The Segment Anything Model (SAM) marks a notable milestone in segmentation models highlighted by its robust zero-shot capabilities and ability to handle diverse prompts. SAM follows a pipeline that separates interactive segmentation into image preprocessing through a large encoder and interactive inference via a lightweight decoder ensuring efficient real-time performance. However SAM faces stability issues in challenging samples upon this pipeline. These issues arise from two main factors. Firstly the image preprocessing disables SAM to dynamically use image-level zoom-in strategies to refocus on the target object during interaction. Secondly the lightweight decoder struggles to sufficiently integrate interactive information with image embeddings. To address these two limitations we propose FocSAM with a pipeline redesigned on two pivotal aspects. First we propose Dynamic Window Multi-head Self-Attention (Dwin-MSA) to dynamically refocus SAM's image embeddings on the target object. Dwin-MSA localizes attention computations around the target object enhancing object-related embeddings with minimal computational overhead. Second we propose Pixel-wise Dynamic ReLU (P-DyReLU) to enable sufficient integration of interactive information from a few initial clicks that have significant impacts on the overall segmentation results. Experimentally FocSAM augments SAM's interactive segmentation performance to match the existing state-of-the-art method in segmentation quality requiring only about 5.6% of this method's inference time on CPUs. Code is available at https://github.com/YouHuang67/focsam.
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Teeth-SEG: An Efficient Instance Segmentation Framework for Orthodontic Treatment based on Multi-Scale Aggregation and Anthropic Prior Knowledge
PDF ↗Teeth localization segmentation and labeling in 2D images have great potential in modern dentistry to enhance dental diagnostics treatment planning and population-based studies on oral health. However general instance segmentation frameworks are incompetent due to 1) the subtle differences between some teeth' shapes (e.g. maxillary first premolar and second premolar) 2) the teeth's position and shape variation across subjects and 3) the presence of abnormalities in the dentition (e.g. caries and edentulism). To address these problems we propose a ViT-based framework named TeethSEG which consists of stacked Multi-Scale Aggregation (MSA) blocks and an Anthropic Prior Knowledge (APK) layer. Specifically to compose the two modules we design 1) a unique permutation-based upscaler to ensure high efficiency while establishing clear segmentation boundaries with 2) multi-head self/cross-gating layers to emphasize particular semantics meanwhile maintaining the divergence between token embeddings. Besides we collect 3) the first open-sourced intraoral image dataset IO150K which comprises over 150k intraoral photos and all photos are annotated by orthodontists using a human-machine hybrid algorithm. Experiments on IO150K demonstrate that our TeethSEG outperforms the state-of-the-art segmentation models on dental image segmentation.
Segment Anything Model (SAM) has emerged as a powerful tool for numerous vision applications. A key component that drives the impressive performance for zero-shot transfer and high versatility is a super large Transformer model trained on the extensive high-quality SA-1B dataset. While beneficial the huge computation cost of SAM model has limited its applications to wider real-world applications. To address this limitation we propose EfficientSAMs light-weight SAM models that exhibits decent performance with largely reduced complexity. Our idea is based on leveraging masked image pretraining SAMI which learns to reconstruct features from SAM image encoder for effective visual representation learning. Further we take SAMI-pretrained light-weight image encoders and mask decoder to build EfficientSAMs and finetune the models on SA-1B for segment anything task. We perform evaluations on multiple vision tasks including image classification object detection instance segmentation and semantic segmentation and find that our proposed pretraining method SAMI consistently outperforms other masked image pretraining methods. On segment anything task such as zero-shot instance segmentation our EfficientSAMs with SAMI-pretrained lightweight image encoders perform favorably with a significant gain (e.g. 4 AP on COCO/LVIS) over other fast SAM models. Our EfficientSAM code and models are available at https://github.com/yformer/EfficientSAM.
Data replay is a successful incremental learning technique for images. It prevents catastrophic forgetting by keeping a reservoir of previous data original or synthesized to ensure the model retains past knowledge while adapting to novel concepts. However its application in the video domain is rudimentary as it simply stores frame exemplars for action recognition. This paper presents the first exploration of video data replay techniques for incremental action segmentation focusing on action temporal modeling. We propose a Temporally Coherent Action (TCA) model which represents actions using a generative model instead of storing individual frames. The integration of a conditioning variable that captures temporal coherence allows our model to understand the evolution of action features over time. Therefore action segments generated by TCA for replay are diverse and temporally coherent. In a 10-task incremental setup on the Breakfast dataset our approach achieves significant increases in accuracy for up to 22% compared to the baselines.
This paper studies open-vocabulary segmentation (OVS) through calibrating in-vocabulary and domain-biased embedding space with generalized contextual prior of CLIP. As the core of open-vocabulary understanding alignment of visual content with the semantics of unbounded text has become the bottleneck of this field. To address this challenge recent works propose to utilize CLIP as an additional classifier and aggregate model predictions with CLIP classification results. Despite their remarkable progress performance of OVS methods in relevant scenarios is still unsatisfactory compared with supervised counterparts. We attribute this to the in-vocabulary embedding and domain-biased CLIP prediction. To this end we present a Semantic-assisted CAlibration Network (SCAN). In SCAN we incorporate generalized semantic prior of CLIP into proposal embedding to avoid collapsing on known categories. Besides a contextual shift strategy is applied to mitigate the lack of global context and unnatural background noise. With above designs SCAN achieves state-of-the-art performance on all popular open-vocabulary segmentation benchmarks. Furthermore we also focus on the problem of existing evaluation system that ignores semantic duplication across categories and propose a new metric called Semantic-Guided IoU (SG-IoU).
When editing a video a piece of attractive background music is indispensable. However video background music generation tasks face several challenges for example the lack of suitable training datasets and the difficulties in flexibly controlling the music generation process and sequentially aligning the video and music. In this work we first propose a high-quality music-video dataset BGM909 with detailed annotation and shot detection to provide multi-modal information about the video and music. We then present evaluation metrics to assess music quality including music diversity and alignment between music and video with retrieval precision metrics. Finally we propose the Diff-BGM framework to automatically generate the background music for a given video which uses different signals to control different aspects of the music during the generation process i.e. uses dynamic video features to control music rhythm and semantic features to control the melody and atmosphere. We propose to align the video and music sequentially by introducing a segment-aware cross-attention layer. Experiments verify the effectiveness of our proposed method. The code and models are available at https://github.com/sizhelee/Diff-BGM.
Fusing Personal and Environmental Cues for Identification and Segmentation of First-Person Camera Wearers in Third-Person Views
PDF ↗As wearable cameras become more popular an important question emerges: how to identify camera wearers within the perspective of conventional static cameras. The drastic difference between first-person (egocentric) and third-person (exocentric) camera views makes this a challenging task. We present PersonEnvironmentNet (PEN) a framework designed to integrate information from both the individuals in the two views and geometric cues inferred from the background environment. To facilitate research in this direction we also present TF2023 a novel dataset comprising synchronized first-person and third-person views along with masks of camera wearers and labels associating these masks with the respective first-person views. In addition we propose a novel quantitative metric designed to measure a model's ability to comprehend the relationship between the two views. Our experiments reveal that PEN outperforms existing methods. The code and dataset are available at https://github.com/ziweizhao1993/PEN.
In recent interactive segmentation algorithms previous probability maps are used as network input to help predictions in the current segmentation round. However despite the utilization of previous masks useful information contained in the probability maps is not well propagated to the current predictions. In this paper to overcome this limitation we propose a novel and effective algorithm for click-based interactive image segmentation called MFP which attempts to make full use of probability maps. We first modulate previous probability maps to enhance their representations of user-specified objects. Then we feed the modulated probability maps as additional input to the segmentation network. We implement the proposed MFP algorithm based on the ResNet-34 HRNet-18 and ViT-B backbones and assess the performance extensively on various datasets. It is demonstrated that MFP meaningfully outperforms the existing algorithms using identical backbones. The source codes are available at https://github.com/cwlee00/MFP.
GoodSAM: Bridging Domain and Capacity Gaps via Segment Anything Model for Distortion-aware Panoramic Semantic Segmentation
PDF ↗This paper tackles a novel yet challenging problem: how to transfer knowledge from the emerging Segment Anything Model (SAM) -- which reveals impressive zero-shot instance segmentation capacity -- to learn a compact panoramic semantic segmentation model i.e. student without requiring any labeled data. This poses considerable challenges due to SAM's inability to provide semantic labels and the large capacity gap between SAM and the student. To this end we propose a novel framework called GoodSAM that introduces a teacher assistant (TA) to provide semantic information integrated with SAM to generate ensemble logits to achieve knowledge transfer. Specifically we propose a Distortion-Aware Rectification (DAR) module that first addresses the distortion problem of panoramic images by imposing prediction-level consistency and boundary enhancement. This subtly enhances TA's prediction capacity on panoramic images. DAR then incorporates a cross-task complementary fusion block to adaptively merge the predictions of SAM and TA to obtain more reliable ensemble logits. Moreover we introduce a Multi-level Knowledge Adaptation (MKA) module to efficiently transfer the multi-level feature knowledge from TA and ensemble logits to learn a compact student model. Extensive experiments on two benchmarks show that our GoodSAM achieves a remarkable +3.75% mIoU improvement over the state-of-the-art (SOTA) domain adaptation methods e.g. [41]. Also our most lightweight model achieves comparable performance to the SOTA methods with only 3.7M parameters.
Unsupervised video object segmentation aims to segment the most prominent object in a video sequence. However the existence of complex backgrounds and multiple foreground objects make this task challenging. To address this issue we propose a guided slot attention network to reinforce spatial structural information and obtain better foreground-background separation. The foreground and background slots which are initialized with query guidance are iteratively refined based on interactions with template information. Furthermore to improve slot-template interaction and effectively fuse global and local features in the target and reference frames K-nearest neighbors filtering and a feature aggregation transformer are introduced. The proposed model achieves state-of-the-art performance on two popular datasets. Additionally we demonstrate the robustness of the proposed model in challenging scenes through various comparative experiments.
High-resolution wide-angle fisheye images are becoming more and more important for robotics applications such as autonomous driving. However using ordinary convolutional neural networks or vision transformers on this data is problematic due to projection and distortion losses introduced when projecting to a rectangular grid on the plane. We introduce the HEAL-SWIN transformer which combines the highly uniform Hierarchical Equal Area iso-Latitude Pixelation (HEALPix) grid used in astrophysics and cosmology with the Hierarchical Shifted-Window (SWIN) transformer to yield an efficient and flexible model capable of training on high-resolution distortion-free spherical data. In HEAL-SWIN the nested structure of the HEALPix grid is used to perform the patching and windowing operations of the SWIN transformer enabling the network to process spherical representations with minimal computational overhead. We demonstrate the superior performance of our model on both synthetic and real automotive datasets as well as a selection of other image datasets for semantic segmentation depth regression and classification tasks. Our code is publicly available.
See Detail Say Clear: Towards Brain CT Report Generation via Pathological Clue-driven Representation Learning
PDF ↗Brain CT report generation is significant to aid physicians in diagnosing cranial diseases.Recent studies concentrate on handling the consistency between visual and textual pathological features to improve the coherence of report.However, there exist some challenges: 1) Redundant visual representing: Massive irrelevant areas in 3D scans distract models from representing salient visual contexts.2) Shifted semantic representing: Limited medical corpus causes difficulties for models to transfer the learned textual representations to generative layers. This study introduces a Pathological Clue-driven Representation Learning (PCRL) model to build cross-modal representations based on pathological clues and naturally adapt them for accurate report generation.Specifically, we construct pathological clues from perspectives of segmented regions, pathological entities, and report themes, to fully grasp visual pathological patterns and learn cross-modal feature representations. To adapt the representations for the text generation task, we bridge the gap between representation learning and report generation by using a unified large language model (LLM) with task-tailored instructions. These crafted instructions enable the LLM to be flexibly fine-tuned across tasks and smoothly transfer the semantic representation for report generation.Experiments demonstrate that our method outperforms previous methods and achieves SoTA performance.Our code is available at https://github.com/Chauncey-Jheng/PCRL-MRG.
Morphologically rich languages are notoriously challenging to process for downstream NLP applications. This paper presents a new pretrained language model, ByT5-Sanskrit, designed for NLP applications involving the morphologically rich language Sanskrit. We evaluate ByT5-Sanskrit on established Sanskrit word segmentation tasks, where it outperforms previous data-driven approaches by a considerable margin and matches the performance of the current best lexicon-based model. It is easier to deploy and more robust to data not covered by external linguistic resources. It also achieves new state-of-the-art results in Vedic Sanskrit dependency parsing and OCR post-correction tasks. Additionally, based on the Digital Corpus of Sanskrit, we introduce a novel multitask dataset for the joint training of Sanskrit word segmentation, lemmatization, and morphosyntactic tagging tasks. We fine-tune ByT5-Sanskrit on this dataset, creating a versatile multitask model for various downstream Sanskrit applications. We have used this model in Sanskrit linguistic annotation projects, in information retrieval setups, and as a preprocessing step in a Sanskrit machine translation pipeline. We also show that our approach yields new best scores for lemmatization and dependency parsing of other morphologically rich languages. We thus demonstrate that byte-level pretrained language models can achieve excellent performance for morphologically rich languages, outperforming tokenizer-based models and presenting an important vector of exploration when constructing NLP pipelines for such languages.
The goal of topic modeling is to find meaningful topics that capture the information present in a collection of documents. The main challenges of topic modeling are finding the optimal number of topics, labeling the topics, segmenting documents by topic, and evaluating topic model performance. Current neural approaches have tackled some of these problems but none have been able to solve all of them. We introduce a novel topic modeling approach, Contextual-Top2Vec, which uses document contextual token embeddings, it creates hierarchical topics, finds topic spans within documents and labels topics with phrases rather than just words. We propose the use of BERTScore to evaluate topic coherence and to evaluate how informative topics are of the underlying documents. Our model outperforms the current state-of-the-art models on a comprehensive set of topic model evaluation metrics.
Many open-ended conversations (e.g., tutoring lessons or business meetings) revolve around pre-defined reference materials, like worksheets or meeting bullets. To provide a framework for studying such conversation structure, we introduce *Problem-Oriented Segmentation & Retrieval (POSR), the task of jointly breaking down conversations into segments and linking each segment to the relevant reference item. As a case study, we apply POSR to education where effectively structuring lessons around problems is critical yet difficult. We present *LessonLink*, the first dataset of real-world tutoring lessons, featuring 3,500 segments, spanning 24,300 minutes of instruction and linked to 116 SAT Math problems. We define and evaluate several joint and independent approaches for POSR, including segmentation (e.g., TextTiling), retrieval (e.g., ColBERT), and large language models (LLMs) methods. Our results highlight that modeling POSR as one joint task is essential: POSR methods outperform independent segmentation and retrieval pipelines by up to +76% on joint metrics and surpass traditional segmentation methods by up to +78% on segmentation metrics. We demonstrate POSR’s practical impact on downstream education applications, deriving new insights on the language and time use in real-world lesson structures.
Position bias in large language models (LLMs) leads to difficulty in accessing information retrieved from the retriever, thus downgrading the effectiveness of Retrieval-Augmented Generation (RAG) approaches in open-question answering. Recent studies reveal that this bias is related to disproportional attention across the context. In this work, we examine how to direct LLMs to allocate more attention towards a selected segment of the context through prompting, aiming to compensate for the shortage of attention. We find that language models do not have relative position awareness of the context but can be directed by promoting instruction with an exact document index. Our analysis contributes to a deeper understanding of position bias in LLMs and provides a pathway to mitigate this bias by instruction, thus benefiting LLMs in locating and utilizing relevant information from retrieved documents in RAG applications. The code and data in our study have been made publicly available.
Large Language Models (LLMs) have demonstrated impressive performance across various tasks. However, current training approaches combine standard cross-entropy loss with extensive data, human feedback, or ad hoc methods to enhance performance. These solutions are often not scalable or feasible due to their associated costs, complexity, or resource requirements. This study investigates the use of established semantic segmentation loss functions in natural language generation to create a versatile, practical, and scalable solution for fine-tuning different architectures. We evaluate their effectiveness in solving Math Word Problems and question answering across different models of varying sizes. For the analyzed tasks, we found that the traditional Cross-Entropy loss represents a sub-optimal choice, while models trained to minimize alternative (task-dependent) losses, such as Focal or Lovász, achieve a mean improvement of +36% on exact match without requiring additional data or human feedback. These findings suggest a promising pathway for more efficient and accessible training processes.
Easy Read text is one of the main forms of access to information for people with reading difficulties. One of the key characteristics of this type of text is the requirement to split sentences into smaller grammatical segments, to facilitate reading. Automated segmentation methods could foster the creation of Easy Read content, but their viability has yet to be addressed. In this work, we study novel methods for the task, leveraging masked and generative language models, along with constituent parsing. We conduct comprehensive automatic and human evaluations in three languages, analysing the strengths and weaknesses of the proposed alternatives, under scarce resource limitations. Our results highlight the viability of automated Easy Read segmentation and remaining deficiencies compared to expert-driven human segmentation.
Segmenting text into fine-grained units of meaning is important to a wide range of NLP applications. The default approach of segmenting text into sentences is often insufficient, especially since sentences are usually complex enough to include multiple units of meaning that merit separate treatment in the downstream task. We focus on the task of abstractive proposition segmentation (APS): transforming text into simple, self-contained, well-formed sentences. Several recent works have demonstrated the utility of proposition segmentation with few-shot prompted LLMs for downstream tasks such as retrieval-augmented grounding and fact verification. However, this approach does not scale to large amounts of text and may not always extract all the facts from the input text.In this paper, we first introduce evaluation metrics for the task to measure several dimensions of quality.We then propose a scalable, yet accurate, proposition segmentation model. We model proposition segmentation as a supervised task by training LLMs on existing annotated datasets and show that training yields significantly improved results. We further show that by using the fine-tuned LLMs (Gemini Pro and Gemini Ultra) as teachers for annotating large amounts of multi-domain synthetic distillation data, we can train smaller student models (Gemma 1 2B and 7B) with results similar to the teacher LLMs. We then demonstrate that our technique leads to effective domain generalization, by annotating data in two domains outside the original training data and evaluating on them. Finally, as a key contribution of the paper, we share an easy-to-use API for NLP practitioners to use.
Advancing Cross-Lingual Entity Alignment with Large Language Models: Tailored Sample Segmentation and Zero-Shot Prompts
PDF ↗In recent years, the advent of large language models (LLMs) like GPT and Llama has significantly influenced numerous domains, particularly in advancing natural language processing (NLP) capabilities. LLMs have shown remarkable performance in NLP tasks such as relation extraction (RE) and knowledge graph completion (KGC), enhancing activities related to knowledge graphs. As a result, there is a growing interest in integrating LLMs into cross-lingual entity alignment (EA) task, which aims to identify equivalent entities across various knowledge graphs, thereby improving the performance of current baselines. However, employing LLMs for entity alignment poses challenges in efficiently handling large-scale data, generating suitable data samples, and adapting prompts for the EA task. To tackle these challenges, we propose Seg-Align, an innovative framework that integrating distance feature extraction, sample **Seg**mentation, and zero-shot prompts. Through extensive experiments on two widely used cross-lingual benchmark datasets, we have not only demonstrated the effectiveness of our proposed sample segmentation algorithm but also highlighted the state-of-the-art performance of Seg-Align. Code is available at https://github.com/yangxiaoxiaoly/Seg-Align.