Search Results for author:
Found 26 papers, 7 papers with code
VideoOrion: Tokenizing Object Dynamics in Videos
We present VideoOrion, a Video Large Language Model (Video-LLM) that explicitly captures the key semantic information in videos--the spatial-temporal dynamics of objects throughout the videos.
From RNNs to Foundation Models: An Empirical Study on Commercial Building Energy Consumption
The impact of increasing dataset heterogeneity in time series forecasting, while keeping size and model constant, is understudied.
Pseudo-Probability Unlearning: Towards Efficient and Privacy-Preserving Machine Unlearning
Machine unlearning--enabling a trained model to forget specific data--is crucial for addressing biased data and adhering to privacy regulations like the General Data Protection Regulation (GDPR)'s "right to be forgotten".
FedDTPT: Federated Discrete and Transferable Prompt Tuning for Black-Box Large Language Models
To address these challenges, we propose for the first time a federated discrete and transferable prompt tuning, namely FedDTPT, for black-box large language models.
FedSpaLLM: Federated Pruning of Large Language Models
Large Language Models (LLMs) achieve state-of-the-art performance but are challenging to deploy due to their high computational and storage demands.
CogDevelop2K: Reversed Cognitive Development in Multimodal Large Language Models
To this end, we propose CogDevelop2K, a comprehensive benchmark that spans 12 sub-concepts from primitive knowledge like object permanence and boundary to more complex abilities like intentionality understanding, structured via the developmental trajectory of a human mind.
RIPPLECOT: Amplifying Ripple Effect of Knowledge Editing in Language Models via Chain-of-Thought In-Context Learning
This approach effectively guides the model through complex multi-hop questions with chains of related facts.
From Pixels to Tokens: Byte-Pair Encoding on Quantized Visual Modalities
Multimodal Large Language Models have made significant strides in integrating visual and textual information, yet they often struggle with effectively aligning these modalities.
Vision Language Models Know Law of Conservation without Understanding More-or-Less
Conservation is a critical milestone of cognitive development considered to be supported by both the understanding of quantitative concepts and the reversibility of mental operations.
Vision Language Models See What You Want but not What You See
Knowing others' intentions and taking others' perspectives are two core components of human intelligence typically considered as instantiations of theory of mind.
Probing Mechanical Reasoning in Large Vision Language Models
Mechanical reasoning is a fundamental ability that sets human intelligence apart from other animal intelligence.
ARVideo: Autoregressive Pretraining for Self-Supervised Video Representation Learning
This paper presents a new self-supervised video representation learning framework, ARVideo, which autoregressively predicts the next video token in a tailored sequence order.
Jailbreaking Prompt Attack: A Controllable Adversarial Attack against Diffusion Models
In this work, we investigate a more practical and universal attack that does not require the presence of a target model and demonstrate that the high-dimensional text embedding space inherently contains NSFW concepts that can be exploited to generate harmful images.
Can 3D Vision-Language Models Truly Understand Natural Language?
To test the language understandability of 3D-VL models, we first propose a language robustness task for systematically assessing 3D-VL models across various tasks, benchmarking their performance when presented with different language style variants.
Towards Adversarially Robust Dataset Distillation by Curvature Regularization
Extensive empirical experiments suggest that our method not only outperforms standard adversarial training on both accuracy and robustness with less computation overhead but is also capable of generating robust distilled datasets that can withstand various adversarial attacks.
Approximate Nullspace Augmented Finetuning for Robust Vision Transformers
In this work, we provide a finetuning approach to enhance the robustness of vision transformers inspired by the concept of nullspace from linear algebra.
Beyond Finite Data: Towards Data-free Out-of-distribution Generalization via Extrapolation
Starting with the class of interest, we query the LLMs to extract relevant knowledge for these novel domains.
SparseLLM: Towards Global Pruning for Pre-trained Language Models
The transformative impact of large language models (LLMs) like LLaMA and GPT on natural language processing is countered by their prohibitive computational demands.
Toward a Team of AI-made Scientists for Scientific Discovery from Gene Expression Data
For instance, it has facilitated the identification of disease-predictive genes from gene expression data, significantly advancing healthcare.
Dataset Distillation via the Wasserstein Metric
Dataset Distillation (DD) emerges as a powerful strategy to encapsulate the expansive information of large datasets into significantly smaller, synthetic equivalents, thereby preserving model performance with reduced computational overhead.
Choosing Wisely and Learning Deeply: Selective Cross-Modality Distillation via CLIP for Domain Generalization
SCMD leverages the capabilities of large vision-language models, specifically CLIP, to train a more efficient model, ensuring it acquires robust generalization capabilities across unseen domains.
Towards Understanding Adversarial Transferability in Federated Learning
We investigate a specific security risk in FL: a group of malicious clients has impacted the model during training by disguising their identities and acting as benign clients but later switching to an adversarial role.
Diverse Cotraining Makes Strong Semi-Supervised Segmentor
Deep co-training has been introduced to semi-supervised segmentation and achieves impressive results, yet few studies have explored the working mechanism behind it.
Multi-metrics adaptively identifies backdoors in Federated learning
To evaluate the effectiveness of our approach, we conduct comprehensive experiments on different datasets under various attack settings, where our method achieves the best defensive performance.
Consistent-Teacher: Towards Reducing Inconsistent Pseudo-targets in Semi-supervised Object Detection
In this study, we dive deep into the inconsistency of pseudo targets in semi-supervised object detection (SSOD).
More than Encoder: Introducing Transformer Decoder to Upsample
The local and detailed feature from the shallower layer such as boundary and tissue texture is particularly more important in medical segmentation compared with natural image segmentation.
