14 accepted papers
Vision-Language Retrieval (VLR) aims to retrieve relevant visual or textual information from multimodal data using language or image queries. However, traditional VLR methods often rely on data-driven shallow semantic alignment and fail to understand the deeper structural and fine-grained entity fea
Vision-language retrieval (VLR), which uses text or image queries to retrieve corresponding cross-modal content, plays a crucial role in multimedia and computer vision tasks. However, challenging concepts in queries often confuse retrievers, limiting their ability to align concepts with visual conte
The ever-increasing computational demands of large language models (LLMs) make efficient inference a central challenge. While recent advances leverage specialized architectures or selective activation, they typically require (re)training or architectural modifications, limiting their broad applicabi…
From extracting features to generating text, the outputs of large language models (LLMs) typically rely on their final layers, following the conventional wisdom that earlier layers capture only low-level cues. However, our analysis shows that intermediate layers can encode even richer representation…
The online information ecosystem enables influence campaigns of unprecedented scale and impact. We urgently need empirically grounded approaches to counter the growing threat of malicious campaigns, now amplified by generative AI. But, developing defenses in real-world settings is impractical. Socia
Large Reasoning Models (LRMs) demonstrate strong performance on complex tasks through chain-of-thought (CoT) reasoning. However, they suffer from high inference latency due to lengthy reasoning chains. In this paper, we propose SpecCoT, a collaborative framework that combines large and small models
Videos are often used to learn or extract the necessary information to complete tasks in ways different than what text or static imagery can provide. However, many existing agent benchmarks neglect long-context video understanding, instead focus- ing on text or static image inputs. To bridge this ga…
Graphical User Interface (GUI) automation relies on accurate GUI grounding. However, obtaining large-scale, high-quality labeled data remains a key challenge, particularly in desktop environments like Windows Operating System (OS). Existing datasets primarily focus on structured web-based elements,…
Large language models (LLMs) show potential as computer agents, enhancing productivity and software accessibility in multi-modal tasks. However, measuring agent performance in sufficiently realistic and complex environments becomes increasingly challenging as: (i) most benchmarks are limited to sp…
Finetuning large language models (LLMs) has been empirically effective on a variety of downstream tasks. Existing approaches to finetuning an LLM either focus on parameter-efficient finetuning, which only updates a small number of trainable parameters, or attempt to reduce the memory footprint durin…
Many security applications require unsupervised anomaly detection, as malicious data are extremely rare and often only unlabeled normal data are available for training (i.e., zero-positive). However, security operators are concerned about the high stakes of trusting black-box models due to their lac…
Regular expressions (REs) offer one-shot solutions for many networking tasks, e.g., network intrusion detection. However, REs purely rely on expert knowledge and cannot utilize labeled data for better accuracy. Today, neural networks (NNs) have shown superior accuracy and flexibility, thanks to thei…
Works on implicit regularization have studied gradient trajectories during the optimization process to explain why deep networks favor certain kinds of solutions over others. In deep linear networks, it has been shown that gradient descent implicitly regularizes toward low-rank solutions on matrix c…
Local differential privacy (LDP), a technique applying unbiased statistical estimations instead of real data, is often adopted in data collection. In particular, this technique is used in frequency oracles (FO) because it can protect each user’s privacy and prevent leakage of sensitive information.…