Knowledge catalogue
arXiv:2606.05587v1 Announce Type: new Abstract: Multi-object tracking (MOT) from UAV imagery presents unique challenges: altitude varies across sequences, objects are small and densely packed, and fre
arXiv:2506.20263v2 Announce Type: replace Abstract: Few-shot fine-grained image classification (FS-FGIC) is challenging as it requires distinguishing visually similar subclasses with extremely limited
arXiv:2606.06390v1 Announce Type: new Abstract: Indoor scene generation is crucial for robot simulation and modern interior design. However, complex layouts together with scarce 3D scene data make lea
arXiv:2606.05458v1 Announce Type: new Abstract: Automated detection of equine facial action units (AUs) is a promising yet under-explored avenue for pain and affective state assessment in horses. Half
arXiv:2606.06100v1 Announce Type: new Abstract: Vision-Language Models (VLMs) struggle with compositional reasoning that requires understanding inter-object relationships. A natural remedy is to injec
arXiv:2606.05769v1 Announce Type: new Abstract: Video event prediction (VEP) requires models to infer unobserved future states from partial video evidence. Existing video MLLMs usually verbalize inter
arXiv:2603.17310v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) with extended reasoning capabilities often generate verbose and redundant reasoning traces, incurring unnecessary
arXiv:2606.05561v1 Announce Type: new Abstract: Speech-based mental health screening offers scalable depression detection, yet clinical deployment faces a significant barrier: users' privacy concerns
arXiv:2606.05716v1 Announce Type: new Abstract: Style representation learning is a powerful tool for authorship analysis and modeling writing style, yet the latent nature of learned representations ma
arXiv:2606.06098v1 Announce Type: new Abstract: Foundational Large Language Models (LLMs) demonstrate proficiency on a wide range of general tasks, and achieve remarkable results on various specialize
arXiv:2606.06078v1 Announce Type: new Abstract: Autoregressive (AR) image generation models are highly expressive but computationally intensive, motivating effective model compression. Knowledge disti
arXiv:2512.21218v2 Announce Type: replace Abstract: While Large Multimodal Models (LMMs) have made significant progress, they remain largely text-centric, relying on language as their core reasoning m
arXiv:2606.05501v1 Announce Type: new Abstract: The estimation of odometry in legged robots depends on the assumption that the velocity of the foot with respect to the world remains zero during the st
arXiv:2606.05422v1 Announce Type: new Abstract: Learning from demonstratins (LfD) is usually performed over Euclidean spaces, while the robot state, e.g. orientation, naturally evolves over curved spa
arXiv:2606.06178v1 Announce Type: cross Abstract: Large language models (LLMs) present a trade-off between performance and cost, where more powerful models incur greater expense. LLM routing aims to m
arXiv:2606.06320v1 Announce Type: cross Abstract: Machine unlearning aims to remove targeted knowledge from a trained model while preserving its general capabilities. For autoregressive language model
arXiv:2606.06048v1 Announce Type: new Abstract: Pathological gait datasets remain scarce due to privacy, recruitment, cost, and movement variability. Our work presents a multimodal LLM-guided framewor
arXiv:2502.14145v3 Announce Type: replace Abstract: Achieving full-duplex communication in spoken dialogue systems (SDS) requires real-time coordination between listening, speaking, and thinking. This
arXiv:2606.06286v1 Announce Type: new Abstract: Large language models can reproduce training data, but existing memorization evaluations mostly measure whether models can be forced to do so, rather th
arXiv:2606.06267v1 Announce Type: new Abstract: Circuit discovery methods identify subgraphs that explain specific model behaviors, and structural differences between discovered circuits are commonly
arXiv:2606.05494v1 Announce Type: new Abstract: Automatic text summarization has become increasingly important due to the rapid growth of digital textual information. This paper presents a Multi-Model
arXiv:2606.05970v1 Announce Type: new Abstract: Large language models are increasingly used for structured extraction from clinical free-text notes, but the sensitivity of their output to upstream con
arXiv:2606.05917v1 Announce Type: cross Abstract: Long-video question answering remains challenging for Vision-Language Models (VLMs), as answer-relevant evidence is often sparse, transient, and tempo
arXiv:2606.05581v1 Announce Type: cross Abstract: Intrinsic methods fill the default toolbox for geometry processing on meshes. Intrinsic operators, in particular the Laplacian, underlie methods that
arXiv:2606.06245v1 Announce Type: new Abstract: Vision-Language-Action (VLA) policies remain brittle in long-horizon and high-uncertainty control, where one-pass action decoding provides limited infer
arXiv:2606.06103v1 Announce Type: new Abstract: Medical image segmentation is often framed as a search for stronger architectures, but this can obscure a more fundamental question: what does the datas
arXiv:2606.05181v1 Announce Type: new Abstract: Natural Language Inference (NLI) is a fundamental task in natural language understanding that requires determining the logical relationship between a pr
arXiv:2606.05641v1 Announce Type: new Abstract: Reliable crack assessment requires not only accurate pixel-level masks but also connected crack geometry and confidence estimates that remain stable und
arXiv:2606.06065v1 Announce Type: new Abstract: Second-language (L2) speech recognition often requires transcriptions of pronunciations and intended meanings. Multi-task learning (MTL) is a natural ap
arXiv:2606.05444v1 Announce Type: new Abstract: Coreference resolution is a core NLP task, having a broad range of downstream applications, e.g.~machine translation, question answering, document summa
arXiv:2606.05545v1 Announce Type: new Abstract: The development of multilingual Alzheimer's Disease Dementia (AD) detection models presents significant challenges due to the resource-intensive and tim
arXiv:2606.05997v1 Announce Type: new Abstract: We present the AILS-NTUA submission to the EXIST 2026 Lab at CLEF, addressing multimodal sexism identification and characterization in memes (Task 2) an
arXiv:2606.05724v1 Announce Type: new Abstract: Long-form narrative QA requires reasoning over evolving story worlds rather than isolated passages: answers may depend on earlier goals, changing charac
arXiv:2606.05785v1 Announce Type: new Abstract: Real-Time License Plate Detection and Recognition (LPDR) forms the backbone of modern smart cities. Although the YOLOV5-PDLPR model substantially improv
arXiv:2606.05460v1 Announce Type: new Abstract: Abdominal CT disease classification is challenging because each scan is a large 3D volume with many possible findings, while diagnostic evidence is ofte
arXiv:2606.06485v1 Announce Type: new Abstract: Recent advances in 3D multimodal large language models (3D-MLLMs) have enabled unified solutions for 3D scene understanding tasks, including visual ques
arXiv:2606.05703v1 Announce Type: new Abstract: Autoregressive (AR) models have demonstrated remarkable performance in generating high-fidelity images. However, their inherently sequential next-token
arXiv:2510.26236v2 Announce Type: replace Abstract: Motion imitation is a promising approach for humanoid locomotion, enabling agents to acquire humanlike behaviors. Existing methods typically rely on
arXiv:2606.06361v1 Announce Type: new Abstract: Image-to-Video diffusion models leverage input images to generate visually stunning content, yet frequently produce motion that violates physical laws.
arXiv:2606.05610v1 Announce Type: new Abstract: The efficacy of continued pre-training for Large Language Models (LLMs) hinges upon hyperparameter configurations, such as learning rate and batch size.
arXiv:2606.05663v1 Announce Type: new Abstract: Conventional multirotors suffer from a rapid collapse of attainable wrench space (AWS) under abrupt total rotor failures, rendering full 6-DOF recovery
arXiv:2606.06033v1 Announce Type: new Abstract: Learning dexterous manipulation requires demonstrations that preserve fine hand-object interactions while remaining executable at deployment. Existing p
arXiv:2606.06428v1 Announce Type: new Abstract: Prior work has shown that large language models (LLMs) can translate unseen or low-resource languages by undergoing continued training or even by encodi
arXiv:2601.02880v2 Announce Type: replace-cross Abstract: Every existing inference-time reasoning framework discards all failure context at problem boundaries, leaving a model solving problem 500 no w
arXiv:2606.05858v1 Announce Type: new Abstract: Recent advances in Large Language Models (LLMs) have opened new avenues for generating training-free text embeddings. However, the causal attention in d
arXiv:2606.06443v1 Announce Type: new Abstract: Large language models are increasingly used to simulate social media users and infer how individuals may respond to online discussions. However, it rema
arXiv:2606.06176v1 Announce Type: new Abstract: Underwater Image Enhancement (UIE) is essential for mitigating degradations caused by water medium. Although learning-based methods have advanced signif
arXiv:2606.06228v1 Announce Type: new Abstract: Training-free image editing has recently attracted increasing attention due to its ability to modify real images using powerful pre-trained diffusion an
arXiv:2606.06199v1 Announce Type: new Abstract: Standard segmentation metrics such as Dice and Hausdorff distance measure geometric overlap but say nothing about whether a segmented surface is suitabl
arXiv:2606.05912v1 Announce Type: new Abstract: Modeling dynamic facial expressions using 3D Gaussian representations remains challenging due to their unstructured nature. Conventional Gaussian avatar
arXiv:2503.22929v3 Announce Type: replace Abstract: Face anti-spoofing (FAS) techniques aim to enhance the security of facial identity authentication by distinguishing authentic live faces from decept
arXiv:2306.09712v2 Announce Type: replace-cross Abstract: In reinforcement learning (RL), there are two major settings for interacting with the environment: online and offline. Online methods explore
arXiv:2601.22580v2 Announce Type: replace Abstract: The success of Large Language Models (LLMs) hinges on the stable training of deep Transformer architectures. A critical design choice is the placeme
arXiv:2605.05367v2 Announce Type: replace Abstract: Existing 3D sign language avatar reconstruction methods are developed and evaluated exclusively on Western sign languages, and no 3D parametric anno
arXiv:2606.05328v1 Announce Type: cross Abstract: Modern video diffusion models generate increasingly realistic and temporally coherent videos, motivating their use as candidate world simulators. Yet
arXiv:2606.06188v1 Announce Type: new Abstract: Recent work has sought to understand Large Language Models (LLMs) reasoning, yet a principled, model-intrinsic signal that captures its layer-wise reaso
arXiv:2606.05375v1 Announce Type: new Abstract: Optical coherence tomographic angiography (OCTA) is a powerful technique for imaging retinal microvasculature. However, acquiring reliable quantificatio
To be clear: there should not be a president of AI (let alone me 😅). Which is pretty much one point I make in the video. This reminder is about the content of the video, which is 3 years old, but ever
arXiv:2606.05846v1 Announce Type: new Abstract: Automatic Speech Recognition (ASR) has become a key technology for human--AI interaction. However, code-switching ASR (CS-ASR) remains particularly chal
arXiv:2602.03410v2 Announce Type: replace Abstract: Recent advances in large-scale diffusion models have intensified concerns about their potential misuse, particularly in generating realistic yet har