The paper investigates how CLIP-style dual encoders sometimes increase similarity scores when a text description is extended with a plausible but incorrect detail, a phenomenon termed "half-truths." On the COCO dataset, CLIP correctly prefers the shorter, accurate description only 40.6% of the time, dropping to 32.9% when the added detail is a relation. The authors attribute this to weak supervision on caption components and propose CS-CLIP, a component-supervised fine-tuning approach that raises half-truth accuracy to 69.3% and improves compositional benchmark performance by 5.7 points.
arXiv:2610.33798v1 Announce Type: cross Abstract: An image is rarely described only once. Retrieval benchmarks and many collections attach several captions to each picture, yet dual encoders still ju...
Flow-CLIP introduces a method to improve compositional image-text matching by refining global image-text alignment with image-conditioned changes in token–patch evidence. It combines Global Compatibility Calibration (GCC) for sentence-level similarity, Image-Conditioned Evidence Refinement (ICER) to incorporate visual patch context, and Cross-Image Margin Ranking (CIMR) to enhance ranking margins when images support correct descriptions. Experiments demonstrate that Flow-CLIP outperforms pretrained CLIP and other global alignment approaches on binding accuracy and reduces reliance on language priors.
The paper introduces HN-CLIP, a new objective for dense-caption retrieval that adapts similarity margins per negative example using the text encoder’s own geometry. By adding a detached caption‑similarity matrix to the negative logits, HN‑CLIP addresses the issue of near‑duplicate captions that cause premature loss saturation in InfoNCE training. Experiments on four benchmarks show that HN‑CLIP outperforms leading methods by 2.4–4.3 R@1, trains 2.4× faster than GOAL and 5.4× faster than StructXLIP, and achieves the best full‑data baseline with only 20% of the training data.
The paper introduces Preserve-and-Compose Training (PACT) for composed image retrieval, a task where a query image is modified by a textual instruction while preserving visual content from a reference image. PACT learns from image–text–text triplets, using target captions for supervision and visual evidence from the source image to maintain relevant details, without requiring target images or gallery updates. The authors also propose Chord scoring, which blends target similarity with source-relative directional agreement in a frozen image space, and demonstrate that this combined approach yields strong retrieval performance across multiple zero-shot CIR benchmarks and various backbones.
arXiv:2610.32407v1 Announce Type: new Abstract: Text-to-image (T2I) diffusion models have advanced notably, but still fail to satisfy prompt conditions, resulting in attribute mismatches and missing...
arXiv:2607. 23052v1 Announce Type: cross Abstract: Dual-encoder vision-language models (VLMs) expose a similarity interface that enables zero-shot retrieval but fails compositional constraints: queries like "umbrella and no person" retrieve images containing both, even when concept detection is reliable.
arXiv:2607. 03143v1 Announce Type: cross Abstract: Vision-language alignment powers open-vocabulary recognition, retrieval, and LVLM grounding, yet natural captions are often underspecified, making similarity brittle and overly confident under paraphrase and omitted details.
arXiv:2606. 07451v1 Announce Type: cross Abstract: Vision-language models such as CLIP are highly useful for diverse tasks due to their shared image-text embedding space.
Intelligent systems that act in the world require image understanding that is both comprehensive and spatially grounded. Current vision-language models (VLMs) can generate fluent and detailed image ca...
DEPICT is a new training‑free metric for evaluating text‑to‑image alignment. It replaces fixed reference answers with an agreement rule that compares image‑based and caption‑only responses, weighting questions by how decisively the caption determines them. By merging this agreement score with a holistic score, DEPICT improves negation accuracy dramatically and outperforms existing training‑free metrics while matching or exceeding fine‑tuned evaluators on several benchmarks.
CS-CLIP is a vision‑language model that improves compositional reasoning by using scene graphs to identify compositional elements and create structured negative examples through selective masking. The approach retains only the most contradictory negatives, encouraging the model to depend on compositional structure instead of surface cues. CS-CLIP achieves state‑of‑the‑art performance on compositional reasoning benchmarks while maintaining strong cross‑modal retrieval and downstream visual reasoning capabilities with fewer training samples.
Zero-shot composed image retrieval (ZS-CIR) aims to retrieve a target image from a multimodal query consisting of a reference image and an edit text describing the desired modification. Recent ZS-CIR studies have relied on projection-based methods that map a reference image into pseudo-word tokens in the text embedding space.