arXiv Machine Learning
Jun 2
Density-Aware Translation of Spurious Correlations in Zero-Shot VLMs
arXiv:2606. 01710v1 Announce Type: cross Abstract: Vision-Language models (VLMs), such as CLIP, achieve powerful zero-shot classification.
RECOUNT: Reference-guided Counting with Synthetic Visual Exemplars
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arXiv AI
Jul 2
Learning to Compose: Revisiting Proxy Task Design for Zero-Shot Composed Image Retrieval
arXiv:2607. 00374v1 Announce Type: cross Abstract: Composed Image Retrieval (CIR) retrieves a target image from a reference image and a textual modification.
arXiv Computer Vision
1d ago
Retrieval Heads Meet Vision: Uncovering How VLMs Locate and Extract Visual Information
The paper introduces Visual Retrieval Heads (VRHs), a small fraction of attention heads in vision‑language models that are causally responsible for grounding text descriptions to image regions. By recasting head‑scoring methods and evaluating across eleven VLMs and five benchmarks, the authors show that masking the top 20 VRHs can drop grounding accuracy by up to 80 percentage points, while random masking has little effect. VRHs generalize across various visual reference tasks, preserve output format while corrupting localization, and transfer causally across models sharing an LLM backbone.
arXiv Computer Vision
3d ago
Object Counting Across Modalities: Taxonomies, Benchmarks, Applications, and Open Challenges
The paper reviews the evolution of object‑counting techniques from class‑specific density regression to open‑vocabulary, foundation‑model‑backed counters that can handle visual and textual prompts. It highlights that current evaluation relies on a few saturated benchmarks, leading to models exploiting statistical regularities rather than true generalization. The authors propose a five‑axis taxonomy and audit the literature across domains such as microscopy, remote sensing, crowd counting, and agriculture, identifying six structural contradictions and outlining a roadmap for robust, multimodal evaluation protocols.
arXiv AI
Jul 7
Token-Based Affordance Grounding with Large Vision-Language Models
arXiv:2607. 03595v1 Announce Type: cross Abstract: Affordance grounding aims to localize image regions that support a specific action, serving as a core capability for physical intelligence and embodied perception.
arXiv Computer Vision
Aug 21
ID-VTG: Image-Disambiguated Video Temporal Grounding
arXiv:2608. 20127v1 Announce Type: new Abstract: Video Temporal Grounding (VTG) faces significant challenges when natural language queries must distinguish between multiple events involving visually similar entities, particularly when relying on fine-grained visual attributes that are difficult to describe accurately in words alone.
arXiv AI
Jul 8
OpenGround: Planning-based Online Perception for Open-World 3D Visual Grounding
arXiv:2512. 23020v3 Announce Type: replace-cross Abstract: 3D visual grounding aims to locate objects based on natural language descriptions in 3D scenes.
arXiv AI
Jun 8
Never Seen Before: Benchmarking Genuine Zero-Shot Composed Image Retrieval with Consistent Video-Sourced Datasets
arXiv:2606. 07032v1 Announce Type: cross Abstract: Zero-Shot Composed Image Retrieval (ZS-CIR) aims to retrieve a target image based on a query composed of a reference image and a relative caption without training samples.
arXiv Machine Learning
Jul 2
AdaBoosting Text Prompts for Vision-Language Models
arXiv:2607. 00684v1 Announce Type: new Abstract: The classification accuracy of pretrained Vision-Language Models (VLMs) relies on the quality of the text prompts.
arXiv AI
Aug 7
HoloCount: A Holistic Visual Counting Benchmark for MLLMs
arXiv:2607. 06420v1 Announce Type: cross Abstract: Visual counting is a fundamental pillar of multimodal intelligence, requiring a seamless integration of fine-grained grounding and spatial reasoning.
Hugging Face Trending Papers
1d ago
Retrieval Heads Meet Vision: Uncovering How VLMs Locate and Extract Visual Information
Vision-language models (VLMs) can locate an image region referred to by a text prompt and route the corresponding visual evidence to the output, yet the internal mechanism behind this behavior is not...
arXiv AI
Jul 28
Disentangling Semantic Attention from Structural Bias in the Attention Manifold
arXiv:2607. 24017v1 Announce Type: cross Abstract: The empirical success of attention mechanism in Multimodal Large Language Models (MLLMs) often obscures its inherent, subtle flaws.