salesforce-research-64f0857f·2 events·first seen Aliases: Salesforce Research, Salesforce AI Research
Researchers from Salesforce AI Research propose Evidence-Backed Video Question Answering (E-VQA), a task requiring Video LLMs to jointly produce semantic answers and precise spatio-temporal evidence via dense tracked object segmentation masklets. They introduce ST-Evidence, the first human-verified pixel-level grounding benchmark for video QA, and ST-Evidence-Instruct, a 160k-scale training dataset. Fine-tuning on this data yields substantial gains over size-matched baselines (+27.2 t-mean, +13.8 J&F on a 7B model), revealing a critical gap between QA accuracy and true visual perception that scaling alone does not close.
Hugging Face published a blog post introducing BLIP-2, a multimodal model that enables zero-shot image-to-text generation by bridging frozen image encoders and large language models via a lightweight Querying Transformer (Q-Former). The post covers the model's architecture, capabilities, and how to use it via the Hugging Face Transformers library. BLIP-2 achieves strong performance on visual question answering and image captioning tasks without task-specific fine-tuning.