🔍 Information Retrieval & RAG

🎞️ ECCV2026 · 8 paper notes

📌 Same area in other venues: 🔬 ICLR2026 (81) · 💬 ACL2026 (73) · 🧪 ICML2026 (26) · 🤖 AAAI2026 (21) · 🧠 NeurIPS2025 (25) · 📹 ICCV2025 (5)

TSEmbed: Unlocking Task Scaling in Universal Multimodal Embeddings

TSEmbed distributes conflicting multimodal objectives across input-dependent MoE-LoRA experts, then weights hard negatives by routing similarity after expert warm-up, reaching overall scores of 70.5/74.7 with MMEB-only 2B/7B models and surpassing the corresponding B3 baselines by 2.4/2.7 percentage points.

LightSTAR: Efficient Visual Document Retrieval via Lightweight Selection with Vision-Adaptive Refinement

To address the issue where MLLMs are too slow to encode all pages in visual document retrieval, LightSTAR proposes first filtering out candidates with high recall using an LLM-free lightweight visual selection module, and then performing vision-adaptive semantic refinement only on those candidates. This achieves 89.1 NDCG@5 on ViDoRe while reducing the end-to-end latency for 5,000 pages to 123.9s.

FlowCIR: Semantic Transport via Flow Matching for Zero-Shot Composed Image Retrieval

FlowCIR reformulates zero-shot composed image retrieval as conditional semantic transport within vision-language embedding space, leveraging conditional flow matching and top-K hard-negative retrieval supervision for fast one-step transport, supplemented by an inference-time multi-negative steering strategy to mitigate negation collapse while requiring only 0.5 hours of single-GPU training.

HyFL-CLIP: Hyperbolic Fine-Tuning of CLIP for Robust Long-Context Understanding

HyFL-CLIP distills the Euclidean image-text alignment of pretrained CLIP into hyperbolic space (Lorentz model). By modeling the "global description - local component" hierarchical entailment relationship via Einstein midpoint aggregation, the model stably retrieves correct images even when long texts are perturbed (reordered, deleted, word-dropped), achieving up to a 19.5% improvement over the strongest baseline under word perturbations.

ME-IQA: Memory-Enhanced Image Quality Assessment via Re-Ranking

To resolve the severe discrete collapse issue in reasoning-induced vision-language models for image quality assessment, ME-IQA introduces a training-free, test-time memory-enhanced re-ranking framework that retrieves aligned neighbors from a hybrid memory bank and fuses pairwise VLM preference probabilities with the initial score under Thurstone's Case V model.

What Images Cannot Say: Language-Guided Olfactory Representation Learning

Addressing the fundamental challenge that camera images cannot observe out-of-frame diffusing odor sources and ambient contexts, SCENT leverages Vision-Language Models (VLMs) to generate structured language descriptors across objects, context, and inferred smells as a semantic bridge, achieving state-of-the-art cross-modal retrieval and interpretable odor mixture disentanglement.

MMAgent-R2: Learning to Rerank and Reject for Agentic mRAG

MMAgent-R2 unifies visual reranking and active rejection into an internal verification mechanism for agentic mRAG, optimized via GRPO with step-level verification rewards to break free from the constraints of static candidate pools and noisy global visual retrieval.

Multi-Anchor Distillation with Text-Guided Analytic Classifier for Continual Learning

To tackle both cumulative feature drift in the visual encoder and gradient-based decision boundary overwriting in the classifier for exemplar-free continual learning, this paper proposes an integrated framework combining multi-anchor historical distillation with a closed-form text-guided analytic classifier, establishing new state-of-the-art benchmarks especially on long task sequences.