Search Results for author:
Found 11 papers, 7 papers with code
Residual Objectness for Imbalance Reduction
For a long time, object detectors have suffered from extreme imbalance between foregrounds and backgrounds.
Is Heuristic Sampling Necessary in Training Deep Object Detectors?
In this paper, we challenge the necessity of such hard/soft sampling methods for training accurate deep object detectors.
Long-term Joint Scheduling for Urban Traffic
Recently, the traffic congestion in modern cities has become a growing worry for the residents.
Foreground-Background Imbalance Problem in Deep Object Detectors: A Review
Recent years have witnessed the remarkable developments made by deep learning techniques for object detection, a fundamentally challenging problem of computer vision.
DropIT: Dropping Intermediate Tensors for Memory-Efficient DNN Training
A standard hardware bottleneck when training deep neural networks is GPU memory.
AssistQ: Affordance-centric Question-driven Task Completion for Egocentric Assistant
In this paper, we define a new task called Affordance-centric Question-driven Task Completion, where the AI assistant should learn from instructional videos to provide step-by-step help in the user's view.
Affordance Grounding from Demonstration Video to Target Image
Humans excel at learning from expert demonstrations and solving their own problems.
Ranked #1 on
Video-to-image Affordance Grounding
on EPIC-Hotspot
AssistGPT: A General Multi-modal Assistant that can Plan, Execute, Inspect, and Learn
2) Flexible inputs and intermediate results.
UniVTG: Towards Unified Video-Language Temporal Grounding
Most methods in this direction develop taskspecific models that are trained with type-specific labels, such as moment retrieval (time interval) and highlight detection (worthiness curve), which limits their abilities to generalize to various VTG tasks and labels.
Ranked #3 on
Highlight Detection
on QVHighlights
(using extra training data)
Ego-Exo4D: Understanding Skilled Human Activity from First- and Third-Person Perspectives
We present Ego-Exo4D, a diverse, large-scale multimodal multiview video dataset and benchmark challenge.
Bootstrapping SparseFormers from Vision Foundation Models
In this paper, we propose to bootstrap SparseFormers from ViT-based vision foundation models in a simple and efficient way.
