arXiv AI
TDDN: Text-aligned Diffused DINO Network for Puzzle Understanding
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arXiv AI
Oct 1
VisualNeedle: Benchmarking Active Visual Search in Information-Dense Scenes
arXiv:2605.26380v2 Announce Type: replace-cross Abstract: Frontier multimodal large language models (MLLMs) have been reported to achieve over 90\% accuracy on fine-grained perception benchmarks. How...
arXiv AI
Oct 9
Rethinking Contrastive Loss in CLIP Post-training: A Complementary Framework with Frozen Text Encoder
The paper revisits the use of contrastive loss for post‑training CLIP models, showing that the reported catastrophic forgetting is mainly due to an overly large temperature parameter in the InfoNCE objective. By setting a smaller temperature, contrastive post‑training can actually improve CLIP. The authors introduce ComCLIP, a lightweight single‑epoch recipe that freezes the text encoder, trains the vision encoder with a properly tempered contrastive loss, an MSE anchoring loss, and a relational distillation loss, achieving comparable zero‑shot classification to CLIP‑Refine while significantly enhancing visual feature transferability and maintaining downstream compatibility with LLaVA.
Hugging Face Trending Papers
Jul 27
MAViE: A Multi-scale Adaptive Vision Encoder for Fine-grained Visual Perception and Efficient Multimodal Reasoning
Vision-language models commonly project all tokens produced by a pretrained vision encoder into a large language model. However, final-layer features can discard text, local attributes, and spatial relationships, while high-resolution inputs substantially increase context length and inference latency.
arXiv AI
3d ago
V-LUMEN: Visual Lookup Memory for Embedding Scaling in Vision-Language Models
V-LUMEN introduces a visual lookup memory for vision‑language models by discretizing visual features with a frozen codebook, creating spatially aware memory keys via hashing, and modulating them with text‑conditioned relevance signals. The retrieved embeddings are fused into a frozen Qwen3‑VL‑2B backbone through a gated residual pathway, yielding substantial improvements on five reasoning‑intensive benchmarks and zero‑shot transfer to three disjoint image datasets. At a matched parameter budget, reallocating capacity from LoRA or MoE adapters to V‑LUMEN preserves accuracy on two backbones, demonstrating that external visual memory offers an independent capacity axis. whyItMatters":"The paper shows that adding a non‑parametric visual lookup memory can significantly boost vision‑language model performance without increasing per‑token compute, offering a new way to scale such models."
arXiv AI
Jul 28
What CLIP Knows but Cannot Say: Recovering Negation from Frozen Intermediate Features
arXiv:2607. 23271v1 Announce Type: cross Abstract: Contrastive vision-language models such as CLIP map semantically opposite phrases (e.
arXiv AI
Oct 7
VisionWeave: Weaving Elastic Visual Representations as a Native Capability of MLLMs
VisionWeave introduces elastic visual representation weaving, a native capability for multimodal large language models that learns where and at what granularity to encode visual information. The method combines a gated spatial pooler for coarse representations with a granularity router that allocates content‑adaptive token usage, trained end‑to‑end on large‑scale data. Experiments on Qwen3.5‑4B and Qwen3.8‑27B show that VisionWeave can save 43.0% of tokens while preserving 98.9% of performance across eight benchmarks, and delivers significant throughput gains and latency reductions when deployed on the SGLang serving engine.