COCOTree is a 21K-image benchmark with 1.8M nodes and an OTQ metric for the new task of open tree-structured visual decomposition.
Semantic-sam: Segment and recognize anything at any granu- larity
11 Pith papers cite this work, alongside 51 external citations. Polarity classification is still indexing.
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VASA is a vision-guided agent for open ad-hoc segmentation that creates and validates masks through planning, tool use, and error recovery, outperforming baselines on the new PARS benchmark and RefCOCOm.
VideoRepair detects text-video misalignments via MLLM-generated questions and performs localized, region-preserving refinement to improve alignment in existing T2V diffusion models.
Set-of-Mark prompting marks segmented image regions with alphanumerics and masks to let GPT-4V achieve state-of-the-art zero-shot results on referring expression comprehension and segmentation benchmarks like RefCOCOg.
ICRDrag performs region-based drag editing in diffusion models by feeding source image, source mask, and target mask into an in-context framework with image-mask attention consistency and source-target attention correspondence regularizations, plus a new Paired Region Dataset.
EPS3D is an end-to-end architecture for 3D panoptic segmentation from multi-view images that uses distillation and semantic-instance mutual enhancement to achieve higher benchmark performance and speed than prior methods.
Amodal SAM extends SAM with a Spatial Completion Adapter, Target-Aware Occlusion Synthesis for data, and consistency losses to reach SOTA amodal segmentation with strong generalization to new objects and scenes.
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.
Presents a training-free personalization toolkit for LVLMs that extracts features via vision foundation models, applies RAG for instance retrieval, and uses visual prompting for multi-concept adaptation on images and videos, claiming SOTA results on a new real-world benchmark.
YOLO26 presents a unified real-time vision model family with dual-head end-to-end design, new training components, and task-specific heads that reports improved mAP-latency tradeoffs on COCO and LVIS benchmarks across detection, segmentation, pose, and oriented detection.
UnAC improves LMM performance on visual reasoning benchmarks by combining adaptive visual prompting, image abstraction, and gradual self-checking.
citing papers explorer
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COCOTree: A Dataset and Benchmark for Open Tree-Structured Visual Decomposition
COCOTree is a 21K-image benchmark with 1.8M nodes and an OTQ metric for the new task of open tree-structured visual decomposition.
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Vision Harnessing Agent for Open Ad-hoc Segmentation
VASA is a vision-guided agent for open ad-hoc segmentation that creates and validates masks through planning, tool use, and error recovery, outperforming baselines on the new PARS benchmark and RefCOCOm.
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Self-Correcting Text-to-Video Generation with Misalignment Detection and Localized Refinement
VideoRepair detects text-video misalignments via MLLM-generated questions and performs localized, region-preserving refinement to improve alignment in existing T2V diffusion models.
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Set-of-Mark Prompting Unleashes Extraordinary Visual Grounding in GPT-4V
Set-of-Mark prompting marks segmented image regions with alphanumerics and masks to let GPT-4V achieve state-of-the-art zero-shot results on referring expression comprehension and segmentation benchmarks like RefCOCOg.
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In-context Region-based Drag: Drag Any Region to Any Shape
ICRDrag performs region-based drag editing in diffusion models by feeding source image, source mask, and target mask into an in-context framework with image-mask attention consistency and source-target attention correspondence regularizations, plus a new Paired Region Dataset.
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EPS3D: End-to-End Feed-Forward 3D Panoptic Segmentation
EPS3D is an end-to-end architecture for 3D panoptic segmentation from multi-view images that uses distillation and semantic-instance mutual enhancement to achieve higher benchmark performance and speed than prior methods.
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Amodal SAM: A Unified Amodal Segmentation Framework with Generalization
Amodal SAM extends SAM with a Spatial Completion Adapter, Target-Aware Occlusion Synthesis for data, and consistency losses to reach SOTA amodal segmentation with strong generalization to new objects and scenes.
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MV3DIS: Multi-View Mask Matching via 3D Guides for Zero-Shot 3D Instance Segmentation
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.
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Personalization Toolkit: Training Free Personalization of Large Vision Language Models
Presents a training-free personalization toolkit for LVLMs that extracts features via vision foundation models, applies RAG for instance retrieval, and uses visual prompting for multi-concept adaptation on images and videos, claiming SOTA results on a new real-world benchmark.
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Ultralytics YOLO26: Unified Real-Time End-to-End Vision Models
YOLO26 presents a unified real-time vision model family with dual-head end-to-end design, new training components, and task-specific heads that reports improved mAP-latency tradeoffs on COCO and LVIS benchmarks across detection, segmentation, pose, and oriented detection.
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UnAC: Adaptive Visual Prompting with Abstraction and Stepwise Checking for Complex Multimodal Reasoning
UnAC improves LMM performance on visual reasoning benchmarks by combining adaptive visual prompting, image abstraction, and gradual self-checking.