Language-Based Segmentation

• Language-based segmentation is a computational approach that leverages language cues to guide the partitioning of data across modalities such as text, images, video, and 3D point clouds.
• Techniques span supervised neural frameworks, zero-shot transfer, and unsupervised methods, demonstrated in applications from visual reasoning to medical and sign language segmentation.
• The approach integrates metrics like Dice loss and cross-entropy, enabling precise, intention-aligned outputs and enhancing generalization and interactive refinement.

Language-based segmentation refers to a family of computational approaches in which the segmentation of data—across modalities such as text, images, video, speech, or 3D point clouds—is guided, conditioned, or evaluated through language signals. This encompasses both classic tasks (e.g., word segmentation in unsegmented languages) and contemporary multimodal applications (e.g., segmenting image regions that satisfy a natural language description). Techniques vary from supervised neural architectures leveraging deep LLMs to zero-shot transfer and unsupervised methods that use language as the only or primary interface. Language-based segmentation thus generalizes traditional segmentation, enabling complex, intention-aligned, and context-aware predictions across domains.

1. Language-Based Segmentation in Vision: Reasoning and Semantic Segmentation

Reasoning segmentation exemplifies the state of the art in language-based segmentation for images, in which a model must produce a binary mask that localizes pixels according to an implicit, often compositionally complex natural language query. In the LISA framework (Lai et al., 2023), the input is an image $I \in \mathbb{R}^{H \times W \times 3}$ and a natural language query $Q$, and the output is a binary mask $\hat{M} \in \{0,1\}^{H \times W}$ highlighting pixels that satisfy $Q$. Queries may require attribute identification, functional reasoning, or commonsense/world knowledge—e.g., “the food with high Vitamin C” or “where can we throw away the rest of the food and scraps?”.

LISA employs a multimodal LLM backbone (e.g., LLaVA-7B/13B), frozen vision encoder (e.g., SAM ViT-H), and a segmentation decoder, with a task-specific extension: the <SEG> token. The embedding-as-mask paradigm projects the hidden state for <SEG> into a mask query that, together with visual features, allows a mask decoder (such as a lightweight mask-transformer) to output the segmentation. The training objective combines a text cross-entropy term and a mask loss, itself blending binary cross-entropy and Dice loss, with typical weights $\lambda_{txt} = 1.0$, $\lambda_{mask} = 1.0$, $\lambda_{bce} = 2.0$, $\lambda_{dice} = 0.5$.

The ReasonSeg benchmark (1,218 image-instruction-mask triplets) captures this complexity, utilizing both OpenImages V4 and ScanNetv2, and annotating pixel-level masks corresponding to implicit textual queries. LISA demonstrates strong zero-shot and fine-tuning performance: e.g., LISA-13B zero-shot achieves $gIoU \approx 44.8$, $cIoU \approx 45.8$; fine-tuning on 239 reasoning samples lifts these to $gIoU \approx 51.7$, $cIoU \approx 51.1$. Using LLaVA-v1.5 as the base further increases $gIoU$ to $\sim$61.3. Qualitatively, LISA is robust to complex world-knowledge (e.g., segmenting strawberries and bell peppers but not tomatoes for vitamin C queries), functional queries, and multi-object queries (via multiple <SEG> tokens), while also supporting explanatory rationales in text.

The embedding-as-mask paradigm circumvents limitations of polygon sequencing or discrete prompt engineering, enabling more expressive alignment between language and dense mask outputs. Possible extensions include panoptic/instance segmentation (multi-token expansion), adaptation to other dense prediction tasks (e.g., depth, flow), and explicit tool-use signaling (e.g., “DETECT”, “SEGMENT”, “TRACK”).

2. Unsupervised and Neural LLM Segmentation for Text

In classical settings, language-based segmentation refers to splitting raw character sequences into word boundaries, crucial for languages lacking explicit delimiters (e.g., Chinese, Thai). Early systems utilize n-gram or neural LLMs with beam search decoders (Doval et al., 2018). At each position, a segmentation hypothesis is scored as the average log-probability under a character n-gram (modified Kneser–Ney, $n=10-12$) or LSTM-based recurrent LLM. The best systems achieve precision up to $\sim$0.92 (English, German, Spanish) and remain robust in microtext (e.g., tweets) and morphologically diverse languages.

Joint sequence-tagging frameworks (e.g., BiRNN-CRF over B, I, E, S tags and transducers for multiword tokens) further generalize to dozens of typologically diverse languages (Shao et al., 2018). Results show macro-averaged F1 = 98.90% (over 81 Universal Dependencies corpora), including substantial gains ($+3-6$ F1) on morphologically complex or non-segmenting languages (Chinese, Japanese, Arabic, Hebrew, Vietnamese).

Recent innovations exploit LLM-based comprehension: for word segmentation in “comprehend first, segment later” pipelines, prompting LLMs (e.g., “insert a space between each word”) directly leverages deep semantic knowledge (Zhang et al., 26 May 2025). LLACA combines LLM-based candidate extraction with an Aho–Corasick automaton, dynamic $n$-gram model, and Viterbi decoding, yielding up to 88–89% F-measure on Chinese and improved cross-domain robustness.

3. Language-Based Segmentation Beyond Text: Medical Imaging, 3D, and Sign Language

The language-based paradigm generalizes to medical image segmentation, 3D point clouds, and even sign language. Approaches such as FLanS (Da et al., 2024) harness retrieval-augmented generation (RAG) for prompt synthesis, CLIP-based text encoders, intention heads, and symmetry-aware canonicalization to achieve robust free-form query-based segmentation across 100k+ axial CT slices. FLanS attains Dice scores of $0.908$ (FLARE), $0.837$ (WORD), and $0.852$ (RAOS); on anatomy-agnostic prompts, it matches or exceeds bbox-prompted baselines.

In interactive medical segmentation, LIMIS (Heinemann et al., 2024) allows users to adapt segmentation masks via language, combining text-to-bbox (Grounding DINO) and box-to-mask (ScribblePrompt/SAM), supporting iterative text-driven refinement. This yields radiologist-grade masks (initial Dice $\sim$0.66; post-interaction +$4\rightarrow$0.70 average Dice) and evidences the feasibility of hands-free, language-guided adaptation without explicit boundary annotations.

3D language-based segmentation frameworks (e.g., SeCondPoint (Liu et al., 2021)) introduce semantics-conditioned WGANs to model distributions of point features conditioned on class language embeddings (e.g., word2vec). This enables effective zero-shot segmentation on unseen classes (HACC up to $\sim$60\% on S3DIS) and universally improves conventional segmentation accuracies across several backbones.

Sign language segmentation is approached via per-frame BIO taggers informed by linguistic cues (e.g., hand-shape, 3D pose, optical flow), enabling both sign-level and phrase-level segmentation (Moryossef et al., 2023). Here, language-informed explicit modeling outperforms IO tagging and optical flow features aid in phrase boundary detection, confirming the relevance of linguistic strategies even outside spoken/written modalities.

4. Training Objectives, Datasets, and Evaluation Metrics

Across modalities, language-based segmentation frameworks share the need for datasets pairing raw input (image, sequence, point cloud, etc.), language query or instruction, and ground-truth segmentations. In vision, benchmarks such as ReasonSeg (Lai et al., 2023), referring datasets (refCOCO/g), and semantic segmentation corpora (ADE20K, COCO-Stuff) supply paired examples spanning explicit and implicit queries. Biomedical applications leverage large CT datasets and diversified prompt corpora (clinical EMRs, synthetic, and user queries).

Loss functions typically employ combinations of cross-entropy (text, mask), Dice, binary cross-entropy, and alignment objectives. In LISA and VideoLISA (Bai et al., 2024), mask losses are $\lambda_{bce} \cdot$BCE $+~ \lambda_{dice} \cdot$ Dice; auxiliary pretraining incorporates segmentation QA and visual question answering.

The evaluation employs task-specific IoU metrics: global/cumulative IoU (gIoU/cIoU) for reasoning tasks, mean IoU for semantic segmentation, Dice/NSD for medical images, and frame-wise macro-F1, ROC-AUC, or IoU for sign/phrase segmentation. Unsupervised text methods rely on exact-match precision/recall, bits per character, and boundary F1, while 3D and speech domains use harmonic mean accuracy (HACC), segmentation-F$_{0.5}$, and downstream pipeline impacts (e.g., translation BLEU for speech).

Key results illustrate the competitiveness or superiority of language-based approaches: LISA-13B (fine-tuned) gIoU = 51.7; LaSagnA (Wei et al., 2024) mIoU = 42.0 (ADE20K), 63.2 (Cityscapes); FLanS outperforms specialist bbox/point-prompted architectures on both in-domain and rotated out-of-domain scans.

5. Generalization, Reasoning, and Multimodal Innovations

A central motivation and outcome of language-based segmentation is strong generalization to unseen, compositionally described, or functionally specified targets. LISA achieves robust zero-shot reasoning segmentation without explicit reasoning data during pretraining; fine-tuning on minimal reasoning data yields sizable gains. Open-set and panoptic extensions depend only on extending token vocabulary or mask outputs.

Video-based systems (e.g., VideoLISA (Bai et al., 2024)) further abstract the paradigm, introducing temporally coherent, reasoning-aligned segmentation by extending the single-token (<TRK>) approach for cross-frame consistency. Sparse-Dense Sampling balances temporal coverage and spatial resolution, facilitating multi-frame reasoning with practical computational requirements.

Language-based segmentation has also been deployed interactively, e.g., through natural language refinement loops (LIMIS), highlighting future directions in multi-turn, correction-tolerant dialog for segmentation tasks and the integration of richer, ontology-aware instruction following.

6. Limitations, Challenges, and Future Directions

Salient limitations of current language-based segmentation models include the dependency on frozen or imperfect vision encoders (leading to failures in occlusion or color/lighting shifts), difficulties with abstract or overly literary instructions, and challenges in precise multi-object and crowded-scene segmentation. Some systems rely on modular rather than end-to-end integration of language and vision (e.g., the current LIMIS pipeline), and mask quality can degrade outside the scope of training queries.

Promising research directions include jointly training unified vision-language backbones, scaling up complex reasoning datasets, elevating the sophistication of language grounding (via more expressive text encoders or prompt-understanding modules), and expanding the interactive refinability of segmentation outputs. There is increasing evidence that LLM-powered segmentation is competitive with specialist fixed-label methods on closed and open sets while unlocking new classes of functionality—reasoning about intent, function, attribute, or commonsense—entirely via language (Lai et al., 2023, Li et al., 2022, Wei et al., 2024).

In unsupervised and zero-shot settings, methods such as LLACA (Zhang et al., 26 May 2025) exploit deep semantic priors from LLMs combined with automata and sequence models, attaining segmentation quality near the inherent “incompatibility” ceiling defined by gold-standard corpus divergence. This suggests that further advances may rely not only on better models but on revisiting task definitions and gold standards to accommodate comprehension-aligned, intention-robust segmentation aligned with human linguistic reasoning.