Search: “semantic-segmentation”

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ENEAS: Embedding-guided Neural Ensemble for Adaptive Segmentation

ENEAS adds text prompting and semantic verification to video segmentation to keep tracking targets through occlusion and reject lookalike distractors.

ENEAS is a unified text-promptable method for instance tracking and open-concept semantic discovery in video, designed to fix temporal hallucinations, spatial fragmentation, and semantic misclassification seen in SAM 3-class foundation models. It extends the geometrically robust SeC architecture with a text-prompting adapter and temporal memory, and uses a verification layer combining fast visual embedding matching with conditional VLM refinement for ambiguous candidates. It targets 3D reconstruction pipelines where a single misclassified distractor corrupts the asset. Code and models are open-sourced.

Hugging Face daily papers·Sep 2, 20:00 UTC · 13d agoAI research

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GoDeep: Annotation-Free Open-Vocabulary 3D Scene Understanding via Language-Space Lifting

GoDeep achieves annotation-free open-vocabulary 3D segmentation by grounding structured image descriptions in language-only embeddings, outperforming CLIP-based lifting on out-of-vocabulary objects.

GoDeep uses a vision-language model purely as a translator, producing structured entity-level image descriptions that are grounded, projected, and aggregated in a general-purpose language-only embedding space, with no 3D training corpus or dedicated 3D encoder required. On ScanNet++ the pipeline is competitive with strong annotation-free baselines, and on a cultural-heritage benchmark a systematic vocabulary correction reverses initial CLIP-based rankings. Language-space embeddings separate genuinely out-of-vocabulary objects more sharply, localize them within scenes, and keep all predictions explainable as discrete text.

arXiv cs.AI / cs.LG / cs.CL·Sep 8, 17:31 UTC · 7d agoAI research

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ConvMem: Convolutional Memory for Long-Context Reasoning

Researchers propose ConvMem, a training-free framework treating LLMs as convolutional kernels for parallelizable long-context reasoning beyond fixed context windows.

ConvMem reformulates long-context reasoning as a hierarchical convolution in which the LLM summarizes text segments hierarchically, shortening the reasoning path from a linear chain to a logarithmic tree. It uses configurable strides, skip connections, and multi-kernel convolution to capture evidence, decompose queries, and enable massive parallelization across segments and reasoning threads. On RULER-HotpotQA and RULER-2WikiMultiHopQA it outperforms training-free baselines and avoids the out-of-distribution overfitting seen in RL-trained approaches like MemAgent.

arXiv cs.AI / cs.LG / cs.CL·Sep 9, 16:53 UTC · 6d agoAI research

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Studying Image Tokenizers as Visual Languages in Unified Multimodal Models

A controlled pure-autoregressive testbed shows task-specific validation losses rank image tokenizers differently, with I2T loss the most consistent signal.

Researchers built a controlled pure-autoregressive testbed and tracked task-specific validation losses during multimodal continual pretraining across text, image, text-to-image (T2I), and image-to-text (I2T) prediction. They find losses should be analyzed per task because they exhibit distinct scaling behavior and rank tokenizers differently, and that the loss-performance relationship depends on the predicted token space. I2T loss, computed over a shared text vocabulary, correlates consistently with both generation and visual understanding performance after supervised finetuning. Case studies revisit the discriminator, semantic supervision, and vocabulary size as tokenizer design axes.

arXiv cs.AI / cs.LG / cs.CL·Sep 8, 17:57 UTC · 7d agoAI research1

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VoT: Vision-of-Thought for Unified Multimodal Representation Alignment

Researchers propose Vision-of-Thought (VoT), a discrete visual-planning token layer between VLMs and diffusion transformers improving text-to-image semantic alignment.

VoT introduces a discrete visual-thinking layer between vision-language models and diffusion transformers, letting the VLM act as a multimodal planner that emits tokens describing objects and layouts before pixel generation. A specialized VoT tokenizer is trained with VLM alignment, feature reconstruction, and vector-quantization losses. Experiments show improved semantic alignment and a structured, interpretable interface for controllable generation.

arXiv cs.AI / cs.LG / cs.CL·Sep 7, 17:53 UTC · 8d agoAI research

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Studying Image Tokenizers as Visual Languages in Unified Multimodal Models

A controlled autoregressive testbed shows validation losses must be analyzed per task, and image tokenizer choice affects joint multimodal text modeling.

Researchers built a pure-autoregressive testbed to study image tokenizers as the 'visual language' of unified multimodal models, tracking task-specific validation losses during multimodal continual pretraining across text, image, text-to-image (T2I), and image-to-text (I2T) prediction. They found that losses exhibit distinct scaling behavior per task and rank tokenizers differently, and that I2T loss over a shared text vocabulary gives a more consistent loss–performance signal than T2I loss. Better reconstruction does not necessarily yield lower task-specific losses or stronger downstream performance, and tokenizer choice can affect text modeling under joint optimization. Case studies examine the discriminator, semantic supervision, and vocabulary size design axes.

Hugging Face daily papers·Sep 7, 20:00 UTC · 8d agoAI research1

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VDiff-Bench: A Challenging Benchmark for Fine-Grained Image Difference Identification

VDiff-Bench, a 1,756-question benchmark, shows multimodal LLMs struggle with fine-grained image-difference identification, scoring as low as 8.7% on low-level changes.

VDiff-Bench is a multiple-choice benchmark of 1,756 four-way questions over image pairs covering 10 change categories including position, motion, color, texture, OCR/text and illumination, with curated hard negatives. Evaluation of 11 state-of-the-art open- and closed-source MLLMs shows fine-grained visual comparison remains brittle: 7-8B-scale open-source models score 52.5-70.6% on semantic changes but only 8.7-33.3% on low-level changes like noise and texture. Notably, Grok 4.3 shows a sharp performance drop on noise and texture differences, falling behind large open-source models like Kimi K2.5 and K3.

Hugging Face daily papers·Sep 4, 20:00 UTC · 11d agoAI research

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Foundation Models for Generalizable Semantic and Goal-Oriented Communication

FMSGOC uses vision-language foundation model priors plus diffusion reconstruction to enable generalizable semantic communication at 0.039 bits per pixel for 6G.

FMSGOC targets generalization failures in semantic and goal-oriented communication for 6G by leveraging broad visual-linguistic foundation model priors. A vision-language model selects sparse, goal-aligned semantic anchors while a fine-tuned diffusion model performs masked completion to reconstruct images at the receiver, decoupling what to send from how to reconstruct. On CIFAR-10 it reaches 0.039 bits per pixel with cosine similarity 0.87-0.90 and 0.83-0.86 on unseen ImageNet inputs, outperforming end-to-end baselines at lower bit rates.

arXiv cs.AI / cs.LG / cs.CL·Sep 7, 18:12 UTC · 8d agoAI research

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MAxBench: A Multinomial Concept Recovery Benchmark

MAxBench evaluates multinomial concept recovery methods, finding affine subspaces steer most reliably but none consistently beats prompting.

MAxBench is a geometry-agnostic evaluation framework for multinomial concept representations in language models, based on sampling from recovered concept representations. It compares 10 localization methods covering 5 geometry types across 6 concepts and 4 models. Findings show affine subspaces steer more reliably than rank-one or linear subspaces due to better non-zero offsets, manifold steering is competitive where applicable, and no method consistently outperforms prompting.

arXiv cs.AI / cs.LG / cs.CL·Sep 11, 17:08 UTC · 4d agoAI research

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The Semantic Bottleneck: Leveraging Semantic Representations for Non-Invasive Speech Decoding

Researchers introduced Brain2Semantics2Text, decoding sentence meaning from non-invasive MEG brain recordings via a semantic bottleneck, improving on prior Brain2Text methods.

The paper proposes Brain2Semantics2Text, a non-invasive speech decoding method that maps sentence-level magnetoencephalography (MEG) responses into a semantic embedding space and inverts those embeddings into natural language. Motivated by evidence that high-level semantic representations are distributed across cortex and evolve on slower timescales, the approach targets meaning rather than phonemes or words, avoiding the need for word-level alignment. The authors report improved sentence-level results compared to prior non-invasive Brain2Text methods despite the low signal-to-noise ratio of neural recordings.

Hugging Face daily papers·Sep 8, 20:00 UTC · 7d agoAI research2

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Reason Through the Latent! Making Latent Visual Reasoning Necessary

Researchers introduce CVRR, forcing multimodal models to rely on recurrent latent computation rather than accessible image tokens, validated via causal interventions and benchmarks.

The paper presents Causal Visual Recurrent Reasoning (CVRR), which makes recurrent hidden-state computation the required image-conditioned path for prediction in vision-language models. Before decoding, visual states and the original multimodal KV cache are removed so only the final recurrent state carries image information to the answer. CVRR retains strong performance on V*, MMVP, BLINK, and MME-RealWorld-Lite while comparable latent reasoners fail under the same constraint. Causal interventions show predictions remain sensitive to recurrent content and that persistent visual evidence causally revises the recurrent trajectory.

Hugging Face daily papers·Sep 5, 20:00 UTC · 10d agoAI research

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OracleZoom: On-Policy Self-Distillation Inspired Reference-Constrained Recursive Image Super Resolution

OracleZoom enables recursive extreme-scale image super-resolution via reference-constrained on-policy distillation, reducing hallucinations at deep zoom scales.

OracleZoom tackles recursive super-resolution, where repeated feeding of predictions back into the same model leaves deeper-scale outputs unsupervised as required source resolution grows geometrically. The framework trains on its own trajectory while carrying the last ground-truth evidence beyond the supervision boundary, combining direct and cross-scale supervision, a no-reference quality objective, a KL-constrained pretrained latent prior, and EMA consistency. Across seven datasets it achieves state-of-the-art SR quality across zoom scales, averaging 0.713 CLIPIQA with larger gains at deeper scales and significantly reduced hallucinations. Code, data, and models are publicly released.

Hugging Face daily papers·Sep 5, 20:00 UTC · 10d agoAI research

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Anatomical Grounding and Leakage-Aware Multimodal Contrastive Learning for Alzheimer's Disease Classification from Structural MRI

Study of label leakage and anatomical grounding in multimodal MRI models for Alzheimer's staging shows cognitive-score fusion accuracy of 87.3% is leakage-driven.

The authors train a ResNet18 slice-based encoder with a one-layer Transformer on 1,075 ADNI-1 T1 MRI scans, using FastSurfer segmentations and YOLOv8 localization (mAP_50 above 0.96) as anatomical reference. Grad-CAM shows the image-only classifier often attends to skull and background rather than disease-relevant structures. A CLIP-style image-tabular contrastive framework organized along a label-leakage spectrum yields 87.3% three-way accuracy with cognitive scores versus 73.0% with regional volumes, and cropping to the medial temporal lobe raises image-only accuracy from 58.7% to 65.1%. Results come from single runs on a small balanced test set with reported confidence intervals.

arXiv cs.AI / cs.LG / cs.CL·Sep 14, 17:13 UTC · 1d agoAI research1

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Enoki: Efficient Multi-Level Hallucination Detection

Researchers introduce Enoki, an open information extraction framework unifying claim-level and span-level hallucination detection in LLMs at lower inference cost.

Enoki extracts text-anchored relational facts, verifies them against evidence, and projects unsupported facts back onto hallucinated spans, so claim-level verification and span-level localization share one representation without separate alignment. It supports LLM-based, encoder-based, and rule-based extraction regimes to balance accuracy against inference cost. Experiments show it stays competitive with strong claim-level systems while using fewer resources and outperforms them on fine-grained span- and entity-level localization. The authors also release EnokiQA, a dual-granularity dataset with aligned claim-level verification and span-level localization annotations.

Hugging Face daily papers·Aug 31, 22:21 UTC · 15d agoAI research

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Cross-Model Agreement as a Deployment-Time Reliability Signal for Automatic Polyp Segmentation

Referee-Based Quality Estimation flags unreliable polyp segmentations at inference without ground truth, reaching ROC-AUC 0.960 with SegFormer-B0 referees.

RBQE measures agreement between a primary segmentation model and an independently trained referee on a 1,223-image external benchmark drawn from four public datasets. A cross-architecture SegFormer-B0 referee achieves the strongest signal (ROC-AUC 0.960), beating a Test-Time Augmentation baseline by 0.055 ROC-AUC under an identical protocol. Excluding trivially separable empty-mask cases, ROC-AUC falls to 0.876 (SegFormer-B0) and 0.783 (same-architecture control), but RBQE's margin over baselines widens. Progressive rejection of low-agreement predictions increases mean Dice of retained outputs, supporting selective prediction at the cost of one extra forward pass.

arXiv cs.AI / cs.LG / cs.CL·Sep 9, 17:32 UTC · 6d agoAI research

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ReMoMask-2: Latent Retrieval-Augmented Masked Motion Generation

ReMoMask-2 rebuilds retrieval in the generator's latent space for text-to-motion generation, achieving lowest FID on KIT-ML and SnapMoGen.

ReMoMask-2 is a retrieval-augmented text-to-motion framework that constructs its retrieval database directly in the generator's pre-quantization latent space and aligns text queries through a distilled lightweight projector, eliminating the representation gap. The framework combines Hierarchical Bidirectional Momentum contrastive learning, Semantic Spatial-Temporal Attention, and Topology Structured Masking to handle hierarchical motion structure. The retriever achieves state-of-the-art accuracy, and ReMoMask-2 attains the lowest FID on KIT-ML and SnapMoGen, with a single mask-transformer stage outperforming the previous two-stage pipeline while delivering the fastest inference.

Hugging Face daily papers·Sep 7, 20:00 UTC · 8d agoAI research

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Technical Manual for a Toolkit for Measuring Contextual Individuation in Transformer Language Models

An open methodology toolkit measures whether transformer language models contextualize fixed word forms across domains using bridge forms and layer-wise silhouette analysis.

The manual documents an open toolkit built around 'bridge forms' - identical written words recurring across two or more subject domains with a different sense in each - to test whether transformer language models individuate word occurrences by context beyond the embedding layer. It covers declarative specification of bridge forms, Wikipedia corpus acquisition, occurrence localization, layer-wise representation extraction, domain-pairwise silhouette measurement, and visualization, justifying each choice against failure modes such as sense contamination and subword-tokenization misalignment. It is a methodological and implementation reference and reports no empirical results.

arXiv cs.AI / cs.LG / cs.CL·Sep 4, 16:38 UTC · 11d agoAI research

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Training and Finetuning Multi-Vector Embedding Models with Sentence Transformers

Hugging Face published a tutorial on training and finetuning multi-vector embedding models using the Sentence Transformers library.

Hugging Face's blog walks through training and finetuning multi-vector embedding models with Sentence Transformers. Multi-vector approaches store multiple vectors per document to support late-interaction retrieval. The post is a practical guide for developers building retrieval pipelines with the library.

Hugging Face Blog·Aug 26, 00:00 UTC · 21d agoAI tools & infra1

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Feature Recovery for Object Understanding After Irreversible Fire Damage

TRACE benchmark with 21.4K scenes studies post-fire object understanding; a Feature Recovery Module improves degraded-image retrieval by 12.5% and material recovery by 20.1%.

The paper introduces TRACE, a transformation-aware benchmark with 21.4K real-image-grounded synthetic scenes, 499 object identities across 189 categories, and five tasks covering degraded-object detection, pristine-state recovery, material recovery, description generation, and functional reasoning. Existing models degrade sharply: RF-DETR mAP falls 71% relative from least to most severe level, and InternVL3.5 retrieval R@1 drops from 93.85 to 28.11. The proposed Feature Recovery Module maps degraded encoder features to pristine-aligned representations while keeping the host model frozen, averaging relative gains of 12.5% for retrieval and 20.1% for material recovery across VLM hosts and severity levels.

Hugging Face daily papers·Sep 9, 20:00 UTC · 6d agoAI research

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BVB: Benchmarking Agentic Video Understanding via Programmatic Reconstruction in Blender

Blender-VideoBench evaluates agentic video understanding by having agents programmatically reconstruct real videos in Blender scenes.

BVB (Blender-VideoBench) tests whether multimodal agents truly understand videos by requiring programmatic reconstruction of real-world videos as animated Blender scenes via a lightweight Mini-BVB harness under identical sandbox and cost constraints. Evaluation uses Dual VQA for spatiotemporal fact preservation and Latent Similarity for perceptual match, combined in a square-root mean overall score. Across 51 configurations from 10 model families, the best model reaches 88.6 Latent Similarity but retains only 53.7% of source-correct spatiotemporal answers, showing semantic retention remains the main challenge.

Hugging Face daily papers·Sep 13, 20:00 UTC · 2d agoAI research

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CoVeR: Coverage-Based Token Pruning for Multi-View 3D Reasoning in VLMs

CoVeR, a training-free coverage-based token pruner, preserves 93.5% of VLM 3D-reasoning performance using only about 8% of visual tokens.

Researchers introduce CoVeR, a deterministic, training-free selector that chooses visual tokens to cover every region of a multi-view 3D scene using only token coordinates. Unlike learned-importance and voxelization pruners, it enforces an exact per-scene token budget, avoids saturation plateaus, and prevents near-duplicate selections. Experiments across four vision-language models show it surpasses prior state of the art by 3.9 percentage points on average across three 3D reasoning benchmarks.

Hugging Face daily papers·Sep 7, 20:00 UTC · 8d agoAI research

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Discrete Beckmann Transport Models for One-Step Language Modeling and Reasoning

DBTM achieves one-step text generation via a time-independent transport map trained directly from data, removing pretrained teacher distillation.

Discrete Beckmann Transport Models (DBTM) build a time-independent flow whose autonomous transport map provably carries any point in ambient space to a fixed point on simplex vertices in a single step. The fixed-point property is characterized by a conservation equation whose residual can be minimized directly from data, eliminating the need for a teacher flow, distillation, and time conditioning. A partial-context interpolant extension turns additional function evaluations into refinement steps rather than ODE integration steps. On language modeling and reasoning tasks, DBTM's one- and few-step generation improves quality and accuracy over discrete diffusion and continuous flow baselines.

arXiv cs.AI / cs.LG / cs.CL·Sep 14, 17:19 UTC · 1d agoAI research1

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Attention Quantization for Tabular Foundation Models

FP8 quantization of attention queries, keys, and values speeds tabular foundation model inference up to 1.7x with no accuracy loss.

The paper develops an FP8 quantization strategy targeting attention calculations (queries, keys, values) in tabular foundation models, arguing attention matters more than weight or KV cache quantization given their differing size and serving patterns versus LLMs. Aligning quantization error between test rows and training rows proves crucial, since misalignment causes drastic accuracy drops. A Triton kernel using explicit FP8 matrix multiplication achieves up to 1.7x speedup over regular 16-bit kernels, with no relevant accuracy loss on TabPFN-v3 and TabICLv2 across TabArena and BeyondArena benchmarks.

arXiv cs.AI / cs.LG / cs.CL·Sep 11, 16:31 UTC · 5d agoAI research1

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Real-World Knowledge-Guided Change Data Synthesis for Remote Sensing

Researchers introduce KnowChange, a framework that uses pretrained vision-language models to synthesize realistic change-detection training data for remote sensing.

KnowChange is a knowledge-guided change data synthesis framework that leverages pretrained vision-language models to reason about plausible change locations and class transitions from pre-change scenes and desired change types. It addresses the limited class-transition coverage and inflexibility of handcrafted rule-based synthesis methods, enabling diverse change types in a unified pipeline. Experiments show KnowChange-generated data outperforms existing synthetic datasets in both synthetic-to-real transfer and synthetic data augmentation, despite compact generation scale.

Hugging Face daily papers·Aug 24, 20:00 UTC · 22d agoAI research

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Label-Guided Knowledge Distillation for 3D-CNNs in Action Recognition

LGKD uses ground-truth labels to guide feature distillation for 3D-CNNs, combining sample-wise and class-wise distillation for action recognition.

The paper proposes Label-Guided Knowledge Distillation (LGKD) for 3D-CNNs, noting that most video feature distillation methods are simple adaptations of image techniques that neglect temporal-dimension differences. LGKD combines sample-wise distillation, which uses label information and the teacher's probability distribution to guide features impacting temporal accuracy, with class-wise distillation employing a prototype network to capture relational knowledge among same-category samples. Experiments on the UCF101 and HMDB51 action recognition benchmarks achieve competitive results.

arXiv cs.AI / cs.LG / cs.CL·Sep 11, 16:21 UTC · 5d agoAI research

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SceneMosaic: Efficient and Diverse Simulation-Ready Scene Generation via Hybrid Agentic Layout Evolution

SceneMosaic combines image-based 3D priors with VLM agent refinement to generate diverse, simulation-ready indoor scenes 24x faster than agentic baselines.

SceneMosaic is a hybrid framework that takes an initial candidate from a learned image-to-3D prior and evolves it with VLM agents for efficiency and physical validity. It decomposes scenes into independent local units, evolves each separately, and composes the global scene via Cartesian product. On SceneEval-100 it matches the strongest agentic baseline in semantic layout quality with a 24x speedup, substantially reduces physical violations, and receives the highest human ratings. Code is publicly available.

Hugging Face daily papers·Sep 3, 20:00 UTC · 12d agoAI research

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Understanding the Impact of Model Pruning on Long-Tail Forgetting and Explanation Reliability in Medical Imaging

Systematic study finds model pruning causes frequency-dependent long-tail forgetting in medical imaging and that gradient-informed methods best preserve explanations.

Across two long-tailed medical imaging datasets, two CNN architectures, four pruning methods, and sparsity up to 95%, the study measures predictive performance, explanation stability, and faithfulness. Rare classes degrade earlier and more severely than frequent ones, while explanation reliability depends mainly on the pruning strategy, with gradient-informed methods degrading least. Mechanistic analysis ties explanation collapse to loss of class-discriminative gradients rather than vanishing feature activations, recommending class- and explanation-aware evaluation of compression.

arXiv cs.AI / cs.LG / cs.CL·Sep 7, 17:44 UTC · 8d agoAI research

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Large Language Models Develop Belief State Geometry In-Context

Probing six open-source LLMs on HMM-generated data shows belief states are linearly decodable (R² 0.83–0.99), suggesting in-context learning approximates Bayesian prediction.

Researchers prompted six open-source LLMs with data from 40 hidden Markov models selected for non-trivial belief structure and probed residual-stream activations for belief states (posteriors over hidden states). Belief states were linearly decodable with peak R² values of 0.83–0.99 across HMM/LLM combinations, spanning early to late layers. Patching and steering the probe-identified subspace preserved downstream prediction quality while control interventions degraded performance substantially, establishing functional relevance. The results provide representation-level evidence that in-context learning approximates optimal Bayesian prediction over a context-inferred generative model.

arXiv cs.AI / cs.LG / cs.CL·Sep 15, 16:17 UTC · 1d agoAI research

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SpatialBlock: Enhancing Spatial Intelligence in LVLMs via Synthetic Block-Stacking Problem

Researchers release SpatialBlock-15k, a synthetic block-stacking dataset that improves 3D spatial reasoning in large vision-language models without dense geometric annotations.

The paper addresses limited spatial intelligence in LVLMs by training on structured block-manipulation tasks instead of costly real-scene annotated datasets. SpatialBlock-15k contains 15,000 synthetic problems covering 3D-to-2D projection, viewpoint transformation, and structural combination, with color modulation as visual cues. LVLMs trained on it via direct answering or reasoning-based prediction outperform baselines and generalize to real-world spatial tasks. Code and data are released on GitHub.

Hugging Face daily papers·Sep 6, 20:00 UTC · 9d agoAI research

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Harnessing CLIP and DINO: An Uncertainty-Aware Cascaded Fusion Network for Generalizable Deepfake Image Detection

UCF-Net fuses CLIP and DINO features with entropy-based uncertainty weighting to improve generalizable deepfake image detection across generators.

Researchers propose UCF-Net, an uncertainty-aware cascaded fusion network that combines CLIP's language-aligned semantic priors with DINO's self-supervised visual-structure priors for deepfake detection. It aggregates hierarchical features across transformer depths via layer-wise expert modules and performs weighted fusion driven by entropy-derived uncertainty. The authors consolidate public deepfake datasets into a unified benchmark of roughly 4 million images plus a cross-generator set of over 8,000 faces from eight recent generators, where UCF-Net achieves the best mean AUC among evaluated methods, though zero-shot transfer remains challenging.

Hugging Face daily papers·Sep 6, 20:00 UTC · 9d agoAI research

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