1 citations · 2 across the 14 of their papers we have counts for
14 papers
SmoLSTM: A Compact Vision-Language-Action Model with Recurrent Memory that Persists
Jan-Gerrit Habekost, Parsa Mastouri Kashani, Connor Gäde +5
Vision-language-action models often predict actions from only the current observation, which can leave tasks involving object occlusion or visually identical objects ambiguous with…
Extraction and Analysis of Multimodal Concepts in Vision Language Models through Sparse Autoencoders
Sergio Lanza, Jae Hee Lee, Stefan Wermter
Vision Language Models (VLMs) have demonstrated impressive performance in tasks requiring joint understanding of images and text, such as image captioning and Visual Question Answe…
Learning to Hear Hesitation: Continual Learning for Disfluency-Aware ASR
Henri-Leon Kordt, Theresa Pekarek Rosin, Jae Hee Lee +1
Despite advances in large-scale Automatic Speech Recognition (ASR), disfluent speech remains challenging, as state-of-the-art systems are often optimized to omit disfluencies, lead…
Bias Leaves a Gradient Trail: Label-Free Bias Identification via Gradient Probes on Concept Decompositions
Thomas Vitry, Kieran Edgeworth, Stefan Wermter +1
Vision classifiers can exploit spurious correlations, achieving high in-distribution accuracy yet failing under distribution shift. Existing approaches to bias mitigation and analy…
The Expert Strikes Back: Interpreting Mixture-of-Experts Language Models at Expert Level
Jeremy Herbst, Stefan Wermter, Jae Hee Lee
Mixture-of-Experts (MoE) architectures have become the dominant choice for scaling Large Language Models (LLMs), activating only a subset of parameters per token. While MoE archite…
Explaining, Verifying, and Aligning Semantic Hierarchies in Vision-Language Model Embeddings
Gesina Schwalbe, Mert Keser, Moritz Bayerkuhnlein +8
Vision-language model (VLM) encoders such as CLIP enable strong retrieval and zero-shot classification in a shared image-text embedding space, yet the semantic organization of this…
Knowing the Facts but Choosing the Shortcut: Understanding How Large Language Models Compare Entities
Hans Hergen Lehmann, Jae Hee Lee, Steven Schockaert +1
Large Language Models (LLMs) are increasingly used for knowledge-based reasoning tasks, yet understanding when they rely on genuine knowledge versus superficial heuristics remains…
Concept-Based Explanations in Computer Vision: Where Are We and Where Could We Go?
Jae Hee Lee, Georgii Mikriukov, Gesina Schwalbe +2
Concept-based XAI (C-XAI) approaches to explaining neural vision models are a promising field of research, since explanations that refer to concepts (i.e., semantically meaningful…
Mental Modeling of Reinforcement Learning Agents by Language Models
Wenhao Lu, Xufeng Zhao, Josua Spisak +2
Can emergent language models faithfully model the intelligence of decision-making agents? Though modern language models exhibit already some reasoning ability, and theoretically ca…
Details Make a Difference: Object State-Sensitive Neurorobotic Task Planning
Xiaowen Sun, Xufeng Zhao, Jae Hee Lee +3
The state of an object reflects its current status or condition and is important for a robot's task planning and manipulation. However, detecting an object's state and generating a…
Causal State Distillation for Explainable Reinforcement Learning
Wenhao Lu, Xufeng Zhao, Thilo Fryen +4
Reinforcement learning (RL) is a powerful technique for training intelligent agents, but understanding why these agents make specific decisions can be quite challenging. This lack…
Read Between the Layers: Leveraging Multi-Layer Representations for Rehearsal-Free Continual Learning with Pre-Trained Models
Kyra Ahrens, Hans Hergen Lehmann, Jae Hee Lee +1
We address the Continual Learning (CL) problem, wherein a model must learn a sequence of tasks from non-stationary distributions while preserving prior knowledge upon encountering…
Visually Grounded Continual Language Learning with Selective Specialization
Kyra Ahrens, Lennart Bengtson, Jae Hee Lee +1
A desirable trait of an artificial agent acting in the visual world is to continually learn a sequence of language-informed tasks while striking a balance between sufficiently spec…
From Neural Activations to Concepts: A Survey on Explaining Concepts in Neural Networks
Jae Hee Lee, Sergio Lanza, Stefan Wermter
In this paper, we review recent approaches for explaining concepts in neural networks. Concepts can act as a natural link between learning and reasoning: once the concepts are iden…