13 papers
Agentic Context Learning with Self-Discovered Specification
Jike Zhong, Ming Li, Yuxiang Lai +8
Context learning is an emerging inference-time task where LLMs must learn and apply novel, task-specific knowledge from intricate contexts absent from pre-training; even frontier m…
Revisiting Model Stitching In the Foundation Model Era
Zheda Mai, Ke Zhang, Fu-En Wang +6
Model stitching, connecting early layers of one model (source) to later layers of another (target) via a light stitch layer, has served as a probe of representational compatibility…
AVA-Bench: Atomic Visual Ability Benchmark for Vision Foundation Models
Zheda Mai, Arpita Chowdhury, Zihe Wang +5
The rise of vision foundation models (VFMs) calls for systematic evaluation. A common approach pairs VFMs with large language models (LLMs) as general-purpose heads, followed by ev…
A Study of Failure Modes in Two-Stage Human-Object Interaction Detection
Lemeng Wang, Qinqian Lei, Vidhi Bakshi +8
Human-object interaction (HOI) detection aims to detect interactions between humans and objects in images. While recent advances have improved performance on existing benchmarks, t…
Lessons and Open Questions from a Unified Study of Camera-Trap Species Recognition Over Time
Sooyoung Jeon, Hongjie Tian, Lemeng Wang +7
Camera traps are vital for large-scale biodiversity monitoring, yet accurate automated analysis remains challenging due to diverse deployment environments. While the computer visio…
Continual Unlearning for Text-to-Image Diffusion Models: A Regularization Perspective
Justin Lee, Zheda Mai, Jinsu Yoo +3
Machine unlearning--the ability to remove designated concepts from a pre-trained model--has advanced rapidly, particularly for text-to-image diffusion models. However, existing met…