CGC improves fine-grained multi-image understanding in MLLMs by constructing contrastive training instances from existing single-image annotations and adding a rule-based spatial reward, achieving SOTA on MIG-Bench and VLM2-Bench with transfer gains to other multimodal tasks.
P., and Fung, Y
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EMCompress introduces EMC as an endomorphic sufficient-statistic transformation for VideoQA that preserves answer invariance, releases a dedicated benchmark, and reports a ReSimplifyIt baseline with 0.40 F-1 gains plus efficiency improvements.
A 9B multimodal model learns to tailor raw video/GUI streams into schema-aligned training data, matching a proprietary annotator on downstream tasks; the abstract's capacity-scaling claims are not supported by the body.
TCAP detects backdoor samples in MLLM fine-tuning via tri-component attention profiling, GMM-based head identification, and EM vote aggregation.
citing papers explorer
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CGC: Compositional Grounded Contrast for Fine-Grained Multi-Image Understanding
CGC improves fine-grained multi-image understanding in MLLMs by constructing contrastive training instances from existing single-image annotations and adding a rule-based spatial reward, achieving SOTA on MIG-Bench and VLM2-Bench with transfer gains to other multimodal tasks.
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EMCompress: Video-LLMs with Endomorphic Multimodal Compression
EMCompress introduces EMC as an endomorphic sufficient-statistic transformation for VideoQA that preserves answer invariance, releases a dedicated benchmark, and reports a ReSimplifyIt baseline with 0.40 F-1 gains plus efficiency improvements.
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DataClaw0: Agentic Tailoring Multimodal Data from Raw Streams
A 9B multimodal model learns to tailor raw video/GUI streams into schema-aligned training data, matching a proprietary annotator on downstream tasks; the abstract's capacity-scaling claims are not supported by the body.
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TCAP: Tri-Component Attention Profiling for Unsupervised Backdoor Detection in MLLM Fine-Tuning
TCAP detects backdoor samples in MLLM fine-tuning via tri-component attention profiling, GMM-based head identification, and EM vote aggregation.