93 citations · 97 across the 8 of their papers we have counts for
8 papers
ScaleHP: Scale-Mediated Optimization of Coupled Errors for Metric-Space Hand Pose Estimation
Ruitao Jing, Xingyu Chen, Hongyang Li +3
In this paper, we present ScaleHP, a unified framework that explicitly represents per-instance metric scale to resolve the coupled errors in calibrated camera-space hand pose estim…
SegDINO3D: 3D Instance Segmentation Empowered by Both Image-Level and Object-Level 2D Features
Jinyuan Qu, Hongyang Li, Xingyu Chen +5
In this paper, we present SegDINO3D, a novel Transformer encoder-decoder framework for 3D instance segmentation. As 3D training data is generally not as sufficient as 2D training i…
TAPTRv3: Spatial and Temporal Context Foster Robust Tracking of Any Point in Long Video
Jinyuan Qu, Hongyang Li, Shilong Liu +3
In this paper, built upon TAPTRv2, we present TAPTRv3. TAPTRv2 is a simple yet effective DETR-like point tracking framework that works fine in regular videos but tends to fail in l…
TAPTRv2: Attention-based Position Update Improves Tracking Any Point
Hongyang Li, Hao Zhang, Shilong Liu +5
In this paper, we present TAPTRv2, a Transformer-based approach built upon TAPTR for solving the Tracking Any Point (TAP) task. TAPTR borrows designs from DEtection TRansformer (DE…
TAPTR: Tracking Any Point with Transformers as Detection
Hongyang Li, Hao Zhang, Shilong Liu +4
In this paper, we propose a simple and strong framework for Tracking Any Point with TRansformers (TAPTR). Based on the observation that point tracking bears a great resemblance to…
Grounded SAM: Assembling Open-World Models for Diverse Visual Tasks
Tianhe Ren, Shilong Liu, Ailing Zeng +14
We introduce Grounded SAM, which uses Grounding DINO as an open-set object detector to combine with the segment anything model (SAM). This integration enables the detection and seg…
LLaVA-Grounding: Grounded Visual Chat with Large Multimodal Models
Hao Zhang, Hongyang Li, Feng Li +8
With the recent significant advancements in large multi-modal models (LMMs), the importance of their grounding capability in visual chat is increasingly recognized. Despite recent…
Visual In-Context Prompting
Feng Li, Qing Jiang, Hao Zhang +9
In-context prompting in large language models (LLMs) has become a prevalent approach to improve zero-shot capabilities, but this idea is less explored in the vision domain. Existin…