Search Results for author:
Found 13 papers, 10 papers with code
SHERL: Synthesizing High Accuracy and Efficient Memory for Resource-Limited Transfer Learning
Parameter-efficient transfer learning (PETL) has emerged as a flourishing research field for adapting large pre-trained models to downstream tasks, greatly reducing trainable parameters while grappling with memory challenges during fine-tuning.
LocCa: Visual Pretraining with Location-aware Captioners
In this paper, we propose a simple visual pretraining method with location-aware captioners (LocCa).
Animate Your Motion: Turning Still Images into Dynamic Videos
For the first time, we integrate both semantic and motion cues within a diffusion model for video generation, as demonstrated in Fig 1.
Exploiting CLIP for Zero-shot HOI Detection Requires Knowledge Distillation at Multiple Levels
In this paper, we investigate the task of zero-shot human-object interaction (HOI) detection, a novel paradigm for identifying HOIs without the need for task-specific annotations.
Human-Object Interaction Detection
Knowledge Distillation
+2
UniPT: Universal Parallel Tuning for Transfer Learning with Efficient Parameter and Memory
Parameter-efficient transfer learning (PETL), i. e., fine-tuning a small portion of parameters, is an effective strategy for adapting pre-trained models to downstream domains.
A Study of Autoregressive Decoders for Multi-Tasking in Computer Vision
A key finding is that a small decoder learned on top of a frozen pretrained encoder works surprisingly well.
Weakly-supervised HOI Detection via Prior-guided Bi-level Representation Learning
Human object interaction (HOI) detection plays a crucial role in human-centric scene understanding and serves as a fundamental building-block for many vision tasks.
Human-Object Interaction Detection
Knowledge Distillation
+3
Unsupervised Vision-Language Grammar Induction with Shared Structure Modeling
We introduce a new task, unsupervised vision-language (VL) grammar induction.
Single Image 3D Object Estimation with Primitive Graph Networks
To address those challenges, we adopt a primitive-based representation for 3D object, and propose a two-stage graph network for primitive-based 3D object estimation, which consists of a sequential proposal module and a graph reasoning module.
Bipartite Graph Network with Adaptive Message Passing for Unbiased Scene Graph Generation
Scene graph generation is an important visual understanding task with a broad range of vision applications.
Learning Cross-modal Context Graph for Visual Grounding
To address their limitations, this paper proposes a language-guided graph representation to capture the global context of grounding entities and their relations, and develop a cross-modal graph matching strategy for the multiple-phrase visual grounding task.
Pose-aware Multi-level Feature Network for Human Object Interaction Detection
Reasoning human object interactions is a core problem in human-centric scene understanding and detecting such relations poses a unique challenge to vision systems due to large variations in human-object configurations, multiple co-occurring relation instances and subtle visual difference between relation categories.
