Preserving Localized Patch Semantics in VLMs

Logit Lens has been proposed for visualizing tokens that contribute most to LLM answers. Recently, Logit Lens was also shown to be applicable in autoregressive Vision-Language Models (VLMs), where it illustrates the conceptual content of image tokens in the form of heatmaps, e.g., which image tokens are likely to depict the concept of cat in a given image. However, the visual content of image tokens often gets diffused to language tokens, and consequently, the locality of visual information gets mostly destroyed, which renders Logit Lens visualization unusable for explainability. To address this issue, we introduce a complementary loss to next-token prediction (NTP) to prevent the visual tokens from losing the visual representation inherited from corresponding image patches. The proposed Logit Lens Loss (LLL) is designed to make visual token embeddings more semantically aligned with the textual concepts that describe their image regions (e.g., patches containing a cat with the word "cat"), without requiring any architectural modification or large-scale training. This way, LLL constrains the mixing of image and text tokens in the self-attention layers in order to prevent image tokens from losing their localized visual information. As our experiments show, LLL not only makes Logit Lens practically relevant by producing meaningful object confidence maps in images, but also improves performance on vision-centric tasks like segmentation without attaching any special heads.

Direct Visual Grounding by Directing Attention of Visual Tokens

Vision Language Models (VLMs) mix visual tokens and text tokens. A puzzling issue is the fact that visual tokens most related to the query receive little to no attention in the final layers of the LLM module of VLMs from the answer tokens, where all tokens are treated equally, in particular, visual and language tokens in the LLM attention layers. This fact may result in wrong answers to visual questions, as our experimental results confirm. It appears that the standard next-token prediction (NTP) loss provides an insufficient signal for directing attention to visual tokens. We hypothesize that a more direct supervision of the attention of visual tokens to corresponding language tokens in the LLM module of VLMs will lead to improved performance on visual tasks. To demonstrate that this is indeed the case, we propose a novel loss function that directly supervises the attention of visual tokens. It directly grounds the answer language tokens in images by directing their attention to the relevant visual tokens. This is achieved by aligning the attention distribution of visual tokens to ground truth attention maps with KL divergence. The ground truth attention maps are obtained from task geometry in synthetic cases or from standard grounding annotations (e.g., bounding boxes or point annotations) in real images, and are used inside the LLM for attention supervision without requiring new labels. The obtained KL attention loss (KLAL) when combined with NTP encourages VLMs to attend to relevant visual tokens while generating answer tokens. This results in notable improvements across geometric tasks, pointing, and referring expression comprehension on both synthetic and real-world data, as demonstrated by our experiments. We also introduce a new dataset to evaluate the line tracing abilities of VLMs. Surprisingly, even commercial VLMs do not perform well on this task.