arXiv Machine Learning
TeD-Loc: Text Distillation for Weakly Supervised Object Localization
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arXiv AI
Jul 31
TraceCLIP: Recovering Local Semantics from Patch-to-CLS Contributions
arXiv:2607. 26107v1 Announce Type: cross Abstract: Dense vision-language understanding, including object localization, region recognition, and open-vocabulary semantic segmentation, requires associating language concepts with spatially grounded visual regions.
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 27
MLLMCLIP: Feature-Level Distillation of MLLM for Robust Vision-Language Representations
MLLMCLIP introduces a heterogeneous distillation framework that transfers multimodal knowledge from a generative Multimodal Large Language Model (MLLM) teacher directly into a discriminative CLIP student, eliminating the need for synthetic hard negatives. The method uses an attention-based per-layer token selection and a CKA-based distillation loss to bridge architectural differences between the two models. As a result, MLLMCLIP achieves state‑of‑the‑art compositional accuracy and improves zero‑shot classification and image‑text retrieval performance.
arXiv Computer Vision
Sep 17
Not All Patches Are Equally Forgettable: Spatially Localized Domain Unlearning in Vision-Language Models
arXiv:2609.17790v1 Announce Type: new Abstract: Pre-trained vision-language models (VLMs) exhibit strong cross-domain recognition performance even without additional training. However, this robustnes...
Hugging Face Trending Papers
Aug 26
MLLMCLIP: Feature-Level Distillation of MLLM for Robust Vision-Language Representations
Pretrained vision-language models such as CLIP excel at zero-shot recognition but often fail at compositionality, particularly attribute-object and relational structures. Recent studies mitigate this...
arXiv Computer Vision
Sep 1
OPAL: Orthonormal Prototype Alignment Learning for Interpretable Image Classification
arXiv:2608.30003v1 Announce Type: new Abstract: Prototypical part-based models provide explainable predictions by comparing input regions to learned prototypes. However, current approaches are burden...