Search Results for author:
Found 8 papers, 5 papers with code
LLM-Optic: Unveiling the Capabilities of Large Language Models for Universal Visual Grounding
LLM-Optic first employs an LLM as a Text Grounder to interpret complex text queries and accurately identify objects the user intends to locate.
SG-Adapter: Enhancing Text-to-Image Generation with Scene Graph Guidance
In this paper, we introduce the Scene Graph Adapter(SG-Adapter), leveraging the structured representation of scene graphs to rectify inaccuracies in the original text embeddings.
X-Ray: A Sequential 3D Representation for Generation
In this paper, we introduce X-Ray, an innovative approach to 3D generation that employs a new sequential representation, drawing inspiration from the depth-revealing capabilities of X-Ray scans to meticulously capture both the external and internal features of objects.
Decompose and Realign: Tackling Condition Misalignment in Text-to-Image Diffusion Models
The ``Decompose'' phase separates conditions based on pair relationships, computing the result individually for each pair.
Ref-NeuS: Ambiguity-Reduced Neural Implicit Surface Learning for Multi-View Reconstruction with Reflection
We show that together with a reflection direction-dependent radiance, our model achieves high-quality surface reconstruction on reflective surfaces and outperforms the state-of-the-arts by a large margin.
Text-Adaptive Multiple Visual Prototype Matching for Video-Text Retrieval
To address this problem, we propose a Text-Adaptive Multiple Visual Prototype Matching model, which automatically captures multiple prototypes to describe a video by adaptive aggregation of video token features.
Camera-Conditioned Stable Feature Generation for Isolated Camera Supervised Person Re-IDentification
To learn camera-view invariant features for person Re-IDentification (Re-ID), the cross-camera image pairs of each person play an important role.
Cross-Camera Feature Prediction for Intra-Camera Supervised Person Re-identification across Distant Scenes
To learn camera-invariant representation from cross-camera unpaired training data, we propose a cross-camera feature prediction method to mine cross-camera self supervision information from camera-specific feature distribution by transforming fake cross-camera positive feature pairs and minimize the distances of the fake pairs.
