimizes feature discrepancy between on-the-fly rendered object features and observed images across calibrated views.
A new data pipeline using real photos, entity removal, and image-to-video models plus a cross-view attention loss enables text-driven generation of actors in reference scenes with improved alignment.
EmoVerse is a large open-source dataset enabling interpretable visual emotion analysis via B-A-S triplets, region grounding, and unified CES/DES representations created through an MLLM-driven pipeline.
PET-DINO unifies visual and text prompts in Grounding DINO via an alignment-friendly generation module and prompt-enriched training strategies to improve zero-shot open-set object detection.
LangFlash introduces a feed-forward model for 3D language Gaussian splatting from sparse unposed images, claiming superior novel view synthesis and semantic consistency via enriched training data and sparse semantic encoding.
DetRefiner fuses global and local features with a Transformer to refine OVOD confidence scores, delivering up to +10.1 AP gains on novel categories across multiple datasets.
Caries-DETR adds tooth-structure query initialization and lesion-aware loss reweighting to DETR, reaching state-of-the-art caries detection on AlphaDent and DentalAI datasets.
SemLT3D introduces semantic-guided expert distillation with a language MoE module and CLIP projection to enrich features for long-tailed classes in camera-only 3D detection.
MV3DIS uses 3D-guided mask matching and depth consistency to produce more consistent multi-view 2D masks that refine into accurate zero-shot 3D instances.
VoxSAMNet introduces sparsity-aware deformable attention via a dummy node and foreground modulation with dropout plus text-guided filtering to reach new state-of-the-art mIoU of 18.2% on SemanticKITTI and 20.2% on SSCBench-KITTI-360 for monocular 3D scene completion.
A data-generation pipeline plus pairwise subject-consistency rewards in RL improve consistency and prompt adherence for multi-subject personalized image generation.
OVOD-Agent models visual reasoning as a weakly Markovian decision process with bandit-driven exploration to create a self-evolving open-vocabulary detector that improves on rare categories in COCO and LVIS.
citing papers explorer
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3D Instruction Ambiguity Detection
Defines 3D Instruction Ambiguity Detection as a new task, releases the Ambi3D benchmark, shows state-of-the-art 3D LLMs struggle with it, and proposes the AmbiVer framework that gathers multi-view visual evidence to guide VLMs in judging ambiguity.
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OVOD-Agent: A Markov-Bandit Framework for Proactive Visual Reasoning and Self-Evolving Detection
OVOD-Agent models visual reasoning as a weakly Markovian decision process with bandit-driven exploration to create a self-evolving open-vocabulary detector that improves on rare categories in COCO and LVIS.