18 citations · 43 across the 11 of their papers we have counts for
12 papers
How Much Can We Prune? Exploring the Performance Limits of Visual Token Pruning
Janghoon Cho, Shubhankar Borse, Reza Pourreza +4
Visual token pruning is increasingly used to reduce the memory and inference costs of vision-language models. Existing work has largely focused on designing method-specific selecto…
References Indeed Matter? Reference-Free Preference Optimization for Conversational Query Reformulation
Doyoung Kim, Youngjun Lee, Joeun Kim +4
Conversational query reformulation (CQR) has become indispensable for improving retrieval in dialogue-based applications. However, existing approaches typically rely on reference p…
ReFeed: Multi-dimensional Summarization Refinement with Reflective Reasoning on Feedback
Taewon Yun, Jihwan Oh, Hyangsuk Min +4
Summarization refinement faces challenges when extending to multi-dimension. In this paper, we introduce ReFeed, a powerful summarization refinement pipeline that enhances multiple…
Adaptive Shortcut Debiasing for Online Continual Learning
Doyoung Kim, Dongmin Park, Yooju Shin +3
We propose a novel framework DropTop that suppresses the shortcut bias in online continual learning (OCL) while being adaptive to the varying degree of the shortcut bias incurred b…
One Size Fits All for Semantic Shifts: Adaptive Prompt Tuning for Continual Learning
Doyoung Kim, Susik Yoon, Dongmin Park +4
In real-world continual learning (CL) scenarios, tasks often exhibit intricate and unpredictable semantic shifts, posing challenges for fixed prompt management strategies which are…
Active Prompt Learning in Vision Language Models
Jihwan Bang, Sumyeong Ahn, Jae-Gil Lee
Pre-trained Vision Language Models (VLMs) have demonstrated notable progress in various zero-shot tasks, such as classification and retrieval. Despite their performance, because im…
Prompt-Guided Transformers for End-to-End Open-Vocabulary Object Detection
Hwanjun Song, Jihwan Bang
Prompt-OVD is an efficient and effective framework for open-vocabulary object detection that utilizes class embeddings from CLIP as prompts, guiding the Transformer decoder to dete…
Meta-Query-Net: Resolving Purity-Informativeness Dilemma in Open-set Active Learning
Dongmin Park, Yooju Shin, Jihwan Bang +3
Unlabeled data examples awaiting annotations contain open-set noise inevitably. A few active learning studies have attempted to deal with this open-set noise for sample selection b…
Online Continual Learning on a Contaminated Data Stream with Blurry Task Boundaries
Jihwan Bang, Hyunseo Koh, Seulki Park +3
Learning under a continuously changing data distribution with incorrect labels is a desirable real-world problem yet challenging. A large body of continual learning (CL) methods, h…
Rainbow Memory: Continual Learning with a Memory of Diverse Samples
Jihwan Bang, Heesu Kim, YoungJoon Yoo +2
Continual learning is a realistic learning scenario for AI models. Prevalent scenario of continual learning, however, assumes disjoint sets of classes as tasks and is less realisti…
Boosting Active Learning for Speech Recognition with Noisy Pseudo-labeled Samples
Jihwan Bang, Heesu Kim, YoungJoon Yoo +1
The cost of annotating transcriptions for large speech corpora becomes a bottleneck to maximally enjoy the potential capacity of deep neural network-based automatic speech recognit…
SINet: Extreme Lightweight Portrait Segmentation Networks with Spatial Squeeze Modules and Information Blocking Decoder
Hyojin Park, Lars Lowe Sjösund, YoungJoon Yoo +3
Designing a lightweight and robust portrait segmentation algorithm is an important task for a wide range of face applications. However, the problem has been considered as a subset…