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20182026
most citedDynamic Distillation Network for Cross-Domain Few-Shot Recognition with Unlabeled Data

10 citations · 17 across the 10 of their papers we have counts for

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14 papers · 1 filter

cs.CV2026

ECA: Efficient Continual Alignment for Open-Ended Image-to-Text Generation

Jiangtao Kong, Peijun Zhao, Chun-Fu Chen +4

Incremental Learning (IL) for Open-ended Image-to-Text Generation (OpenITG) enables models to continuously generate accurate, contextually relevant text for new images while preser…

cs.CV2022

Temporal Relevance Analysis for Video Action Models

Quanfu Fan, Donghyun Kim, Chun-Fu +4

In this paper, we provide a deep analysis of temporal modeling for action recognition, an important but underexplored problem in the literature. We first propose a new approach to…

cs.CV20213 cited

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…

cs.CV202110 cited

Dynamic Distillation Network for Cross-Domain Few-Shot Recognition with Unlabeled Data

Ashraful Islam, Chun-Fu Chen, Rameswar Panda +3

Most existing works in few-shot learning rely on meta-learning the network on a large base dataset which is typically from the same domain as the target dataset. We tackle the prob…

cs.CV2021

AdaMML: Adaptive Multi-Modal Learning for Efficient Video Recognition

Rameswar Panda, Chun-Fu Chen, Quanfu Fan +4

Multi-modal learning, which focuses on utilizing various modalities to improve the performance of a model, is widely used in video recognition. While traditional multi-modal learni…

cs.CV2021

Detector-Free Weakly Supervised Grounding by Separation

Assaf Arbelle, Sivan Doveh, Amit Alfassy +14

Nowadays, there is an abundance of data involving images and surrounding free-form text weakly corresponding to those images. Weakly Supervised phrase-Grounding (WSG) deals with th…