43 citations · 48 across the 8 of their papers we have counts for
8 papers
Koala: Key frame-conditioned long video-LLM
Reuben Tan, Ximeng Sun, Ping Hu +5
Long video question answering is a challenging task that involves recognizing short-term activities and reasoning about their fine-grained relationships. State-of-the-art video Lar…
CLAMP: Contrastive LAnguage Model Prompt-tuning
Piotr Teterwak, Ximeng Sun, Bryan A. Plummer +2
Large language models (LLMs) have emerged as powerful general-purpose interfaces for many machine learning problems. Recent work has adapted LLMs to generative visual tasks like im…
Label Budget Allocation in Multi-Task Learning
Ximeng Sun, Kihyuk Sohn, Kate Saenko +2
The cost of labeling data often limits the performance of machine learning systems. In multi-task learning, related tasks provide information to each other and improve overall perf…
DualCoOp++: Fast and Effective Adaptation to Multi-Label Recognition with Limited Annotations
Ping Hu, Ximeng Sun, Stan Sclaroff +1
Multi-label image recognition in the low-label regime is a task of great challenge and practical significance. Previous works have focused on learning the alignment between textual…
DIME-FM: DIstilling Multimodal and Efficient Foundation Models
Ximeng Sun, Pengchuan Zhang, Peizhao Zhang +3
Large Vision-Language Foundation Models (VLFM), such as CLIP, ALIGN and Florence, are trained on large-scale datasets of image-caption pairs and achieve superior transferability an…
DualCoOp: Fast Adaptation to Multi-Label Recognition with Limited Annotations
Ximeng Sun, Ping Hu, Kate Saenko
Solving multi-label recognition (MLR) for images in the low-label regime is a challenging task with many real-world applications. Recent work learns an alignment between textual an…
Dynamic Network Quantization for Efficient Video Inference
Ximeng Sun, Rameswar Panda, Chun-Fu Chen +3
Deep convolutional networks have recently achieved great success in video recognition, yet their practical realization remains a challenge due to the large amount of computational…
Improved Techniques for Quantizing Deep Networks with Adaptive Bit-Widths
Ximeng Sun, Rameswar Panda, Chun-Fu Chen +6
Quantizing deep networks with adaptive bit-widths is a promising technique for efficient inference across many devices and resource constraints. In contrast to static methods that…