Uncertainty Quantification for In-Context Learning of Large Language Models

In-context learning has emerged as a groundbreaking ability of Large Language Models (LLMs) and revolutionized various fields by providing a few task-relevant demonstrations in the prompt. However, trustworthy issues with LLM’s response, such as hallucination, have also been actively discussed. Existing works have been devoted to quantifying the uncertainty in LLM’s response, but they often overlook the complex nature of LLMs and the uniqueness of in-context learning. In this work, we delve into the predictive uncertainty of LLMs associated with in-context learning, highlighting that such uncertainties may stem from both the provided demonstrations (aleatoric uncertainty) and ambiguities tied to the model’s configurations (epistemic uncertainty). We propose a novel formulation and corresponding estimation method to quantify both types of uncertainties. The proposed method offers an unsupervised way to understand the prediction of in-context learning in a plug-and-play fashion. Extensive experiments are conducted to demonstrate the effectiveness of the decomposition. The code and data are available at: https://github.com/lingchen0331/UQ_ICL.

Pruning as a Domain-specific LLM Extractor

Large Language Models (LLMs) have exhibited remarkable proficiency across a wide array of NLP tasks. However, the escalation in model size also engenders substantial deployment costs. While few efforts have explored model pruning techniques to reduce the size of LLMs, they mainly center on general or task-specific weights. This leads to suboptimal performance due to lacking specificity on the target domain or generality on different tasks when applied to domain-specific challenges. This work introduces an innovative unstructured dual-pruning methodology, D-PRUNER, for domain-specific compression on LLM. It extracts a compressed, domain-specific, and task agnostic LLM by identifying LLM weights that are pivotal for general capabilities, like linguistic capability and multi-task solving, and domain-specific knowledge. More specifically, we first assess general weight importance by quantifying the error incurred upon their removal with the help of an open-domain calibration dataset. Then, we utilize this general weight importance to refine the training loss, so that it preserves generality when fitting into a specific domain. Moreover, by efficiently approximating weight importance with the refined training loss on a domain-specific calibration dataset, we obtain a pruned model emphasizing generality and specificity. Our comprehensive experiments across various tasks in healthcare and legal domains show the effectiveness of D-PRUNER in domain-specific compression. Our code is available at https: //github.com/psunlpgroup/D-Pruner.

Weakly-Supervised Temporal Action Localization with Multi-Modal Plateau Transformers

Weakly Supervised Temporal Action Localization (WSTAL) aims to jointly localize and classify action segments in untrimmed videos with only video level annotations. To leverage video level annotations most existing methods adopt the multiple instance learning paradigm where frame/snippet level action predictions are first produced and then aggregated to form a video-level prediction. Although there are trials to improve snippet-level predictions by modeling temporal relationships we argue that those implementations have not sufficiently exploited such information. In this paper we propose Multi Modal Plateau Transformers (M2PT) for WSTAL by simultaneously exploiting temporal relationships among snippets complementary information across data modalities and temporal coherence among consecutive snippets. Specifically M2PT explores a dual Transformer architecture for RGB and optical flow modalities which models intra modality temporal relationship with a self attention mechanism and inter modality temporal relationship with a cross attention mechanism. To capture the temporal coherence that consecutive snippets are supposed to be assigned with the same action M2PT deploys a Plateau model to refine the temporal localization of action segments. Experimental results on popular benchmarks demonstrate that our proposed M2PT achieves state of the art performance.

Taming Self-Training for Open-Vocabulary Object Detection

Recent studies have shown promising performance in open-vocabulary object detection (OVD) by utilizing pseudo labels (PLs) from pretrained vision and language models (VLMs). However, teacher-student self-training, a powerful and widely used paradigm to leverage PLs, is rarely explored for OVD.

Seeing the Vibration from Fiber-Optic Cables: Rain Intensity Monitoring using Deep Frequency Filtering

The various sensing technologies such as cameras LiDAR radar and satellites with advanced machine learning models offers a comprehensive approach to environmental perception and understanding. This paper introduces an innovative Distributed Fiber Optic Sensing (DFOS) technology utilizing the existing telecommunication infrastructure networks for rain intensity monitoring. DFOS enables a novel way to monitor weather condition and environmental changes provides real-time continuous and precise measurements over large areas and delivers comprehensive insights beyond the visible spectrum. We use rain intensity as an example to demonstrate the sensing capabilities of DFOS system. To enhance the rain sensing performance we introduce a Deep Phase-Magnitude Network (DFMN) divide the raw sensing data into phase and magnitude component allowing targeted feature learning on each component independently. Furthermore we propose a Phase Frequency learnable filter (PFLF) for the phase component filtering and conduct standard convolution layers on the magnitude component leveraging the inherent physical properties of optical fiber sensing. We formulate the phase-magnitude channel into a parallel network and subsequently fuse the features for a comprehensive analysis in the end. Experimental results on the collected fiber sensing data show that the proposed method performs favorably against the state-of-the-art approaches.

Improving the Efficiency-Accuracy Trade-off of DETR-Style Models in Practice

This report aims to provide a comprehensive view on the inference efficiency of DETR-style detection models. We provide the effect of the basic efficiency techniques and identify the factors that are easily applicable yet effectively improve the efficiency-accuracy trade-off. Specifically, we explore the effect of input resolution, multi-scale feature enhancement, and backbone pre-training. Our experiments support that 1) improving the detection accuracy for smaller objects while minimizing the increase in inference cost is a good strategy to achieve a better trade-off between accuracy and efficiency. 2) Multi-scale feature enhancement can be lightened with marginal accuracy loss and 3) improved backbone pre-training can further enhance the trade-off.

Why Not Use Your Textbook? Knowledge-Enhanced Procedure Planning of Instructional Videos

In this paper we explore the capability of an agent to construct a logical sequence of action steps thereby assembling a strategic procedural plan. This plan is crucial for navigating from an initial visual observation to a target visual outcome as depicted in real-life instructional videos. Existing works have attained partial success by extensively leveraging various sources of information available in the datasets such as heavy intermediate visual observations procedural names or natural language step-by-step instructions for features or supervision signals. However the task remains formidable due to the implicit causal constraints in the sequencing of steps and the variability inherent in multiple feasible plans. To tackle these intricacies that previous efforts have overlooked we propose to enhance the agent’s capabilities by infusing it with procedural knowledge. This knowledge sourced from training procedure plans and structured as a directed weighted graph equips the agent to better navigate the complexities of step sequencing and its potential variations. We coin our approach KEPP a novel Knowledge-Enhanced Procedure Planning system which harnesses a probabilistic procedural knowledge graph extracted from training data effectively acting as a comprehensive textbook for the training domain. Experimental evaluations across three widely-used datasets under settings of varying complexity reveal that KEPP attains superior state-of-the-art results while requiring only minimal supervision. Code and trained model are available at https://github.com/Ravindu-Yasas-Nagasinghe/KEPP

Learning from Synthetic Human Group Activities

The study of complex human interactions and group activities has become a focal point in human-centric computer vision. However, progress in related tasks is often hindered by the challenges of obtaining large-scale labeled datasets from real-world scenarios. To address the limitation, we introduce M3Act, a synthetic data generator for multi-view multi-group multi-person human atomic actions and group activities. Powered by Unity Engine, M3Act features multiple semantic groups, highly diverse and photorealistic images, and a comprehensive set of annotations, which facilitates the learning of human-centered tasks across single-person, multi-person, and multi-group conditions. We demonstrate the advantages of M3Act across three core experiments. The results suggest our synthetic dataset can significantly improve the performance of several downstream methods and replace real-world datasets to reduce cost. Notably, M3Act improves the state-of-the-art MOTRv2 on DanceTrack dataset, leading to a hop on the leaderboard from 10t?h to 2n?d place. Moreover, M3Act opens new research for controllable 3D group activity generation. We define multiple metrics and propose a competitive baseline for the novel task. Our code and data are available at our project page: http://cjerry1243.github.io/M3Act.

ViTA: An Efficient Video-to-Text Algorithm using VLM for RAG-based Video Analysis System

Retrieval-augmented generation (RAG) is used in natural language processing (NLP) to provide query-relevant information in enterprise documents to large language models (LLMs). Such enterprise context enables the LLMs to generate more informed and accurate responses. When enterprise data is primarily videos AI models like vision language models (VLMs) are necessary to convert information in videos into text. While essential this conversion is a bottleneck especially for large corpus of videos. It delays the timely use of enterprise videos to generate useful responses. We propose ViTA a novel method that leverages two unique characteristics of VLMs to expedite the conversion process. As VLMs output more text tokens they incur higher latency. In addition large (heavyweight) VLMs can extract intricate details from images and videos but they incur much higher latency per output token when compared to smaller (lightweight) VLMs that may miss details. To expedite conversion ViTA first employs a lightweight VLM to quickly understand the gist or overview of an image or a video clip and directs a heavyweight VLM (through prompt engineering) to extract additional details by using only a few (preset number of) output tokens. Our experimental results show that ViTA expedites the conversion time by as much as 43% without compromising the accuracy of responses when compared to a baseline system that only uses a heavyweight VLM.

Deep Video Codec Control for Vision Models

Standardized lossy video coding is at the core of almost all real-world video processing pipelines. Rate control is used to enable standard codecs to adapt to different network bandwidth conditions or storage constraints. However standard video codecs (e.g. H.264) and their rate control modules aim to minimize video distortion w.r.t. human quality assessment. We demonstrate empirically that standard-coded videos vastly deteriorate the performance of deep vision models. To overcome the deterioration of vision performance this paper presents the first end-to-end learnable deep video codec control that considers both bandwidth constraints and downstream deep vision performance while adhering to existing standardization. We demonstrate that our approach better preserves downstream deep vision performance than traditional standard video coding.